The sea surface temperature inter-hemispheric dipole (SSTID) is an important variability mode of global SST anomalies, characterized by an anti-phase variation of SST between the two hemispheres. In this study, the decadal variability of the Northern Hemisphere summer monsoon (NHSM) is found to be strongly regulated by the SSTID, with positive (negative) phases of the SSTID corresponding to the strengthening (weakening) of NHSM. Both observation and SST-forced atmospheric model simulations suggest that the SSTID related thermal forcing modulates the NHSM by causing planetary-scale atmospheric circulation adjustments. Positive SSTID events lead to coherent increase (decrease) of surface air temperature over the entire Northern (Southern) Hemisphere, increasing the inter-hemispheric thermal contrast (ITC). As sea level pressure changes are just opposite to air temperature, the increase of ITC enhances the inter-hemispheric pressure gradient (Southern Hemisphere minus Northern Hemisphere), leading to the strengthening of summer monsoonal circulation and the increase of monsoon rainfall in the Northern Hemisphere.

A unique feature of the East Asian summer monsoon is dominated eastward-propagating synoptic-scale disturbances along a subtropical Meiyu front. The synoptic disturbances are strongly coupled with moisture and precipitation, and cannot be explained by traditional dry baroclinic instability theory. In this study, a new theoretical framework is developed, and it extends a traditional 2-level baroclinic model by considering a moisture tendency equation and feedbacks among perturbation moisture, precipitation and circulation. An eigenvalue analysis shows that the most unstable mode has a preferred zonal wavelength of 3400 km, an eastward phase speed of 10 m/s and a westward tilted vertical structure, all of which are in good agreement with observations. Both horizontal and vertical advection processes contribute to low-level moistening. Sensitivity tests show that the instability and the scale selection arise primarily from the moistening and condensational heating processes, while the modest background vertical shear provides an additional energy source for the perturbation growth. The new moist baroclinic instability theory explains well the observed characteristics of the development of synoptic-scale disturbances along the subtropical Meiyu front.

The dynamical response of Southeast Asian summer monsoon (SEASM) to ENSO shows a large spread among CMIP6 models, and only some models can capture the observed features of ENSO’s impact on the monsoon. The problem of the bad-performance models is that the ENSO-related warm SST anomalies extend too far westward in the western equatorial Pacific (WEP) and are overly persistent, caused by a weak negative shortwave radiation feedback due to a low sensitivity of precipitation to local SST changes related to the cold bias in climatological SST. On the other hand, from the mature winter to the decaying summer of El Niño, the El Niño-related anomalous eastward current does not reverse to westward current because of the weak El Niño discharge process, attributed to the weak westerly wind anomalies associated with El Niño. The prolonged anomalous eastward current thus also contributes to the slow decay of WEP SST anomalies via inducing excessively persistent warm zonal advection in the WEP. For a warmer climate, CMIP6 models projects an enhanced response of the SEASM to ENSO, attributed to weakened warm SST anomalies in WEP during El Niño decaying summer. The El Niño related WEP SST anomalies decay rapidly and eventually dissipate from El Niño mature winter to the following summer, intensifying the anomalous anticyclonic circulation over the western North Pacific by enhancing the zonal gradient of SST anomalies between the tropical Indian Ocean and the WEP. The fast decay of WEP SST anomalies is mainly determined by increased latent heat flux from oceans to the atmosphere due to strengthened surface wind, caused by the eastward shifts of ENSO-induced anomalous Walker circulation and associated precipitation anomalies that are resulted from the El Niño-like change in background SST in the equatorial Pacific under global warming.

The El Niño-Southern Oscillation (ENSO) used to affect the Asian summer monsoon (ASM) and Australian summer monsoon (AusSM) precipitation in different ways but global warming may have changed it. This study built robust annual ASM (AusSM) precipitation reconstructions during 1588–2013 (1588–1999) to examine the ENSO-monsoon relationship and how it has changed. During the period of 1588–1850 when natural climate variability was dominant, the ENSO-monsoon and inter-monsoon relationship was weak and non-stationary. Since 1850, however, both the inter-monsoon and ENSO-monsoon relationships saw an enhancement and this trend has been persistent to the present day, suggesting the influence of anthropogenic climate warming. Further analysis of climate model projections found that global warming can strengthen the ENSO-monsoon association that, subsequently, acts to synchronize the ASM and AusSM variations.

The lower tropospheric subtropical circulation (SC) is characterized by monsoons and subtropical highs, playing an important role in global teleconnections and climate variability. The SC changes in a warmer climate are influenced by complex and region-specific mechanisms, resulting in uneven projections worldwide. Here, we present a method to quantify the overall intensity change in global SC, revealing a robust weakening across CMIP6 models. The weakening is primarily caused by global-mean surface warming, and partly counteracted by the direct CO₂ effect. The direct CO₂ effect is apparent in the transient response but is eventually dominated by the surface warming effect in a slow response. The distinct response timescales to global-mean warming and direct CO₂ radiative forcing can well explain the time-varying SC changes in other CO₂ emission scenarios. The declined SC implies a contracted monsoon range and drying at its boundary with arid regions under CO₂-induced global warming.

As IPCC ARs stated, global warming is estimated based on the global average temperature of pre-industrialization (represented by the average throughout 1850 to 1900 with relatively sparse observations). Given the impossibility of massive increasing observation data in the early stages, accurately constraining this baseline has become an unresolved issue. Based on a data-driven research paradigm, we developed a new statistical physical model to quantify the contribution of external forcings to global warming as a “deterministic trend” of the surface temperature series (instead of as non-stationary processes that yield a stochastic trend) and by which constrained the reconstruction of the early time series (1850-1880). The results suggest that the latest AR6 still slightly underestimates the rate of global warming since pre-industrialization, as the existing dataset slightly overestimates surface temperature anomalies over this baseline period. Our study provides a new perspective on the attribution and historical reconstruction of global warming since industrialization. These insights should also help humanity develop future climate change adaptation and mitigation strategies.

Observations show that the teleconnection between the El Niño-Southern Oscillation (ENSO) and the Asian summer monsoon (ASM) is non-stationary. However, the underlying mechanisms are poorly understood due to the inadequate availability of reliable, long-term observations. This study uses two state-of-the-art data assimilation-based reconstructions of last millennium climate to examine changes in the ENSO–ASM teleconnection; we investigate how modes of (multi-)decadal climate variability (namely, the Pacific Decadal Oscillation, PDO, and the Atlantic Multidecadal Oscillation, AMO) modulate the ENSO–ASM relationship. Our analyses reveal that the PDO exerts a more pronounced impact on the ASM variability than the AMO. By comparing different linear regression models, we find that including the PDO in addition to ENSO cycles can improve the prediction of the ASM, especially for the Indian summer monsoon. In particular, the dry (wet) anomalies caused by El Niño (La Niña) in India will be enhanced during the positive (negative) PDO phases due to a compounding effect. However, composite differences in the ENSO–ASM relationship between positive and negative phases of the PDO and AMO are not statistically significant. A significant influence of the PDO/AMO on the ENSO–ASM relationship occurred only over a limited period within the last millennium. By leveraging the long-term paleoclimate reconstructions, our findings underscore the non-stationary nature of the PDO and AMO in modulating the ENSO–ASM relationship.

Recently heavy rainfall events are increasing in Japan. Especially during the Baiu-Meiyu rainfall season, heavy rainfall associated with quasi-stationary band-shaped precipitation systems frequently occurred. It was found that lightning frequencies were active during the heavy rainfall observed in July 2017. On the other hand, lightning frequencies were inactive during the heavy rainfall observed in the following year in July 2018. It was analyzed that convective systems were different between the two cases and that leads the difference of lightning activities. In this study 25 lightning and weather observation systems were deployed over the Southeast Asia, western north Pacific islands and Japan to monitor the lightning activities over these regions. During July 10, 2023, heavy rainfall was observed over northern Kyushu Island in Japan and rainfall amount reached 266 mm within 12 hours. Lightning was very active during the heavy rainfall. Another heavy rainfall was observed over Shikoku Island in Japan during August 9 to 10, 2023 and rainfall amount reached 638 mm within 24 hours. On the other hand, lightning activities were very weak during this case. We analyzed the difference of environmental conditions between the two cases. During the heavy rainfall in northern Kyushu case, west-southwesterly low-level wind provided moisture into the convective clouds. The origin of the moisture can be traced back to China continent. Rich amount of aerosol may lead to create more graupel in the clouds for favorable conditions for lightnings. However, heavy rainfall in Shikoku Island case, south-southeasterly low-level wind was dominant which was blown from the open ocean over the Pacific Ocean. Poor amount of aerosol moisture may suppress creating graupel in the clouds for lightning. It is similar to our previous study that we observed less lightning activity in the tropical cyclone generated far from southeast Asian islands.

Atmospheric Rivers (ARs), corridors of intensive water vapor transport, have received much attention recently due to their potential large economic and societal impacts. However, a comprehensive understanding of the interannual variability of ARs over East Asia is still lacking. This study examines the frequency of AR occurrence over East Asia based on the EAR5 reanalysis. It is shown that the frequency of AR occurrence over East Asia displays obvious seasonality, with its peak in early boreal summer. ARs contribute to up to 50% of total precipitation and largely modulate the interannual variability of precipitation in East Asia during early boreal summer. The interannual variability of ARs over East Asia during early boreal summer is largely controlled by the Indo-western Pacific Ocean Capacitor (IPOC) effect, which is mainly triggered by El Niño-Southern Oscillation and sometimes by Indian Ocean dipole. During early boreal summer, a higher frequency of AR occurrence over East Asia is associated with anomalous anticyclonic circulation (AAC) over the Indo-western Pacific region. The AAC, the atmospheric signal of the IPOC mode, indicates an enhanced East Asian summer monsoon and a northwestward shift of the western North Pacific subtropical high, favoring the AR occurrence over East Asia. This study illustrates that a strong positive Indian Ocean dipole event could impact AR activity over East Asia in the following summer by triggering the IPOC effect. The IPOC-related signals 2-4 months ahead could be useful to predict the AR occurrence over East Asia during early boreal summer.

Oxygenated volatile organic compounds (OVOCs) are crucial reactive pollutants with profound implications for air quality and human health. They play critical roles in tropospheric photochemistry and oxidation capacity, significantly influencing radical cycling and O3 formation. Despite their increasing recognition, the lack of precise quantification hampers a full understanding. We present an integrated study of OVOCs employing both real-time online measurement and the cartridge-based analytical method in the coastal atmosphere of Hong Kong. Using PTR-ToF-MS, we measured 83 VOC species, where OVOCs constituted the majority (77.4%) of concentrations, contributing significantly (>50%) to photochemical OH reactivity and ozone formation potential (OFP). The newly developed analytical method based on UHPLC-MS/MS identified and quantified 47 carbonyl compounds, which play a substantial role in peroxyl radical and ozone formation. Furthermore, previously unmeasured long-chain carbonyls (C≥5) and di-carbonyls, absent in conventional methods, displayed noteworthy abundance and photochemical reactivity, enhancing the O₃ formation rate by up to 30%. This comprehensive study underscores the importance of OVOCs, especially carbonyl compounds, in photochemical pollution, providing insights for effective photochemical pollution control. The developed methodologies enrich our understanding of OVOC chemical composition and reactivity, augmenting our grasp of their significance in atmospheric chemistry and air quality.

Alkyl nitrates (RONO₂) constitute a vital subclass of total reactive nitrogen (NOy) within the Earth's atmosphere. These compounds play a pivotal role in atmospheric chemistry, exerting a substantial influence on air quality. The concentration of alkyl nitrates exhibits considerable variability across diverse regions of human habitation. Characterizing the variations in alkyl nitrate concentrations poses a significant challenge, essential for evaluating the intensity of photochemical reactions and discerning contributions from various sources. Hereby, we present the alkyl nitrate measurement results via canister sample collection and GC-MSD/ECD analysis at a coastal site in Hong Kong during 2021. An analysis of the source apportionment of eight C₁-C₅ alkyl nitrate species, employing anthropogenic and marine tracers, indicates that the predominant contributors to alkyl nitrates in Hong Kong are associated with secondary formation. Moreover, heightened contributions from biomass combustion were observed during the autumn and winter seasons. Notably, marine emissions exert a significant influence on methyl nitrate levels, particularly in the summer. Our study underscores the noteworthy impact of inland biomass burning on photochemical pollutants in Hong Kong. Consequently, recognizing the regional interdependence, cross-regional cooperation in governance within the Greater Bay Area is imperative for a more comprehensive management strategy addressing secondary photochemical pollution.

Organic aerosols (OA) account for a large portion of atmospheric aerosols, which have played a significant role in climate change and human health. The typical sources of POA (Primary OA) like biomass burning, cooking and transportation had been obtained by different measurement techniques. The specific sources of SOA (Secondary OA) are still unclear due to limited highly sensitive and quickly responsive measurement techniques, which have been proven challenging. Source apportionment is key to understand the formation and air pollution control for policy makers. However, the detailed sources of OA in megacities are currently unclear due to lack of information of chemical composition of OA at molecular level. Particularly, specific precursors and formation processes of SOA in megacities remain largely unclear. The newly developed high-resolution online measurement technology like extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS) can provide specific information of OA at molecular level. In this study, we measured chemical composition of OA at molecular level using EESI-TOF-MS at Shanghai in February to March, 2022 and investigated the sources of OA. More than 1000 organic species were detected. Twelve sources (factors) are determined on mass spectra by bin-PMF (Positive Matrix Factorization), of which seven factors are identified as POA and five factors as SOA. POA sources are identified its source based characteristic tracers and time series. SOA sources are identified by linking to their precursors and processes based detailed mass spectra, their time series, and concomitant measurement of physiochemical parameters and gas-phase species. We found that both POA and SOA contribute significantly to OA in winter at urban Shanghai. Online molecular composition in this study reveals under-appreciated sources and processes of OA in megacities. These information enables better understanding of OA sources and more effective air pollution control strategy.

Amines are important nitrogenous compounds in the atmosphere, they affect new particle formation, aerosol properties, climate change and human health. Due to the relatively high volatility, strong polarity, and low ambient concentrations, they are subjected to great difficulty in the accurate determination. In this study, a derivatization- free determination method based on HPLC-MS was developed to determine the amines in fine particle samples collected at coastal Mt. Lao in the spring of 2021. The samples were pre-treated with a non-derivatization method and separated with a hydrophilic interaction column. 16 amines were identified and quantified with the limits of detection from 0.59 to 75.46 ng mL−1 and the recovery rates ranging from 59% to 92%. The average concentration of amines at the sampling site was around 60 ng m−3. Dimethylamine was the most abundant specie with mean fraction of 27% among the quantified amines, followed by diethylamine and ethanolamine. Meanwhile, amines exhibited higher concentrations during the daytime than nighttime at the mountain site. Most amines primarily came from terrestrial activities, while TMA was mainly from marine sources. Seven source factors were identified by PMF model, including agricultural emissions, industrial production, biomass burning, coal combustion, marine emissions, traffic emissions and crustal dusts. Notably, high contributions from agricultural source and biomass burning were observed at this coastal mountain site.

Lignin is a polyphenolic polymer commonly found in the supportive tissue of plants. When biomass burns, lignin can release various phenolic compounds into the atmosphere. These lignin-derived phenols can be easily nitrated due to the strong electron-donating effect of the hydroxyl group and methoxy group on their benzene ring. The resulting nitrated products are nitro-aromatic compounds (NACs), which are important components of brown carbon. While simple NACs have been identified, including 4-nitrophenol (4NP), 4-nitrocatechol (4NC), 2-methyl-4-nitrophenol (2M4NP), 3-methyl-4-nitrophenol (3M4NP), and 5-nitrosalicylic acid (5NSA), lignin-derived NACs with more and larger substituent groups on the benzene ring have received little attention. In this study, we applied a high-resolution UPLC-Orbitrap mass spectrometer (MS) system to analyze NACs in 58 ambient PM_2.5 samples collected at an urban site in Guangzhou in 2013. The outstanding resolution of Orbitrap MS enabled us to identify NACs with 30 different molecular weights and more than 300 individual NACs, taking isomers into consideration. Among the simple NACs with authentic standards or quantification surrogates, the four most abundant ones were 5NSA, 4NC, 3NSA, and 4NP, with average concentrations ranging from 4.4 to 14.2 ng m^-3. In addition, we classified a group of complex lignin-derived NACs, which included hydroxypropyl-dinitrophenol, dimethoxy-nitrophenol, hydroxyethyl-dinitrophenol, hydroxyethyl-nitrophenol, ethyl-nitrocatechol, and nitrovanillic acid. These compounds contain multiple substituent groups (e.g. methoxy group, hydroxypropyl group, and hydroxyethyl group) originating from lignin. We observed a strong correlation (R² ranges from 0.50 to 0.98) between most of these complex NACs and the simple NACs throughout the entire sampling year, indicating the simple NACs likely originated from the burning of lignin. Furthermore, we detected other untargeted NACs, such as trinitrophenol and nitrobenzenetriol, in our samples.

Tropospheric ozone (O₃) plays a crucial role in air quality, climate change, and human health. The large span of tropospheric O₃ photochemistry across various global chemical transport models hinders our ability to evaluate tropospheric O₃ distribution and its environmental impacts. An in-depth understanding of O₃ photochemistry relies on observational constraints on the O₃ chemical budget, especially in pristine environments or free troposphere. Here we present a detailed O₃ chemical budget observation at a remote site over the Tibetan Plateau. Surprisingly high production rate of peroxy radicals was observed, which was mainly attributed to oxidation reactions of oxygenated volatile organic compounds (OVOCs). Radical chain propagation rates were promoted by elevated nitrogen oxides (NO_x) and further facilitated O₃ production. Neither OVOCs nor NO_x could be explained by their local chemical production, suggesting missing sources of them. A global chemical transport model, GEOS-Chem, underestimated both OVOCs and NO_x and hence underestimated the O₃ production rate by 60%. The global model also underestimated photolysis frequencies and water vapor and therefore underestimated the O₃ destruction rate by 20%. Our findings indicate a need for additional observations on the fate of reactive carbon and reactive nitrogen, to improve the model's ability to predict tropospheric O₃.

N₂O₅ serves as a pivotal intermediate in the atmospheric nitrogen cycle. Utilizing a flow tube reactor, we have unveiled a previously undocumented source of N₂O₅ during the photocatalytic oxidation of NO2 on the TiO₂ surface. The release rate of N₂O₅ from TiO₂ was found to be contingent on initial NO₂ concentration, relative humidity, O₂/N₂ ratio, and irradiation intensity. Both experimental evidence and quantum chemical calculations concur that NO₂ can react with surface hydroxyl groups and electron holes generated on TiO₂, subsequently combining with another NO₂ molecule to form N₂O₅. This N₂O₅ physiosorbs on TiO₂, exhibiting a low adsorption energy of -0.13 eV. Box model simulations suggest that this newfound source of N₂O₅ emitted from TiO₂ could elevate daytime N₂O₅ concentrations by up to 20% in urban areas where abundant TiO₂-containing materials and high NOx concentrations are co-present. Our research not only presents a novel chemical mechanism for N₂O₅ formation but also holds significant implications for air quality in urban settings.

Reactive nitrogen emissions from energy and food production (N_r; nitrogen oxides, NO_x, and ammonia, NH₃) contribute substantially to global PM_2.5 and O₃ air pollution, damaging human, ecosystem and vegetation health. To illustrate the role of N_r controls in near-term air quality management, we quantify how the shares of N_r in global PM_2.5 pollution evolve from the present to the year 2050. We use the GEOS-Chem atmospheric chemistry transport model and future emission scenarios under various climate-socioeconomic pathways. We find that the phase-out of anthropogenic NH₃ emissions remains necessary for achieving the WHO target for PM_2.5 and that this is more effective than phasing out NO_x. For more moderate N_r control levels of 25% and 50%, NH₃ controls become less effective than NOx controls in many locations. This is because climate mitigation policies enable clean energy transitions thus reductions of NO_x and SO₂ emissions, the chemical regime shifts towards a NH₃-saturated regime. The later NH₃ is addressed, the more ambitious reduction level is required for effectiveness to emerge. To illustrate the technological and policy feasibility of N_r controls, we assess achievable N_r (NH₃ and NO_x) emission mitigation potentials and evaluate consequent reductions in global air pollution, and human and ecosystem health impacts. We construct an integrated modeling framework that combines future N intervention scenarios, integrated assessment models and the GEOS-Chem model. We find that, through improving crop nitrogen use efficiency and exploiting abatement technologies for industrial, transport and power sectors, global NH₃ and NO_x emissions can be reduced by 40% and 52%, respectively, of 2015 levels by 2050, generating a wide array of consequent benefits for air, yields, ecosystems and human health. Without nitrogen interventions, environmental/health objectives examined will deteriorate by 2050 compared to 2015. N_r interventions remain a key strategy for improving global environmental health that should not be overlooked.

The Asian Australian Monsoon (AAM) region, characterized by abundant precipitation and hydrological extremes, necessitates in-depth explorations of crucial synoptic environmental factors influencing extreme rainfall events. Building on previous research highlighting moisture content and low-level dynamic flow, we developed a data-driven framework using Variational Autoencoder (VAE), an unsupervised and explainable machine learning model, to dissect prominent moisture transport patterns and explore their linkage to extreme precipitation emergences. The vertically-integrated water vapor transport (IVT) from ECMWF Reanalysis v5 (ERA5) is adopted as the primary indicator of moisture environment. Through VAE, daily fields of IVT spanning from 2001 to 2019 are encoded into two-dimensional vectors on a continuous latent space and classified into different regimes. Each latent dimension captures the seasonal progression and synoptic to sub-seasonal variability, respectively, of the large-scale moisture transport pattern. The heat maps of extreme rainfall occurrence derived from Integrated Multi-satellitE Retrievals of Global Precipitation Measurement (IMERG) 0.5-hourly data reveal distinctive patterns across various moisture transport regimes. This hints a potential correspondence between distinct extreme rainfall distributions and specific moisture transport configurations in the AAM region. Detailed investigations into each moisture transport regime, employing composite analyses of synoptic fields and tropical variability indices, elucidate the modulation of moisture transport and the possible mechanisms in influencing extreme rainfall emergence. The current VAE model serves as a foundational step in interpreting the primary factors driving variations in moisture transport patterns and offers the opportunity for estimating future changes in extreme convective systems' hotspots using the “storyline approach”, given the higher confidence in synoptic flow modeling in General Circulation Models. Results and discussions based on CMIP6 models will also be presented to provide valuable insights to the broader scientific discourse on extreme rainfall events in the AAM region.

The emergency of global‐scale hydroclimatic extremes (i.e., meteorological droughts, extreme precipitations, heat waves and cold surges) and associated compound events has recently drawn much attention. A global‐scale unified and comprehensive event set with accurate information on spatiotemporal evolutions is necessary for better mechanism understanding and reliable predictions in sequential studies. Accordingly, this manuscript describes the first‐generation global event‐based database of hydroclimatic extremes produced with the newly proposed 3D (longitude–latitude–time) DBSCAN‐based workflow of event detection. The short name of this database is Glo3DHydroClimEventSet(v1.0) , which is obtained from the FigsharePlus webpage ( https://doi.org/10.25452/figshare.plus.23564517 ). The 1951–2022 ERA5‐based multiscale and multi‐threshold daily running datasets of precipitation and near‐surface air temperature are calculated and employed as the input data. A comprehensive event set of hydroclimate extremes is the output of the 3D DBSCAN‐based workflow. From perspectives of spatiotemporal evolutions, this event‐based database is also measured and attached with metric information. For case‐based validation, some recently reported hydroclimatic extremes (e.g., the 2020 summertime flood‐inducing Yangtze River extreme precipitation event) are employed and accurately detected in the Glo3DHydroClimEventSet(v1.0) database. Meanwhile, global‐scale spatiotemporal distributions are preliminarily analysed. For example, global‐scale event counts of extreme heatwaves displayed an increasing tendency since 2005, with a rapid increase after 2010. To sum up, this Glo3DHydroClimEventSet(v1.0) database may facilitate new scientific achievements concerning event‐based hydroclimatic extremes, especially in communities of atmosphere, hydrology, natural hazards and associated socioeconomics. The DOI-based paper linkage is https://doi.org/10.1002/joc.8289.

Based on the hourly rainfall gauge data and ERA5 reanalysis for the period 1980–2020, typical synoptic patterns responsible for summer regional hourly extreme precipitation events (RHEPE) over the middle and lower Yangtze River basin have been objectively identified using a circulation clustering method. It is found that the Meiyu front with different locations and intensities imbedded in the East Asian summer monsoon, and landfalling typhoons are the leading contributors. As the dominant synoptic pattern, the Meiyu front pattern is associated with ∼92% of the total RHEPE occurrence and can be categorized into a southerly strong-Meiyu type and a northerly weak-Meiyu type. The RHEPE occurrence shows a predominant morning peak associated with the southerly strong-Meiyu type and a secondary late afternoon peak related to the northerly weak-Meiyu type, in which the Meiyu front is pushed northward by the strengthened western North Pacific subtropical high accompanied by accelerated low-level southwesterly flow

Utilizing ERA5 reanalysis data, we carried out a series of analyses to investigate the spatiotemporal characteristics of extreme precipitation over the Yangtze River Basin (YRB) and the major drivers influencing the interannual variability of summer extreme precipitation during 1950-2021. The results indicate that anomalous anticyclones/cyclones over the western North Pacific (WNP) and El Niño/La Niña like sea surface temperature (SST) patterns in the preceding winter are associated with increased/decreased summer extreme precipitation over the YRB. Furthermore, the El Niño and Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the anomalous Indian Ocean Basin Warming (IOBW), which occur in boreal autumn/winter, are shown to have delayed effects on the YRB's extreme precipitation in the subsequent summer. Both ENSO and IOD have the capability to generate anomalous anticyclones/cyclones over the Western North Pacific (WNPAC/WNPC), which in turn can enhance or diminish water vapor transport, leading to an increase or decrease in summer extreme precipitation over the YRB. However, the impact of negative ENSO-IOD forcings is generally weaker than that of positive forcings. The persistence of the abnormal tropospheric WNPAC/WNPC is primarily driven by local thermodynamic forcing and feedback from the IOBW. It is noteworthy that only strong ENSO events can trigger the anomalous IOBW, resulting in more pronounced responses in summer extremes. Notably, the negative ENSO-IOD phases yield non-linear outcomes, thus offering less robustness for the predictability of summer extremes over the YRB. This study provides valuable insights into the interannual variability of summer extreme precipitation in the YRB, highlighting the key predictors and physical mechanisms responsible for these variations. Keywords: Yangtze River Basin; extreme precipitation; ENSO; IOD; Indian Ocean Basin Warming; WNPAC.

Based on meteorological observation from stations over the past thirteen years, China was divided into seven regions: Central China, East China, Southwest China, Southeast China, North China, Northeast China, and Western China. A study was conducted on the spatial and temporal distribution characteristics and their evolution of short-duration intense precipitation in China. The results found that the occurrence of short-duration intense precipitation in China is seasonal, mainly occurring in spring and summer, and primarily concentrated from May to September. Among them, the southern regions have the highest frequency in summer, followed by East China and Central China, then the eastern coastal areas of North China and the southern coastal areas of Northeast China, with the Western region having a very low frequency. In terms of daily occurrence of short-duration intense precipitation, it occurs mostly in the early morning in the Southwest and Central China; while in the downstream Southeast and East China, it mainly occurs in the afternoon, with the highest frequency in coastal areas. In the plains of Northeast China, it also occurs mainly in the afternoon, but with a frequency far less than that of the Southeast and Southwest regions. In the plains of North China, there is no significant difference in the daily time period of occurrence, with occurrences in the early morning, afternoon, and evening. In terms of average intensity of short-duration intense precipitation, the Southeast and North China regions are more intense than other regions. The early morning short-duration intense precipitation in the Southwest and Central China has an eastward transmission characteristic, showing that after the convective cell is generated, it moves eastward, and intensifies in the afternoon upon reaching East and South China. Over the thirteen years, the occurrence of short-duration intense precipitation has shown different evolutionary characteristics in different regions.

As the first satellite mission to give wind profile information on a global scale, Aeolus was launched in 2018, and its horizontal line of sight wind data has been available from the European Space Agency (ESA) Earth Online Portal since May 2020. The significant positive impact of this data on numerical weather prediction has been reported in many researches (Rennie et al., 2021; George et al., 2021). We presented about the assimilation impact on JMA’s global data assimilation and forecasting system and the impact on typhoon forecasting in AOGS 2021 and 2022 (Okabe and Okamoto 2024, accepted). We have started investigating the assimilation impact on heavy rain forecasts using both global and regional data assimilation systems. The configuration of the control experiments (CNTLs) in these assimilation experiments mirrored the operational global system as of December 2019 and the operational regional system as of April 2020 of the Japan Meteorological Agency. The test experiments (TESTs) were the same as CNTLs except the use of Aeolus data. The target event is the heavy rainfall incident that struck Kumamoto and Kagoshima prefectures on the southern Japanese island of Kyushu, on July 4th, during the heavy rain events of July 2020 in the middle of the Baiu season (the East Asian rainy season). As a result of flooding and landslides, more than seventy people were confirmed dead, and approximately 15,000 buildings were destroyed, damaged or flooded. In the TEST of the global experiments, the positional error in heavy rain forecasts has been slightly improved. The investigation into the assimilation impact in the regional experiments is ongoing. The detailed results will be shown in the presentation.

This study assesses the WRF model's accuracy in predicting sub-seasonal to seasonal rainfall in Thailand, specifically focusing on the performance using Noah and Noah-MP land surface models. The research aims to contribute insights into the WRF model's efficacy for sub-seasonal to seasonal rainfall prediction, improving understanding and reliability for the region. The analysis utilizes weekly summer rainfall data with a fine 5 km resolution nested domain driven by CFS Reanalysis data. The study centers on a selected 2021 summer monsoon event marked by a strong Madden-Julian Oscillation. Evaluation of model predictions for six sub-regions reveals increased rainfall in Weeks 2 and 9, generally aligning with observations. Discrepancies exist, particularly in the model's overestimation in the east and south but accurate predictions in the southwest. Probability Distribution Function plots highlight divergent rainfall patterns. Noah-MP outperforms Noah, suggesting its suitability for refining the model to enhance rainfall predictions in Thailand.

The increase in the frequency of extreme precipitation in different parts of the world is well documented and a cause for concern in terms of global climate change. Although clouds are the only source of precipitation, there is a lack of knowledge about the type of clouds involved in extreme precipitation events. Observations by MODIS and IMD show that over the central Indian region (19^oN-26^oN, 75^oE-85^oE), about 60% of the extreme precipitation comes from deep convective clouds (DCC), which have a cloud top pressure (CTP) of less than 440 hPa and an optical cloud thickness (COT) of over 23. It was also observed that cloud water liquid (CWL) and COT show the highest correlation with extreme precipitation and also show the highest contrast between extreme and non-extreme precipitation events. Simulations (hindcast) of a global 12KM model are also analysed for extreme precipitation events. It is found that the model underestimates the threshold for extreme precipitation (99th percentile climatology) with increase of lead time. Simulations of the associated cloud optical parameters also deteriorate with increasing lead time. The model tends to overestimate the convection and cloud water liquid during an extreme precipitation event. The model also fails to capture the observed relationship between the frequency of extreme precipitation and the frequency of deep convective clouds and shows no correlation between them at all lead times.

Vegetation in urban areas, referred to as ‘urban forests’, is recognized as a localized carbon sink for removing a considerable amount of atmospheric CO₂ and the areas of them are changing as urbanization accelerates. Thus, quantifying the carbon uptake of vegetation in urban areas is necessary for improving GHG inventories in cities. Given that the carbon sequestration of urban forests is calculated based on coarse spatiotemporal resolution statistics, the carbon absorption capacity of urban forests is often inadequately quantified and easily overlooked. However, there are limited studies estimating GPP in urban forests due to substantial variation in environmental factors directly related to photosynthesis in cities and the difficulties of obtaining data that can isolate the influence of vegetation. Therefore, we deployed hyperspectral devices (FloX, JB Hyperspectral Devices) to observe Solar-induced chlorophyll fluorescence (SIF) and Eddy covariance system instruments to measure CO₂ fluxes together in two urban forests (Changgyeongung and Namsan Park) in Seoul to explore the carbon uptake capacity of urban forests. We primarily analyzed seasonal trends in Net Ecosystem Exchange (NEE) using CO₂ flux data to examine whether or not urban forests are carbon sinks. By comparing canopy-level SIF with satellites SIF on the same date, along with the CO₂ flux, we found ground-observed SIF can represent the dynamic plant physiological responses. Subsequently, we attempted to quantify GPP at these multi-scale observation sites by partitioning NEE and Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model simulation. This study holds significance in utilizing diverse scale observational data to help us understand the physiological function and responses of urban forests and improve GHG inventories within cities. Additionally, it introduces the first multi-scale observation site that simultaneously measures CO₂ flux and SIF in the same urban forests, emphasizing the need for high spatiotemporal resolution data through the ground-based observation network.

Tree and forests can have a potential to abate the fine dust through the adsorption and deposition under the tree canopy against episodes of wildfire, fireworks and high PM in a city. During high PM episodes in Winter and Spring, most peoples do hesitate to take a field activity of walking, trekking, and climbing in hilly and mountainous landscapes dominated by 63.5% of forests in Korea. Thus, National Institute of Forest Science (NIFoS) has been established Asian Initial for Clean Air Networks (AiCAN) across the Korean Peninsula (44 locations, 132 points) to measure TSP, PM10, PM2.5, PM1.0 and meteorological factors (wind velocity, relative humidity, and temperature) with the equipment of GRIMM EDM 365 from 2019 to 2022. All observation data was monitored in ten minutes interval and the maintenance process of equipment was followed by the legislation of Korean Government. We analyzed the annual amount of PM reduction by forests comparing with the observation point between forests and urban by the analysis of AiCAN dataset. The PM reduction in the forest was the highest in spring and winter when high PM concentrations were observed, and the annual PM10 and PM2.5 were reduced by 32.9kg/ha and 22.7kg/ha in 2021, 26.4kg/ha and 19.3kg/ha in 2022, 29.2kg/ha and 18.9kg/ha in 2023. In consideration of theoretical value was estimated as 46kg/ha, the observation values were distributed at the range of theoretical values. This abatement could be attributed to the effects on absorption, adsorption, blocking, and deposition of fine dusts under tree canopy of forests. NIFoS will utilize this platform to understand the interaction between forests and aerosol not only for predictable and controlled Events of high PM episodes, yellow dusts, heat waves, and typhoon, also un-predictable and un-controlled events of wildfires and fireworks.

Methane (CH₄) is important greenhouse gas with a high Global Warming Potential (GWP) that significantly contributing to climate change. In urban, methane emissions are mainly come from the energy and waste sectors. This study aims to enhancing our understanding of methane emissions from the waste sector, especially solid waste disposal, as a major contributor to total urban methane emissions. Landfills, a key source in the waste sector, substantially contribute to methane emissions through the decomposition of organic waste. The emissions are influenced by weather conditions, but existing methane inventories inadequately consider this variability. In this study, we introduce an improved methane inventory methodology that includes meteorological conditions, offering a more comprehensive representation of methane emissions. In the context of Seoul, we present spatiotemporally high-resolution methane inventories derived from the waste sector. The results show a significant increase in methane emissions during the spring and summer seasons, emphasizing the seasonal variability associated with waste decomposition processes. This detailed understanding is crucial for effective urban methane management. The implementation of our methodology holds significance in improving the accuracy of methane emission estimates from the waste sector. By integrating weather conditions into the methane inventory, this study contributes to a more improved understanding of methane emissions in urban area. This enhanced methodology can support the development of more effective waste management policies and strategies to mitigate methane emissions, addressing climate change in urban environments.

Sewer networks are one of the major sources of greenhouse gas (GHG) emissions in urban areas. However, they are currently missing in South Korea's national GHG inventory. Here, we measured atmospheric methane emissions from urban sewer networks in old residential and commercial areas of Seoul (Gwanak district) using a GHG monitoring platform consisting of the electric vehicle and GHG monitoring instruments such as methane (CH4), and ethane (C2H6). The results of this research showed significant CH4 emissions of about 573 [395-831] CH4 t y-1 from sewers through the manholes and rain gutters. The CH4 emissions in the study area are primarily due to microbial activity within the sewer networks, as suggested by the majority of C2:C1 ratios being below 0.005. This is because more than 90% of the sewer network in Seoul is a gravity drain type of combined sewer network, resulting in the generation of CH4 emissions from the microbial activity. Thus, manholes and rain gutters, which are directly connected to the combined sewer networks are major sources of atmospheric methane emissions in Seoul. The results of this study suggest that proper treatment of sewer networks especially manholes and rain gutters are required to mitigate the one of the significant methane emissions in urban areas of South Korea. This work was supported by Korea Environmental Industry & Technology Institute (KEITI) through "Project for developing an observation-based GHG emissions geospatial information map", funded by Korea Ministry of Environment (MOE)(RS-2023-00232066).

Methane was recognized as one of the most important greenhouse gases after carbon dioxide. Accurate estimation of methane emission is the key to establishing a reasonable and effective reduction target and strategy for achieving the goal of limiting global warming to 1.5 ℃ within reach. Using the eddy covariance technique, methane flux was measured at a meteorological tower in Beijing, China in the summertime to increase the understanding of methane emission in urban regions. The footprint climatology (90% contour lines) demonstrates that the measured flux can represent the summer emission characteristics of ~70 km² of surrounding areas categorized as the typical urban landscape in the megacity of Beijing. The quality assurance and quality control results show more than 50% of the flux data can be labeled with high quality. Compared with the methane flux measured in other cities, the methane flux measured in Beijing, 152.6 ± 107.9 nmol/m²/s, was relatively large. There was no significant diurnal pattern and no clear correlation between methane flux and temperature/solar radiation can be found, which suggests that the biogenic source might not be the dominant source of methane within the footprint area. Constant background emission due to natural gas leakage was likely the main contributor. 

Atmospheric methane (CH4) is currently more than 160% above pre-industrial levels, and the 2020-2022 growth was the fastest since systematic measurements started in the 1980s. This rapid increase is a challenge for reaching the climate mitigation goals of the Global Methane Pledge from COP26, which requires steep cuts in CH4 emissions by 2030. Slowing or reversing the accelerating growth in atmospheric CH4 will require an improved understanding of the anthropogenic and natural CH4 budget, which is currently underconstrained. We have improved our understanding of the global CH4 budget via precise measurements of 13C:12C (δ13C-CH4), a tracer capable of separating fossil and microbial emissions. When atmospheric CH4 increased rapidly after 2006, δ13C-CH4 decreased after increasing for the last 200 years, indicating that the CH4 rise has been dominated by microbial sources such as wetlands and agriculture, not fossil fuels. To provide quantitative estimates of global CH4 emissions that are consistent with observed patterns of atmospheric CH4 and δ13C-CH4, we updated CarbonTracker-CH4 by jointly assimilating atmospheric measurements of CH4 and δ13C-CH4, optimizing source-specific fluxes at a grid-scale, and extending our estimation from 1997 to 2021.Our inversion shows that the global CH4 emission has increased more than 100 Tg yr-1 since 2000 and reached ~650 Tg yr-1 in 2021, and the large long-term increase was from Asia and North America regions. We find that the dual-tracer inversion of CH4 and δ13C-CH4 attributes the growth in atmospheric CH4 in 2020-2021 to a large increase in microbial sources and a slight decrease in fossil sources. The large increase in emissions was mostly from the tropical and Eurasian regions. In contrast, the inversion that uses CH4 data only does not match atmospheric δ13C-CH4 and simulates an increase in fossil sources in 2020-2021. All our results are publicly available at https://gml.noaa.gov/ccgg/carbontracker-ch4/.

The industrial revolution escalated greenhouse gas (GHG) emissions, driving climate change to a critical juncture that necessitates immediate action. Monitoring essential GHGs like CO₂ and CH₄ is imperative for comprehending their impact on the climate system. East Asia, with high energy consumption and robust industrial activity, is a significant GHG emitter, emphasizing the need for meticulous monitoring. While CO is not a direct GHG, it reacts with atmospheric OH radicals, affecting the lifetime of CH₄ and influencing radiative forcing and the global climate. CO is an important trace gas of combustion processes and industrial activities, providing insights into the sources of CH₄ and CO₂. Thus, monitoring CH₄, CO, and CO₂ enhances our understanding of climate change and, to a lesser extent, improves air quality, yielding dual benefits. To investigate the characteristics of CH₄, CO₂, and CO, we established a sampling and instrumental analysis system at the Hong Kong University of Science and Technology. Observation results revealed pronounced seasonality, with higher concentrations in the cold season and lower concentrations in the warm season for all three gases. Non-parametric wind regression and the HYSPLIT back trajectory model indicated that continental air masses carried more concentrated gases, while oceanic air masses contributed to local air dilution. Fluctuations around the background level showed a strong correlation between CH₄ and CO, while CO₂ exhibited a lower correlation with the other two. This suggested distinct sources in Hong Kong compared to typical Chinese cities, where CO₂ and CO exhibited a higher correlation usually. Further analysis utilizing the Potential Source Contribution Function (PSCF) identified Central and East China, as well as northern Guangdong, as major source regions for CH₄ and CO, with Central China being the dominant source region for CO₂. These findings provide valuable insights for developing effective carbon neutrality policies in Hong Kong and China.

Seoul and Tokyo, as major global contributors to greenhouse gas emissions and air pollutants, are focal points for emission reduction initiatives with significant implications for global climate change mitigation. In the winter of February 2022 and 2023, Seoul National University Climate Lab and the National Institute for Environmental Studies (NIES) conducted an extensive observational campaign in both cities using portable ground-based FTIR spectrometers, EM27/SUN, offering a comprehensive analysis of column-averaged dry air mole fractions of CO₂, CH₄, and CO (XCO₂, XCH₄, XCO). In addition, we incorporated integrated observations of CO₂, CH₄, and CO concentrations using ground-aircraft-satellite measurements intensively carried out in Seoul to monitor greenhouse gases and air quality in a megacity. We analyze the spatial distributions and variations of greenhouse gases and air pollutants within the urban area of Seoul, as well as assess the emission characteristics using a combination of the Column-Stochastic Time-Inverted Lagrangian Transport model (X-STILT) and ratio analysis. This study presents results from the two-year campaign, offering a comparative analysis of greenhouse gas concentrations in Seoul, South Korea and Tokyo, Japan. Overall, this research aims to monitor the current atmospheric conditions in the two major cities, as well as to analyze the three-dimensional structure of greenhouse gases and air pollutants in Seoul.

In boreal summer of 2022, Pakistan experienced extremely high rainfall, resulting in severe flooding and displacing over 30 million people. At the same time, heatwaves persisted over central China and Europe. The coexistence of these extreme events suggests a possible linkage. Our analysis indicated that the record rainfall was mainly induced by compounding factors. These included (1) La Niña-induced strong anomalous easterlies over the northern Indian subcontinent, (2) intense southerlies from the Arabian Sea with an upward trend in recent decades, (3) an interaction between extratropical and tropical systems, specifically the northerly flow downstream of the Europe blocking and the southerly monsoon flow from the Arabian Sea. Wave activity flux and regression analyses unveiled a distinct stationary Rossby wave-like pattern connecting the flooding in Pakistan and heatwaves in Europe and China. This pattern, an emerging teleconnection pattern in recent decade, exhibited substantial differences from the reported teleconnection patterns. We also noted the positive feedback of the excessive Pakistan rainfall could further enhance the largescale background flow and the heavy rainfall itself. The 2022 Pakistan flood event was an intensified manifestation of the 2010 Pakistan flood event, which was also caused by compounding factors, but occurred in a more pronounced upward trend in the both tropics and extratropics.

The quasi-biweekly oscillation (QBWO) is an important component of tropical monsoon variations. The QBWO rainfall demonstrates a westward propagation chain from the western North Pacific to the Arabian Sea. Specifically, a delay of about 2 days is observed in the rainfall increase over the Bay of Bengal following an increase over the South China Sea. The mechanisms for the development of QBWO rainfall in the Asian tropical monsoon region is revealed by a moisture budget analysis. Positive specific humidity anomalies are primarily induced by the leading horizontal moisture advection at the lower level. The interaction between QBWO anticyclones circulation anomalies and mean moisture gradient facilitates the increase of moisture in the Asian tropical monsoon region, thereby instigating the westward extension of QBWO. Furthermore, the mean zonal cross-equatorial flow also contributes to the moisture transportation dynamics. The westward propagation chain revealed may benefit the subseasonal rainfall prediction in the Asian tropical monsoon region.

The South Asian summer monsoon is one of the most spectacular monsoon systems in the world. It affects the production and lives of billions of people. Climate change is reshaping the spatial distribution of summer monsoon rainfall. This study has found that rainfall over Pakistan and northwestern India during the summer monsoon has increased by 46% from 1979 to 2022 and is increasing at a rate of 0.4-0.6 mm/day per decade. The enhancement of summer rainfall on the edge of the South Asian monsoon is mainly caused by the rapid warming in spring over the Middle East. The Middle East is one of the most significant regions in terms of global warming, which is found to be 0.5 K per decade during spring. The spring Middle East warming can trigger a decrease in sea level pressure in the region and result in the enhancement of the cross-equatorial winds, which in turn leads to the emergence of low-level jet (LLJ) winds in spring. This process persists until the onset of the summer monsoon, when the LLJ is further strengthens and shifts northward. Since LLJ transport boatloads of moisture into the subcontinent, the northward shift of the LLJ results in the excess supply of moisture to Pakistan and northwestern India. This new research implies that arid and semi-arid countries and regions such as Pakistan and northwestern India, which are on the edge of the monsoon, are now exposed to frequent heavy rainfall events. This will lead to more disasters like the 2022 Pakistan Floods with unacceptably high loss of life and property.

Summer extreme heat events happen frequently in northern China (NC) during recent decades, which have serious impacts on the society and ecosystem. The present study reveals that there is a synergistic effect of the preceding winter positive mid-latitude North Atlantic SST anomaly (pMNA SSTA) and summer negative tropical eastern Indian Ocean SST anomaly (nTEI SSTA) on strengthening the summer extreme heat events in NC. The extreme heat events are stronger and more frequent when the two factors cooccur, and the probability of a strengthened extreme heat events is higher, which indicates a synergistic effect of the two factors. The preceding winter pMNA SSTA and summer nTEI SSTA exert their synergistic effect through a series of atmospheric and snow cover bridges. The preceding winter pMNA SSTA could lead to an anomalous anticyclone over central Asia via the eastward propagating Rossby wave, which decreases snowfall and the subsequent snow cover there. The negative snow cover anomaly may persist into spring and induce a local anomalous anticyclone in summer via the snow-hydrological effect, which is accompanied by an atmospheric teleconnection featured with an anomalous cyclone over West Siberia and an anomalous anticyclone over NC. The summer nTEI SSTA can also induce the anomalous anticyclone there via the Rossby wave propagation. Thus, the two factors exhibit evident synergistic effect on the atmospheric circulation anomaly over NC. The anomalous anticyclone corresponds to the increased atmosphere thickness, which favors the increase of air temperature in NC and the strengthening of extreme heat events. Therefore, the preceding winter pMNA SSTA and summer nTEI SSTA have significant synergistic effect on strengthening the summer extreme heat events in NC.

The purpose of this study is to understand the regime shifts of the East Asian summer monsoon precipitation in different warming periods. We revealed a new precipitation pattern over eastern China as a “+ - + -” precipitation pattern from south to north. The circumglobal teleconnection, the negative phase of the interdecadal Pacific oscillation, the warming over the high-latitude North Atlantic, and the warming over Northern Hemisphere can be used to explain the quadrupole precipitation pattern. These findings could highlight the role of jet streams linking the quadrupole pattern to the teleconnections, dominant decadal ocean variabilities and the warming over Northern Hemisphere. 

Serving as the lifeblood, summer monsoon precipitation fuels agriculture, industry, and daily life in East Asia (EA). Yet, its dark side emerges when severe events batter densely populated regions, causing havoc and disaster. In recent years, South Korea, China, and Japan have all faced the wrath of extreme precipitation events. We zoom into the role of stationary front-induced precipitation, a key character that shapes more than 40% of EA’s summer rainfall narrative. Sifting through observations from 1958 to 2015, we have found a 19.8% surge in the intensity of frontal rainfall. With the Large Ensemble simulations, we have untangled the role of anthropogenic greenhouse gas forcing impact, revealing it as the primary cause behind the amplified EA summer frontal rainfall. Behind this intensification, two culprits emerge: the amplified western North Pacific subtropical high (WNPSH) and increased water vapor convergence. In addition to the enhanced frontal rainfall, the hot extremes have become more severe in the subsequent season. The westward and northward expansion of WNPSH evokes the hot extremes over EA. Our findings suggest that human-induced global warming is radically reshaping the EA summer wet and hot extremes, by sharing a key regional climate mechanism. This transformation is likely to continue, potentially redefining the future of summer extremes in EA.

Monsoon onset signifies the commencement of the rainy season and the reversal of wind circulation over the Asian monsoon area. The factors of the monsoon onset include the thermal condition and arrival of disturbances (e.g. tropical cyclones, Intraseasonal variability). While the prediction of the monsoon onset timing remains a challenging issue, the Cloud system resolving global climate model (CRGCM), which has the advantage of reproducing the tropical disturbance, shows the potential to extend the predictability of the onset timing. Here, we analyze the historical experiment of the Global climate model (MIROC) and CRGCM (NICAM) with prescribed observed SST, especially focusing on the monsoon onset. The results show less reproducibility interannual variability. Of interest is the significant negative interannual correlation between seasonal mean Indian summer monsoon (ISM) strength and the ISM onset timing (summer monsoon tends to be stronger following the early onset) in the GCM and CRGCM while the observational data does not show such significant interannual relation. The ISM system in the model might be mainly driven by the thermal condition in longer persistency. We will discuss the effects of less intraseasonal activity in the model and advance the comprehensive understanding of the combinational effects of different time scale variations such as thermal conditions and disturbances.

Extreme precipitation often dominant around Malaysia during the Northeast Monsoon. However how the extreme precipitation is associated to different monsoonal synoptic circulation is not fully understood. Based on the weather types classification method, this study examines the dominant large-scale patterns related extreme precipitation in Malaysia during Northeast Monsoon season. Daily weather types (WTs) during Northeast Monsoon season (November to February) over Malaysia region (3°S-10°N, 100°–120°E) has been analyzed using 850hPa winds from the Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Data (ERA5) from 1981-2020 by using k-means clustering. Ten WTs were obtained to analyzed in terms of circulation patterns, frequency of occurrence, typical progression and precipitation characteristics and relation to El-Nino Southern Oscillation (ENSO). The resulting WTs represent and include the main synoptic circulation patterns related to the Cold Surge and Borneo Vortex. Cold surge patterns are predominant in January and February while weakening cold surges show significantly reduced frequency during November and December. Three out of ten clusters show Borneo Vortex occurring in different locations over the South China Sea. Most of the WTs remain on the same type during transition and persistence not exceeding 6 days. Clusters that exhibit Borneo Vortex occurrence typically produce heavy rainfall near east coast of Peninsular Malaysia while weakening cold surge shows significant heavy rainfall near south of Borneo. Lastly, the extend to which the El-Nino-Southern Oscillation modulates the probability of occurrence of each of the ten WTs is then discussed.

The Northwest Himalayan (NWH) region has experienced substantial impacts from climate change, necessitating a thorough examination of its dynamic spatiotemporal patterns through suitable statistical methods. To capture systematic trends and elucidate localized characteristics, we employed Rotated Empirical Orthogonal Functions (REOFs) on mean temperature data spanning from 1981 to 2021. Utilizing REOF analysis, we subdivided the NWH region into snow and non-snow cover areas, enhancing the precision of data representation. Particularly in the western parts of Uttarakhand (U.K.) and Himachal Pradesh (H.P.), the second REOF mode of mean temperature exhibited noteworthy fluctuations, explaining 38.7% of the overall variance. However, the associated principal component (PC) failed to accurately depict the data patterns in these regions. In response, we introduced Multi-phase REOF analysis for the first time, aiming to identify PCs that faithfully represent the original data and offer insights into specific temporal variations. Additionally, we explored potential factors contributing to these variances in the specified NWH locations. The deduced physical properties and identified temperature zones from this analysis can enhance climate change modeling in the area.

Clouds are a crucial component of the atmospheric energy system, particularly the radiative balance within, above, and below the troposphere. The vertical distribution of cloud mass and phase determines layer heating rates, the loss of radiation to space, and the amount of radiative heating at the surface. Thus, it is important to know how clouds are distributed both vertically and horizontally at all times of day. Satellite remote sensing is the only approach available to monitor clouds day and night around the globe. In this paper several artificial neural network (ANN) algorithms, employing several Aqua MODIS infrared channels, profiles of relative humidity and temperature from GMAO numerical weather analyses, and the retrieved total cloud visible optical depth, are trained to detect multilayer ice-over-water cloud systems and to retrieve some of their properties as identified by a year of 2008 Aqua MODIS data matched with CloudSat and CALIPSO (CC) cloud profiles. The CC lidar and radar profiles provide the vertical structure that serves as output truth for the multilayer algorithm. The neural networks were trained using one year (2008) of cloud top height data from the CC dataset, with correlation around 0.94 (0.95) and MAE as low as 0.82 (0.81) km for nonpolar regions during the day (night). Applying the trained ANN to independent year 2009 MODIS data resulted in a combined ML and single layer hit rate of 86.4% (85.1%) for nonpolar regions during the day (night). Since the ANN is trained using near-nadir MODIS pixels, infrared radiance corrections were developed as a function of view zenith angle from MODIS and applied to off-nadir pixels when processing MODIS swath data. The multilayer amount derived with the ANN is relatively invariant with increasing view zenith angle compared to the multilayer amount without the corrections.

Mesoscale convective systems (MCSs) are known to induce heavy rainfall and pose significant weather hazards in tropical and extratropical regions. As a subset of MCSs, warm sector rainfalls (WSRs) refer to heavy rainfalls within a weakly forced synoptic environment under the impact of monsoonal airflows. They are often formed in the southern coast of the East Asian monsoon region (e.g. southern China) during the pre-summer season (April-June). Despite extensive studies on the mechanisms of MCS rainfall and the considerable improvement in predictive skills, accurately forecasting MCS-associated WSRs remains challenging. Hong Kong, as a subtropical coastal city in southern China, frequently experiences MCS-associated WSR events. In order to gain a better understanding these events, we analyzed 214 MCSs and 9 WSRs that occurred in the Hong Kong region between April and June from 2005 to 2023. Firstly, we examined the water vapor conditions around Hong Kong using the global navigation satellite system (GNSS) technique. Our findings revealed that during WSRs, the water vapor hovered at a higher level of 62 mm for approximately 36 hours, in contrast to the gradually increasing pattern observed in the other MCSs. Additionally, we investigated three factors - moisture, lift, and instability – related to MCSs using the European Centre for Medium-Range Weather Forecasts reanalysis dataset (ERA5). Our analysis indicated that low moisture deficit lasted for 36/48 hours before the initial/maximum precipitation of WSRs. Furthermore, a stronger convergence in the low atmosphere was observed 36/48 hours before the initial/maximum precipitation of WSRs, compared to the 0/12-hour signal found in other MCSs. In terms of instability, we observed a rapid reduction preceding the maximum precipitation of the MCSs, while the decline shows a smoothing pattern with a small magnitude for the initial time reference. Interestingly, the instability did not display significant fluctuations during WRSs.

Our study looks at the precipitation responses to two possible future emission-mitigation pathways, pushed by the continuing Coronavirus Disease 2019 pandemic (Covid-19) and achieving carbon neutrality in the mid-21st century. We find that simultaneous-reduction in well-mixed greenhouse gases (WMGHGs) and aerosol emissions results in enhanced interhemispheric precipitation contrast in 2040s by amplifying interhemispheric thermal contrast and strengthening meridional overturning circulation at tropics. Reduced aerosol emissions induce generally-increased precipitation in the Northern Hemisphere (NH) and an amplified intertropical rainfall contrast, while reduced WMGHG emissions dominates decrease in precipitation in the areas away from aerosol emission sources. Further, the above precipitation contrast will be enhanced under stronger emission-mitigation pathways, mainly attributed to larger precipitation increases in the NH caused by reduced aerosols. More aggressive WMGHG mitigation policies are required to offset the NH warming driven by aerosols, to avoid the risk of regional drying or wetting driven by the asymmetry in interhemispheric energy budgets.

More than 50% of the global population live in urban areas that contribute to 0.5% of the world’s total land. The resulting concentrated economic and social activities make urban a unique micro-environment that affects air quality and precipitation. Usually, it is more polluted in urban space than its surrounding areas with elevated aerosol levels. The microphysical and radiative effects of aerosols (AMR) would impact cloud and precipitation developments. The direction and magnitude of such urban effect on local precipitation and storms remains an open scientific question. In this study, a fully coupled regional chemistry transport model – the NASA Unified Weather Research & Forecasting (NU-WRF) model – has been applied to the Houston metropolitan area to untangle the complex interactions among urban landscape, aerosols, and precipitation. The simulation results have been evaluated against the measurements of local meteorology and air quality. A series of sensitivity experiments have been conducted to quantify the effect of urban land vs. anthropogenic aerosols on local precipitation onset and intensity. The results show that the urban effects greatly enhanced convection right before rainfall starts via AMR interactions. With the same storm system, aerosols can either suppress or intensify precipitation depending on the underlying land use.

The uncertainty of the cloud condensation nuclei concentration (CCNC) effect on hail precipitation due to perturbed initial meteorological conditions (IC) has been evaluated using an ensemble data from an idealized hailstorm by the high-resolution Weather Research and Forecasting (WRF) simulation. Varied CCNC from clean to polluted condition in six concentrations with uneven intervals resulted in a curve pattern that increasing firstly and then decreasing with single peak for hail precipitation rate. Vertical sensitive simulations showed that CCN at 750-800 hpa plays a dominant role in the change of hail rate, while the total precipitation is dominated by 700-800 hpa height CCNC. However, we found some tiny perturbed ICs from ECMWF, including thermodynamic (TQ, potential temperature and mixed water vapor ratio) and kinematic condition (UV, U wind speed and V wind speed), can even change the curve style with multiple peaks, indicating a potential large uncertainty for CCNC effect on hail in an uncertain IC. Although the meteorological perturbations produce large uncertainties in both hail and total precipitation, varying CCNC by an order of magnitude causes even larger uncertainties than the meteorological perturbations. Changing CCNC modifies the predictability of hail precipitation, with higher predictability in moderately polluted environments compared with very clean and polluted environments. Perturbing the initial meteorological conditions does not qualitatively change how aerosols affect hail and total precipitation.

Balloon sounding with the Compact Optical Backscatter Aerosol Detector (COBALD) and Frost Point hygrometers (FPs) provides in situ data for a better understanding of the vertical distribution of cirrus clouds. In this study, eight summer balloon-borne measurements in Kunming (2012, 2014, 2015, and 2017) and Lhasa (2013, 2016, 2018, and 2020) over the Tibetan Plateau were used to show the distribution characteristics of cirrus clouds. Differences of cirrus occurrence were compared by different indices: the backscatter ratio (BSR) at a 455 nm/940 nm wavelength (BSR455 > 1.2/BSR940 > 2), the color index (CI > 7), and the relative humidity with respect to ice (RHice > 70%). Analysis of the profiles indicated that BSR455 > 1.2 was the optimal criterion to identify the cirrus layer and depict the distribution of the CI and RHice within cirrus clouds. The results showed that the median CI (RHice) within the cirrus clouds at both sites was mostly in the 18–20 (90%–110%) range at pressures below 120 hPa. Furthermore, the balloon-borne measurements combined with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements indicated a high frequency of cirrus occurrence near the tropopause in Kunming and Lhasa. The top height of cirrus occurrence at both sites was above the cold point tropopause and the lapse rate tropopause. Both Kunming and Lhasa had the highest frequency of thin cirrus clouds in the 0–0.4 km vertical cirrus thickness range.

Atmospheric cloud system and the associated precipitation is responsible for variations in how moisture is released and how heat is distributed in Earth's atmosphere. The global distributions of clouds significantly impact on local weather patterns as well as overall climate system. While light rain may not boast high intensity compared to heavy rain, it accounts for significant fraction of rain occurrence and light rain system has recognized as one of the most important atmospheric phenomena to understand global hydrological cycle. The launch of Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) has enabled a nearly-global (65^oS–65^oN) detection of the horizontal and vertical structures of clouds, enabling to understand global precipitation distributions. This research aims to analyze drop size distribution (DSD) characteristics and cloud echo-top height (CETH) defined as the top height of DPR echo from the ground altitude to avoid land elevation interference of light rain using GPM/DPR measurement from 2019 to 2021. In this study, a Gaussian mixture model (GMM) clustering was employed to categorize the light rain system globally. The parameters utilized for the classification in this study are mass-weighted mean diameter (D_m), and normalized intercept parameter (log₁₀N_w) at the clutter-free bottom level, and CETH from GPM/DPR-retrieved products. By using both of microphysical and macrophysical properties of clouds, global light rain was clustered as the following three types: (1) oceanic shallow, (2) oceanic moderate, and (3) continental types. Additionally, to classify prevailing types across different seasons, GMM clustering was conducted seasonally, following the same method. On a global scale, distinct rainfall characteristics are notably disparate between summer and winter, with uniquely classification into two types: oceanic and continental in winter. Henceforth, the DSD parameters will be examined and predominant regions will also be discussed for a comprehensive understanding of the variations in rainfall patterns across seasons.

Using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) model, this study investigates the potential contributions of aerosol direct effect (ADE) and aerosol indirect effect (AIE) on a nocturnal convective precipitation event occurred on 9-10 September 2019 in Beijing. It shows that the ADE and AIE both contribute to the spatial distribution of rainfall in urban areas. The ADE plays a more important role to the changes of heavy precipitation. The ADE causes a slight decrease in precipitation before 13:00 (UTC, hereafter) on 9 September (precipitation I), while increases precipitation during 13:00-21:00 (Precipitation II). The AIE has a weak effect on precipitation before 18:00 on 9 September, while decreases precipitation after 18:00. Further simulation analyses show that the ADE suppresses convection and precipitation by inducing greater surface shortwave radiative cooling and stability during Precipitation I, which reduces the convective energy (CAPE) losses and then increases the later CAPE. The ADE-induced CAPE increase makes the updraft stronger, increasing the cloud fraction and transporting more cloud water into the upper troposphere. This, in turn, causes greater latent heat release from freezing and then stronger convection, resulting in higher rain rate during Precipitation II. During 12:00-18:00, the AIE causes a decrease in CAPE, which weakens the updraft. The AIE decreases the snow mixing ratio above 700 hPa except at 15:00-16:00 on 9 September, which can decrease the release of latent heat from freezing, contributing to the decrease in precipitation after 18:00. Due to the greater stability before 10:00 and longer time required for the updraft to overcome this stability, the ADE necessitates 2-3 hours to adjust the simulated rainfall time series in the study area.

The Silk Road teleconnection is a waveguide teleconnection along the summer subtropical Asian jet along the northern periphery of the Asian summer monsoon anticyclone, also known as the Tibetan/South Asian high. The teleconnection induces undulation of the monsoon anticyclone. It often causes extreme weather events in East Asia such as heat waves and heavy rains. Our analysis of the energetics of the interannual Silk Road teleconnections shows that the baroclinic energy conversion associated with meridional heat transport in the mid- to lower troposphere is key to its maintenance. In a large ensemble AGCM simulation dataset d4PDF and CMIP6 simulations, we find the teleconnection significantly weakens under global warming. This is because baroclinic energy conversion becomes less efficient under reduced linkage with the mid- to lower tropospheric circulation anomalies due to the uplift of the jet axis and enhanced tropospheric stratification. Besides, the southward shift of the Asian jet associated with global warming prevents wave packet injection from the North Atlantic subpolar jet.

The variability and related dynamical mechanisms of the Asian Summer Monsoon Anticyclone (ASMA) were examined on intraseasonal time scale. The intraseasonal behavior of the ASMA is largely related to the convective activity over Indian and East Asian regions reflecting its developing mechanisms. In addition, generation and propagation of Rossby waves over the Eurasian continent also significantly affect the behavior of the ASMA. Particularly, the Rossby wave patten frequently observed over the Eurasian jet (a.k.a. Silk Road Pattern, SRP) significantly influences the intraseasonal variability of the ASMA. The SRP's eastward propagation along the Eurasian jet and dynamical coupling with lower troposphere plays a crucial role in the dynamic variability within the monsoon anticyclone, leading to notable variations in surface weather and chemical transport in the upper troposphere and lower stratosphere across East Asia.

The Aura Microwave Limb Sounder (MLS), launched in July 2004, makes simultaneous co-located measurements of trace gases and cloud ice water content (a proxy for deep convection) in the upper troposphere / lower stratosphere (UTLS) on a daily basis. With its dense spatial and temporal sampling, extensive measurement suite, and insensitivity to aerosol and all but the thickest clouds, Aura MLS is well suited to characterizing UTLS composition in the region of the Asian summer monsoon (ASM) and quantifying the considerable spatial and seasonal variations therein. In addition, the 19-year MLS data record is invaluable for assessing interannual variability in the impact of the ASM on the UTLS. In this talk we will examine MLS measurements of cloud ice and both tropospheric (CO, CH₃Cl, CH₃CN, HCN) and stratospheric (O₃, HNO₃, HCl) tracers, along with meteorological reanalyses, to place the 2017 and 2022 ASM seasons observed in detail by the StratoClim and ACCLIP field campaigns, respectively, into the spatial and temporal context of other monsoons in the last two decades. We will also briefly look at the 2024 ASM season that will just be getting underway.

The Asian summer monsoon Chemical and Climate Impact Project (ACCLIP) used the NASA WB-57f research aircraft, the NSF/NCAR Gulfstream V (GV) research aircraft, the Korean NARA King Air, and a broad set of balloon launches to investigate atmospheric processes that influence ozone depletion and climate in the Korea/Japan region. The NASA WB-57 and NSF GV were flown from Osan Air Base, Republic of Korea during the July-August 2022 period. The circulation of the summertime northern hemisphere upper troposphere – lower stratosphere (UTLS) is dominated by the Asian summer monsoon anticyclone (ASMA or oftentimes called the Tibetan anticyclone). This anticyclonic summer flow develops in response to the southern Asia monsoon and is broadly centered on Tibet. ACCLIP was designed to examine how the ASMA circulation and transport influences the UTLS chemical and aerosol behavior, including links to surface emissions. This presentation will provide background on the ASMA and discuss its wider importance. We will highlight observed dynamical and transport aspects during the summer of 2022, including comparisons to climatological behavior. The ACCLIP mission focused on sampling the ASMA’s eastern flank and outflow into the Pacific Ocean, including mapping the vertical and horizontal structure of UTLS circulation and composition.

The Asian summer monsoon (ASM) has long been known as a weather system, but only recently has its role in atmospheric composition come to be explored in detail. During boreal summer, an anticyclone forms in the upper troposphere and lower stratosphere (UTLS) over Asia which is associated with a pronounced enhancement of chemical and aerosol species lofted from the boundary layer (BL) by ASM deep convection. Transport from the ASM UTLS anticyclone to the global atmosphere was the target of a recent airborne field campaign: The Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP, 2022). In this work, we overview the transport characteristics contributing to ACCLIP airborne sampling with the aid of Lagrangian trajectory modeling. Specifically, we use backward trajectories to connect airborne sampling to deep convection over Asia, and forward trajectories to connect airborne sampling to the stratosphere. Airborne measurements of the tropospheric tracer carbon monoxide (CO) from ACCLIP are used to link model results with the observed UTLS environment. This analysis provides valuable context for the ACCLIP observations, as well as new insight into the role of ASM transport in impacting global atmospheric composition. We show that ACCLIP sampling had convective contributions from both the East and South Asian summer monsoons, with East Asian convection contributing preferentially higher pollutant concentrations. We also show the majority of sampled air masses in the upper troposphere reached the tropopause within the subsequent week.

The Asian summer monsoon (ASM) anticyclone is known as the region with the highest tropopause during the season, ranging from 380-400 K potential temperature on average, in contrast to the tropical tropopause at ~375 K. The ASM anticyclone, therefore, appears to be a large continental-scale convection-driven tropospheric "bubble" over the subtropical background stratosphere. The east-west oscillation of the anticyclone at the top of the convection creates a unique mixing zone of stratospheric and tropospheric air near the eastern edge of the anticyclone, where the ACCLIP campaign conducted research flights. Using the WB-57 measurements of temperature, ozone, water vapor, carbon monoxide, together with the large-scale tropopause analysis, we characterize the mixing zone and its implication on the transport of convectively lofted Asian boundary layer air into the UTLS of greater northern hemisphere.

In the East Asian region, particularly during the winter and spring seasons, the occurrence of secondary ozone peaks in the Upper Troposphere/Lower Stratosphere (UT/LS) at approximately 14 km altitude coincides with active stratosphere-to-troposphere exchange (STE). The significance of these phenomena lies in their contribution to the spatiotemporal variability of total ozone levels, a critical parameter for understanding atmospheric composition and dynamics. The variability in UT/LS ozone emerges as a crucial factor influencing the overall spatiotemporal variation in total ozone. This emphasizes the need to address and account for the complexities arising from vertical ozone distribution when interpreting satellite-retrieved total ozone data. The study focuses on a comprehensive analysis utilizing data from two distinct satellites: the Infrared Atmospheric Sounding Interferometer (IASI), which relies on infrared (IR) channels, and the TROPOspheric Monitoring Instrument (TROPOMI), employing ultraviolet (UV) channels. For the 5-year dataset, we examine the contribution of uncertainties resulting from changes in ozone vertical distribution, with a specific emphasis on UT/LS ozone variability, to the retrieval accuracy of total ozone levels. By comparing the datasets obtained from these two satellite instruments, each utilizing different spectral regions, we aim to enhance our understanding of the challenges associated with satellite-based total ozone retrievals.

The upper troposphere-lower stratosphere (UTLS) ozone distribution is primarily determined by the complex interactions between the troposphere and stratosphere. To fully understand the dynamic and physiochemical processes in the atmospheric layers, comprehensive data representing UTLS ozone variations are essential. To fill this knowledge gap, researchers from the National Center for Atmospheric Research and the National Aeronautics and Space Administration in the United States have collaborated with researchers in Korea and conducted the ACCLIP (Asian Summer Month Chemical and Climate Impact Project) campaign. This study performs numerical simulations for the ACCLIP campaign periods using the Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model. The faithful chemical boundary conditions are vitally important in order to O3 simulations in the UTLS. In our simulation, we employ the Whole Atmosphere Community Climate Model, characterized by 6-hour time steps. This model is selected from a range of global models due to its ability to capture the complex dynamics of the atmosphere across diverse altitudes and regions. Ozonesonde observations were made 38 and 11 times in the Anmyeondo and Osan, respectively, in August 2022. The study aims to assess the feasibility of simulating ozone concentrations in the middle and upper troposphere (3-10 km altitude) using the WRF-CMAQ modeling system by incorporating proper chemical boundary conditions. To simulate ozone distribution in the UTLS, the model top of WRF was extended from 50 hPa to 10 hPa. The number of vertical grids increased. The simulation results were evaluated and showed the reliability of ozone simulations concerning tropospheric altitude. In addition to validation, the principal dynamics and chemical processes influencing UTLS ozone variability were investigated. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2023-00219830).

Located in the junction of land and ocean, coastal areas can be affected by land-based and marine emissions. And atmospheric organic aerosols in coastal cities may have significant regional characteristics. In this study, atmospheric fine particulate matter (PM_2.5) were collected across different seasons at a coastal rural site in Qingdao, China using a high-volume particle sampler, to investigate the chemical and optical characterization of PM_2.5-bound organic fractions (especially water-soluble organic carbon, WSOC) in the coastal suburbs of under the influences of different weather scenarios and air masses. Major PM_2.5 species, molecular composition of water-soluble organic fractions, and primary and secondary organic tracers were analyzed using different types of chromatographs and mass spectrometers. Furthermore, absorption and 3D fluorescence spectra were obtained by advanced spectral analysis instruments. The results showed that, compared to marine air masses influenced samples, WSOC affected by continental air masses exhibited higher light absorption coefficients and generally had a higher degree of aromaticity and unsaturation, as well as contained more molecular formulas of WSOC, especially enriched with sulfur-containing compounds. In contrast, relatively more abundant halogen-containing compounds and higher ship emissions source contributions were identified in the marine air masses influenced samples. Furthermore, the isoprene-sourced SOA contribution from marine emissions was also identified in this study. More detailed discussions will be presented in this talk.

Sea spray aerosol (SSA) represents a major source of aerosol particle populations and significantly impacts the Earth’s radiation budget, cloud formation, and microphysics. It is thus crucial to investigate SSA formation and its atmospheric transformation. Here sea spray aerosol generators were first built and used to study the effects of different organic matters on the production efficiency, size, and morphology of SSA. The molecular weight and concentration of the polymer would affect the production efficiency of SSA. Combining with the measurement of surface tension, we found no clear relationship between surface tension and the yield of SSA, due to the properties of the substances themselves. In addition, the key chemical processes such as the interaction between surface-active molecules, water-soluble organic compounds, and biological macromolecular enzymes at the gas-liquid interface were studied through combination of a Langmuir model and infrared reflection–absorption spectroscopy (IRRAS). We found that the adsorption of glucose and trehalose on the fatty acid monolayer led to the expansion of the mean molecular area. Saccharide–lipid interactions increased with increasing complexity of the saccharide in the order of glucose < trehalose. In a seawater solution, the effects of dissolved saccharides on the ordering and organization of fatty acid chains were muted. The enhancement of the carbonyl band to the low wavenumber region implied that soluble saccharides can form new hydrogen bonds with fatty acid molecules by displacing large amounts of water near the polar headgroups of fatty acids. Our studies provide sufficient insights into formation and transformation of SSA, which would be helpful for improving the accuracy of aerosol emission model parameters.

Recent experiment (He et al, 2021, Science) revealed a vital nucleation process of iodic acid (HIO₃) and iodous acid (HIO₂) under the marine boundary layer conditions. However, HIO₃-HIO₂ nucleation cannot effectively derive the observed rapid new particle formation (NPF) in broad marine regions. Dimethylamine (DMA) is a promising basic precursor to enhance nucleation considering its strong ability to stabilize acidic clusters and the wide distribution in marine atmosphere, while its role in HIO₃-HIO₂ nucleation remains unrevealed. Hence, a method combining quantum chemical calculations and Atmospheric Cluster Dynamics Code (ACDC) simulations was utilized to study the HIO₃-HIO₂-DMA nucleation process. We found that DMA can compete with HIO₂ to accept the proton from HIO₃ as a basic precursor in the most stable configurations of HIO₃-HIO₂-DMA clusters. DMA can significantly enhance the cluster formation rates of HIO₃-HIO₂ kinetically for more than 10³-fold in regions with abundant amine and scarce iodine based on combined factors of high nucleation ability and high concentration of DMA. Furthermore, the iodine oxoacids nucleation enhanced by DMA may explain the sources of rapid NPF events under different conditions corresponding to multiple ocean regions, which can provide important inspirations to understand the frequent and intensive NPF events in broad marine regions.

Bubble bursting on water surfaces is believed to be a main mechanism to produce submicron drops, including sea spray aerosols, which play a critical role in forming cloud and transferring various biological and chemical substances from water to the air. Over the past century, drops production mechanisms from bubble bursting have been extensively studied. They usually involve the centrifugal fragmentation of liquid ligaments from the bubble cap during film rupture, the flapping of the cap film, and the disintegration of Worthington jets after cavity collapse. Here, we show that a major fraction of previously identified as “bubble bursting” submicron drops are in fact generated via a new underwater mechanism, which are not from bursting process at the water surface. This finding may reshapes our understanding of sea spray aerosol production.

As important reservoirs for alkoxy radicals and organic peroxy radicals (ROx), organic peroxides are well-known determinant tracers in ROx chemistry. To evaluate the contribution of these peroxides in chemical processes and aerosol formation, we used quantum chemical modeling to examine the pH effect on the degradation of two selected organic peroxides (methyl hydroperoxide (MHP) and benzoyl peroxide (BZP)) by reaction with dissolved SO₂ (S(IV)). Results showed that due to the presence of the hydroperoxyl group in its structure, MHP preferably forms inorganic sulfate and methyl sulfate in the pH range 1.81 – 6.97, while forming methyl sulfate, exclusively, at pH > 6.97, unlike BZP that exclusively forms benzoyl sulfate in the pH range investigated. The kinetics of these reactions, explored over the ranges pH 1 – 10 and 240 – 340K, indicate that the effective rate constants of the transformation of both organic peroxides exhibit positive pH and temperature dependencies in the pH and temperature ranges investigated. Over this pH range, BZP is more effectively degraded than MHP, with estimated atmospheric lifetimes in the ranges 5.02×10^-19 s – 8.25×10^-3 s for BZP and 1.34×10^-5 s – 5.05×10¹⁰ s for MHP, at 298.15 K. The high reactivity of BZP relative to MHP can be attributed to the activation effect of the benzyl ring on the peroxyl function. Besides the pH effect, this study highlights the role of different substituents on the -O-O- function in altering the kinetics of organic peroxides degradation, and would serve as a ground to evaluate the pH effect in more kinetic analysis of different sources of particulate sulfate. This study further indicates that the aqueous-phase degradation of organic peroxides can adequately drive the change in the chemical composition of dissolved organic matters, both in terms of organic and inorganic sulfate mass fractions.

Mixing of anthropogenic pollutants and biogenic volatile organic compounds impacts the formation of secondary organic aerosols (SOA) to an elusive extent. SO2 is one prevalent anthropogenic pollutant whose role on biogenic SOA formation remains unclear, especially under atmospheric complex pollution conditions. The present study explores factors including relative humidity (RH), NOx and NH3 that may influence the role of SO2 on biogenic SOA formation. Results showed that with a constant SO2 concentration, the increase in RH transformed SO2 sinks from stabilized Criegee intermediates to peroxides in aerosol particles. The associated changes in particle acidity and liquid water content may collectively first lead to decreased and then increased SOA yield with increasing RH. The reaction between peroxides and SO2 under high RH is responsible for the formation of organosulfates, especially highly oxidized organosulfates. When NO2 and SO2 coexisted, aerosol yields were higher than with either pollutant alone. Such a synergistic effect of SO2 and NO2 on aerosol formation is mainly because that the nucleation of sulfuric acid could further promote the gas-particle partition of products from NO3· oxidation and particle-phase reactions, In the coexistence of SO2 and NH3, NH3 weakened the enhancement effect of SO2 on SOA yields. The neutralization of aerosol acidity by NH3 may be the main reason for the antagonistic effect between NH3 and SO2. Our research highlight that it is necessary to comprehensively consider RH and the concentration levels of NOx and NH3 when evaluating effects of SO2-involved anthropogenic-biogenic interactions on SOA formation from the oxidation of biogenic volatile organic compounds.

Ozone (O₃) and sulfur dioxide(SO₂) oxidation chemistry on the organic film and water surface microlayer (SML) generates volatile organic compounds (VOCs) to the atmosphere. To shed light on the proposed significance of this chemistry, we investigated the formation of VOCs through heterogeneous chemistry of O₃ (100 ppb) and SO₂(30 ppb)with authentic SML collected from 10 sites in the South China Sea using a reactor coupled to proton transfer reaction−time of flight−mass spectrometry (PTR−TOF−MS) and a high-resolution quadrupole Orbitrap mass spectrometer(SESI-Orbitrap-MS), subsequently identified by off-line techniques. On the basis of the semiquantitative data of the identified compounds, we estimated the production rates of the key saturated and unsaturated ketones and aldehydes, which correspond to the experimental conditions applied in this study. These results provide a significant update to our understanding of abiotic formation of VOCs in the marine atmosphere, which should be considered in future model studies to properly evaluate the VOC contribution of ozone heterogeneous chemistry with the SML.

Aromatic acids are ubiquitous in seawater and can be transported to the atmosphere via sea spray aerosol (SSA). Despite their importance in affecting the global radiative balance, the contribution of marine aromatic acids and their transport mechanisms through SSA remain unclear. Herein, the distribution of particle size and number concentration of SSA produced in seawater containing nine different aromatic acids (i.e., benzoic acids, benzenedicarboxylic acids, hydroxybenzoic acids, vanillic acid, and syringic acid) was studied using a custom-made SSA simulation chamber; moreover, enrichment of aromatic acids in SSA and their emission flux to the atmosphere were analyzed. Transmission electron microscopy (TEM) images clearly revealed that aromatic acids can be transferred to the nascent SSA. Interestingly, the morphology associated with benzene dicarboxylic acids-coated particles showed that aromatic acids can promote the growth of other surfaces of sea salt, thus making the sea salt core spherical. Aromatic acids showed a significant enrichment behavior at the air-sea interface, which clearly indicated that SSA represent a source of aromatic acids in the atmosphere. Vanillic acid had the largest global emission flux through SSA (962 tons yr-1), even though its concentration in seawater was lower. The calculated results indicated that the global annual flux of aromatic acids was not only affected by the concentration in seawater, but also by their enrichment factor (EF). These data are critical for further quantifying the contribution of organic acids to the atmosphere via SSA, which may provide an estimate of the potential influence of the atmospheric feedbacks to the ocean carbon cycle.

Marine aerosols contribute significantly to atmospheric aerosols, playing substantial roles in influencing the regional and global environment and climate. Marine aerosols over the Eastern China Marginal Seas (ECMS: Bohai Sea-BS, Yellow Sea-YS and East China Sea-ECS) are co-controlled by productive seawater and populated land, but the composition and variation of marine aerosols remain poorly understood. In this study, marine aerosol samples were collected in spring of 2023 covering the entire ECMS to explore the spatial variation and the optical properties of carbonaceous species. Due to significant terrestrial transport, the concentration of TSP and carbonaceous species shows a spatial variation of high in the north and low in the south at latitude. Utilizing satellite data and backward trajectory analysis, the elevated solubility of organic carbon (OC) in southern YS may possibly be influenced by long-range transport and atmospheric aging of terrestrial biomass combustion and marine bioactive gases. Terrestrial transport increases aerosol light absorption, while marine sources and solar radiation weaken it. Humic-like substances are the predominant fluorescent chromophores, and marine sources can regulate the abundance of protein-like substances. The elevated simple forcing efficiency (SFE) and relative radiative forcing (RRF) in the near ultraviolet and visible light range indicate considerably warming effect attributed to terrestrial pollutants. The current results suggest that emphasis should be placed on the effect of terrestrial source transport in BS, while in the relatively open YS and ECS, more attention should be paid to the contribution of marine sources to marginal sea aerosols.

Tiny water drops produced from bubble bursting play a critical role in forming cloud, scattering sunlight, and transporting pathogens from water to the air. Bubbles burst by nucleating a hole at their cap foot and may produce jets, or film drops. The latter originate from the fragmentation of liquid ligaments formed by the centripetal destabilization of opening hole rim. They constitute a major fraction of the aerosols produced from bubbles with cap radius of curvature (R) > ~ 0.4 × capillary length(a). However, our present understanding of the corresponding mechanisms does not explain the production of most submicron film drops, which represent the main number fraction of sea spray aerosols. In this study, we report observations showing that bursting bubbles with R < ~0.4a are actually mainly responsible for submicron film drops production, through a mechanism involving the flapping shear instability of the cap with the outer environment. With this newly proposed pathway, the complex relations between bubble size and number of drops produced per bubble can be better explained, providing a fundamental framework for understanding production flux of aerosols and the transfer of substances mediated by bubble bursting through the air-water interface and the sensitivity of the process to the nature of the environment.

This presentation discusses several conundrums in the current studies of Earth system predictability. This presentation focuses on the potential predictability including its source and limit, instead of the actual predictability (prediction skills). Boundary conditions as the source of climate predictability at the early stage of climate prediction using atmosphere-only models disappear in fully coupled Earth system models (except at the top of the atmosphere). Slowly evolving phenomena, such as the MJO, ENSO, and AMOC, have been cited as the sources of predictability from intraseasonal to decadal timescales. But most of these slowly evolving phenomena are now the targets of prediction and their sources of predictability remain ambiguous. Restrictively speaking, initial conditions are the only sources of predictability for a fully coupled Earth system. If sources of weather predictability come from signals of perturbations in initial conditions, sources of climate predictability come from signals of the mean state in initial conditions, then sources of S2S predictability would be a combination of signals of both perturbations and the mean state in initial conditions. Finally, it is argued that a predictable phenomenon must have a constant feature, which can take various non-static and spatially/temporally varying forms. 

The Met Office along with its partners has recently released a Global Coupled configuration GC5 which will be the baseline for upcoming upgrades to the NWP, seasonal and climate modelling systems. As well as improving most aspects of atmosphere and ocean parametrisation, the development of this configuration made use of an extensive and innovative seamless testing framework to test and improve the model using data assimilation, ensemble, coupled and climate change tests. The configuration provides significant improvements to the southern Ocean, tropical rainfall and several key modes of variability and performance metrics. Additional work to understand the impact of latest development on climate sensitivity and the use of perturbed parameter ensembles will be presented. This configuration is also forming the baseline for the next generation modelling system that will enable better scalability on future infrastructures and preliminary results will be presented.

Climate models are plagued by long-standing biases that degrade predictions. While increasing resolution of global climate models to km scales promises to reduce biases, there is little evidence so far of improvements with currently available computing power. Supermodelling is an alternative approach that has demonstrated reductions in long-standing biases, such as the double ITCZ and tropical SST biases, at a fraction of the computational cost of km scale models. A supermodel is a combination of models that interact during their simulations to mitigate errors before they develop into large-scale biases. Here I will present recent results from a supermodel based on three Earth System Models (NorESM, CESM, MPIESM) trained using observed SST. The models were combined using ocean data assimilation with monthly frequency. The simulation of tropical climate is markedly improved in the supermodel compared to that of the respective standalone models. Seasonal predictions performed with this model are underway and first results will be shown. In addition, I will summarize work on combining atmospheric models that promises to lead to even greater improvements in simulating global climate.

Here we present a new seasonal-to-multiyear earth system prediction system which is based on the Community Earth System Model version 2 (CESM2) in 1° horizontal resolution. A 20- member ensemble of temperature and salinity anomaly assimilation runs serves as the initial condition for 5-year forecasts. Initialized on January 1st of every year, the CESM2 predictions the exhibit only weak climate drift and coupling shocks, allowing us to identify sources of multiyear predictability. To differentiate the effects of external forcing and natural climate variability on longer-term predictability, we analyze anomalies calculated relative to the 50-member ensemble mean of the CESM2 large ensemble. In this presentation we will quantify extent to which marine biogeochemical variables are constrained by physical conditions. This analysis provides crucial insights into error growth of phytoplankton and the resulting limitations for multiyear predictability.

With the emergence of predictive skills of climate variability, we currently stand at the horizon of being able to forecast marine ecosystems. However, the assessment of the capacity to predict high trophic levels of marine ecosystems has been rarely investigated. Here, we investigate predictability of fish biomass integrated by spatially explicit mechanistic model of fish with three functional types. We also evaluate the bottom-up biophysical drivers of fish that are predicted by decadal prediction outcomes from an Earth system model, initialized through forced ocean-sea ice simulations. We found that temperature drivers have longer predictability ~8 years while biological drivers have predictabilities within 1-2 years. The fish biomasses have skillful predictability within 2 years globally, extended more years regionally. These findings build upon prior research in evaluating the predictability of bottom-up drivers on fish, potentially illuminating a path towards operating forecast systems of living marine resources.

The Kuroshio Extension (KE) has far-reaching influences on climate as well as on local marine ecosystems. Thus, skillful multi-year to decadal prediction of the KE state and understanding sources of skill are valuable. Retrospective forecasts using the high-resolution Community Earth System Model (CESM) show exceptional skill in predicting KE variability up to lead year 4, substantially higher than the skill found in a similarly configured low-resolution CESM. The higher skill is attained because the high-resolution system can more realistically simulate the westward Rossby wave propagation of initialized ocean anomalies in the central North Pacific and their expression within the sharp KE front, and does not suffer from spurious variability near Japan present in the low- resolution CESM that interferes with the incoming wave propagation. These results argue for the use of high-resolution models for future studies that aim to predict changes in western boundary current systems and associated biological fields.

Climate extremes, such as heat waves, heavy precipitation, intense storms, droughts, and wildfires, have become more frequent and severe in recent years as a consequence of human-induced climate change. Estimating the predictability and improving prediction of the frequency, duration, and intensity of these extremes on seasonal to multi-year timescales are crucial for proactive planning and adaptation. However, climate prediction at regional scales remains challenging due to the complexity of the climate system and limitations in model accuracy. Here we use a large ensemble of simulations, assimilations, and reforecasts using Community Earth System Model version 2 (CESM2) to assess the predictability of statistics of climate extremes with lead times of up to 5 years. We show that the frequency and duration of heat waves during local summer in specific regions are predictable up to several months to years. Sources of long-term predictability include not only external forcings but also modes of climate variability across time scales such as El Niño and Southern Oscillation, Pacific Decadal Variability, and Atlantic Multidecadal Variability. This study implies opportunities to deepen our scientific understanding of sources for long-term prediction of statistics of climate extremes and the potential for the associated disaster management.

Accurately projecting the impact of the North Atlantic subpolar cooling region despite global warming, known as 'warming hole', on global climate is critical but uncertain due to its complex mechanisms. Here, we use the latest multi-model simulations (CMIP6) to investigate the impact of changes in the warming hole on future climate change. We find that changes in the warming hole play an important role in creating an asymmetry in temperature and precipitation patterns between the hemispheres: the weaker the warming hole (corresponding to strong warming in the North Atlantic subpolar region), the greater non-radiative fluxes (sensible and latent heat flux) transported to the atmosphere, leading to greater warming in the Northern Hemisphere than the Southern Hemisphere. This difference in warming leads to an energy imbalance between the hemispheres, resulting in a northward shift of the Inter-Tropical Convergence Zone (ITCZ). These changes have a significant impact on the distribution of precipitation, contributing to the precipitation imbalance between the two hemispheres. Our findings highlight that a better understanding of the warming hole is important for accurate projections of future global climate, especially hemispheric temperature and precipitation patterns.

Decadal-scale periods of global warming slowdown and acceleration are associated with varying phases of the Pacific Decadal Oscillation (PDO). However, the footprint of the PDO on land temperature has not been determined and how this footprint has changed over time remains unknown. Here we use an observational dataset of global land temperature since 1901 to search the footprint of the PDO and its influence on the variability of climate warming rates. We find that a warming acceleration associated with the transition of PDO from negative to positive phase has remained high and has been expanding in area since the early 20^th century. Meanwhile, the warming slowdown associated with the reverse transition appears to be shrinking worldwide. This configuration implies that in the coming decade with a likely occurrence of an ascending transition of the PDO, a period of faster anthropogenic global warming could be anticipated.

Fog frequently forms in high-elevation complex terrain as over water bodies but is less understood and difficult to predict. Cold fog forms via various thermodynamic, dynamic, and microphysical processes when the air temperature is less than 0°C. It occurs frequently during the cold season in the western United States yet is challenging to detect using standard observations and very difficult to predict. The Cold Fog Amongst Complex Terrain (CFACT) project goals are to 1) investigate the lifecycle of cold-fog events over complex terrain with the latest observation technology, 2) improve microphysical parameterizations and visibility algorithms used in numerical weather prediction (NWP) models, and 3) develop data assimilation and analysis methods for current and next-generation (e.g., sub-kilometer scale) NWP models. The CFACT field campaign took place in Heber Valley, Utah, during January and February 2022, with support from NSF’s Lower Atmospheric Observing Facilities (managed by NCAR’s Earth Observing Laboratory), the University of Utah, and Ontario Technical University. A network of ground-based and aerial in situ instruments and remote sensing platforms were used to obtain comprehensive measurements of thermodynamic profiles, cloud microphysics, aerosol properties, and environmental dynamics. Nine intensive observation periods (IOPs) explored various mountainous weather and cold-fog conditions. Field observations, NWP forecasts, and large-eddy simulations provided unprecedented data sources to help understand the mechanisms associated with cold-fog weather and to identify and mitigate numerical model deficiencies in simulating winter weather over mountainous terrain. This presentation summarizes the CFACT field campaign, its observations, and recent progress in science data analysis, fog process studies, numerical simulations, and model validation. [References: Pu, Z., E. Pardyjak, S. Hoch, I. Gultepe, A. G. Hallar, A. Perelet, R. Beal, G. Carrillo-Cardenas, X. Li, M. Garcia, S. Oncley, W. Brown, J. Anderson, J. Witte, A. Vakhtin, 2023: Cold Fog Amongst Complex Terrain. Bulletin of the American Meteorological Society. 104, E2030-E2052. https://doi.org/10.1175/BAMS-D-22-0030.1].

Boundary layer turbulence, crucial for vertical energy and moisture transport, can be influenced by coherent horizontal flows driven by diurnal heating difference in land surfaces. This study employs Large Eddy Simulations of the vector vorticity equation cloud-resolving model (VVM) coupled with the Noah Land Surface Model to explore these processes. Three land type configurations are examined: a combination of half grassland and half urban land (HALF), entire grassland (GRASS), and entire urban land (URBAN). All experiments adopt a 12.8 12.8 km² horizontal domain with 100 m grid spacing and 20 m vertical resolution. The initial condition reflects a stable, windless environment with convective available potential energy close to zero, sourced from a 06 LST radiosonde observation over the western plain of Taiwan during springtime. As the simulated solar insolation varies from 06 to 14LST, the developed circulation features a near-surface horizontal flow from grassland to urban reaching 2 m/s at noon and a weaker but thicker upper-layer return flow from urban to grassland. The urban patch exhibits an overall updraft with the maximum of 0.3 m/s. Both urban and the grass regions in HALF exhibit weaker vertical turbulent kinetic energy compared to URBAN and GRASS. The boundary layer over the grassland patch in HALF is more stable, with a significant transition zone at the boundary layer top. In HALF, the latent heat flux is predominantly contributed by the grassland patch, exceeding that in GRASS by 9% at noon. The column water vapor (CWV) over the grassland patch in HALF remains constant over time, while the CWV over the urban patch increases. As the induced circulation crossing different land types in HALF potentially plays a dominant role in energy transport, future investigations will focus on its influence on local stability, entrainment at boundary layer top and local surface fluxes.

To understand the difference in the amount of water vapor transported to Yilan under the influence of the northeast monsoon, this study used the WRF model (Weather Research and Forecasting Model, WRF) to simulate water vapor transported by different types of cold high pressure with the highest spatial resolution of 500 m. Calculate the total amount of water vapor and water vapor flux in the Lanyang Plain, and analyze the potential precipitation trend in this area. The research results found that on November 26, 2021, and October 25, 2022, the cold high pressure was at a higher latitude (40 ^。 N), and the low-level water vapor content in the Yilan was higher; on December 6, 2023, and December 7, 2023, the cold high pressure was at a lower latitude (30 ^。 N), and the low-level moisture is lower. Such a result may be related to the strength of the cold air mass, the path and time of the cold air mass passing through the ocean, and the physical mechanism of cold air mass transformation in the ocean boundary layer.

The northeast Taiwan region, particularly Yilan, exhibits distinct spatial rainfall patterns during wintertime northeasterly conditions. These patterns are intricately connected to the interaction between large-scale background flow and complex topography. Kabasawa (1950) initially described the characteristic winter rainfall in Yilan, noting a decrease in precipitation from the coast towards the southwestern mountainous region. Su et al. (2022) further developed a conceptual model during the YESR2020 campaign, elucidating the interplay between local circulation and rainfall patterns influenced by northeasterly winds and complex terrain. This model contrasts with Kabasawa's (1950) findings, revealing a decline in rainfall distribution from the southwestern mountains towards the plains. Spatial rainfall characteristics are intricately linked to variations in large-scale background wind features and moisture flux distribution. Over the past 40 years, an analysis of Yi-Lan's rainfall unveiled terrain-locked precipitation patterns, emphasizing the ERA5 reanalysis data to assess long-term changes in wind features and moisture distributions. Notably, rainfall hotspots were identified on the windward side of prevailing northeasterlies in the mountainous region, decreasing towards the plains. Average rainfall exhibited an upward trend, particularly pronounced in the southern mountain region. High-resolution cloud model experiments illustrated that alterations in large-scale background wind features influence local circulation patterns over terrain, impacting convective initiation locations. This characteristic pattern was consistently observed through cluster analysis of historical rainfall data. The southern mountains experienced higher moisture flux and more easterly wind directions during precipitation hotspots. In essence, understanding the relationship between background winds and rainfall aids in projecting future changes in precipitation characteristics in Yilan under climate change scenarios.

During the winter Northeast Monsoon season, northern Taiwan experiences recurrent episodes of heavy rainfall. Especially in the southern Yilan, due to the unique interaction between its distinctive topography and the Northeast Monsoon, resulting in prolonged and intense rainfall. Notably, this region broke Taiwan's annual rainfall record in 2022. However, the intricacies of the interaction between the Northeast Monsoon and local topography lack clarity, primarily due to a dearth of vertical observational data. To bridge this knowledge gap, the Yilan Experiment of Severe Rainfall was executed in both 2021 and 2022 (YESR2021, YESR2022), providing comprehensive three-dimensional datasets. Utilizing two decades of meteorological station and reanalysis data, this study classifies wind directions in the Yilan region under varying environmental wind conditions. Results indicate a prevailing westward surface wind component during periods dominated by the Northeast Monsoon, with a maximum of 76.5% occurring at ambient wind directions between 50-70 degrees. This aligns with the periods of maximum rainfall duration and intensity. Moreover, the maximum frequency of intense rainfall in the plains and coastal mountain areas occurs under different angles of the environmental wind, indicating distinct mechanisms for heavy rainfall in these regions. Additionally, this study is going to incorporate high-temporal and spatial resolution unmanned aerial vehicle (UAV) vertical observations from YESR2021 and YESR2022. The aim is to calculate moist Froude number and moist static energy, providing insights into the convection location and intensity. By integrating stability analysis with ground station results, a comprehensive exploration of the favorable conditions for rainfall in the Yilan will be conducted. 

Due to the advances in the atmospheric boundary layer observation technique, many high spatial- and temporal-resolution instruments are deployed to investigate turbulence structures. This study conducts observation and model simulation to investigate the planetary boundary layer structures. The radial wind profiler, Doppler wind lidar, microwave radiometer profiler, and radiosonde were used to study the PBL mean and turbulent fields. We performed high-resolution WRF simulations with various types of PBL schemes to assess the impact of the PBL parameterizations on the meteorological fields. When model resolution approaches the scales of the most energy-containing turbulence, the gray-zone problem occurs. This study characterized the turbulence kinetic energy based on the observation and model simulation and assessed the model performance against the observation data. Details will be discussed during the conference.

An oil burn experiment was conducted at the University of Alaska Fairbanks’ Poker Flat Research Range on a subset of a 1 ha artificial pond during August 2-3, 2022. The downwind PM 2.5 concentrations were measured with unmanned aircraft systems (UAS) flown by the U.S. Geological Survey carrying the U.S. Environmental Protection Agency’s (EPA) “Kolibri” sampler. The near-field wind velocity, temperature, and relative humidity were collected using radiosonde. In this study, we used the Weather Research and Forecasting (WRF) model to provide meteorological fields in a sub-kilometer spatial resolution for running NOAA’s dispersion model, HYSPLIT, to simulate the transport and dispersion of one of the oil burn experiments. One of the sources of uncertainty in dispersion modeling is the accuracy of the meteorological input used to drive the model. Potential errors in the simulated wind fields may cause the modeled plume to move in the wrong direction, while the uncertainty of stability and turbulent parameters may result in an inaccurate mixing in the dispersion simulation. We assimilated the radiosonde data in WRF simulations through observational nudging to improve the meteorological fields that later were used to drive HYSPLIT dispersion modeling. There were seven burns in this experiment. Burn 3 was conducted on August 3, 2022, starting at 1711 UTC. The observational nudging with the wind profiles collected by the radiosonde successfully reduced the bias in wind speed and direction prediction. The nudged simulation still underpredicted the wind speed, but its variation along with altitude was much improved by the observational nudging using the radiosonde data. We compared the transport pattern from HYSPLIT results driven by two sets of WRF meteorological data (non-nudged and nudged). When using the WRF wind fields corrected by the wind measurements, the modeled plume moved in a direction more consistent with the UAS measurement.

The seasonal extremes of Indian Summer Monsoon Rainfall (ISMR) - floods and droughts – are often thought to be triggered by external forcing such as the El Niño Southern Oscillation. Specifically, La Niña (El Niño) conditions are believed to modulate monsoon circulation leading to seasonally anomalous excess (deficit) rain over India. However, historical records suggest that nearly 40-50 % of these seasonal extremes (floods or droughts) over India have occurred when the equatorial Pacific sea surface temperatures were near neutral. Here, we focus specifically on monsoon floods and highlight the differences in rainfall evolution during (i) flood and normal rainfall years, and (ii) floods associated with La Niña and No La Niña conditions. Our analysis suggests that floods appear to be mainly because of increased rainfall either at the beginning and/or end of the season. In particular, we show that during Non La Niña floods, there is a near-doubling of rainfall during late-August/early-September. More importantly, we show using reanalysis that this abrupt increase in rainfall accumulation can be related to an interplay between atmospheric conditions in the northern and southern midlatitudes. Specifically, our analysis suggests that a wave train curving in from northern mid-latitudes towards equator, coupled with a blocking high centred around (60N, 15E) and an anomalously strong low level jet, in late August may have led to the observed heavy rain over India. Equally importantly, a large pressure gradient between India and Madagascar over a 20-day period in late-August appears to be a critical driver for strengthening of the low-level jet.

Arctic sea-ice decline has been accelerating under greenhouse warming. We investigate the tropical precipitation extreme events response to declining Arctic sea-ice using observations and climate model simulations. Idealized warming experiments in which Arctic becomes ice free under greenhouse warming show robust response on the tropical precipitation extreme events. We find enhancement of mean tropical precipitation and increasing propensity of extreme precipitation events over South Asian region. The enhanced Arctic sea-ice melt increases the mid-latitude waviness transcending energy further southwards into the tropics. This in addition to northward shift of intertropical convergence zone enhances tropical precipitation. The enhanced energy in the tropics along with the anomalous mid-latitude intrusions provide a conducive environment for moisture convergence and intense summer monsoon precipitation events over South Asia. Our findings suggest that the intense precipitation events over the South Asian Summer monsoon region are projected to increase as the Arctic continues to warm more quickly than the rest of the planet under greenhouse warming.

On August 28 2023, heavy rainfall of 102 mm/day occurred over West Sumatra in Indonesia and caused flooding and landslides in the vicinity. To improve the NWP simulation of this rainfall event, weather radar data assimilation is one way to improve the initial atmospheric data conditions. This research aims to investigate the effect of Doppler weather radar data assimilation on the Weather Research Forecasting (WRF) numerical model and compare between three-dimensional and four-dimensional variations data assimilation methods (3DVar and 4DVar). Three simulations were conducted using the WRF model, i.e. without data assimilation, with 3DVar and 4DVar data assimilation. The simulation was then compared with surface rainfall observation data and Quantitative Precipitation Estimation (QPE) from Doppler weather radar data. The results indicate improvements in the numerical model using assimilated data.

Fundamental modes of tropical variability consist of convectively coupled equatorial waves (CCEWs). The understanding and simulations of CCEWs remained a significant issue in weather and climate modelling. Using 20 years of IITM GFS T1534 (~12.5 km resolution) hindcast data from 2000 to 2019, the climatology of CCEWs and the Madden-Julian Oscillation (MJO) in particular are analyzed for the first time in the present study. We evaluated the high resolution 12km GFS model prediction of tropical variability with respect to observations like IMERG rainfall and NOAA OLR data. The results shows that the features are well simulated up to day 3 forecast. However, there are certain biases noted in the simulation of tropical waves which need further improvement. The main characteristics of the climatological equatorial rainfall through it’s space–time spectra are accurately portrayed, and the model's performance is assessed by analyzing the rainfall's diurnal phase. These long-term results can serve as benchmarks to enhance the model physics for predicting such wave patterns and further improvement.

In recent years, accurate monitoring of precipitation has become increasingly important in Japan, as heavy rainfall disasters have been becoming more frequent due to global warming. As a current rainfall monitoring system, tipping bucket rain gauges are widely used throughout Japan, but the spatial resolution are too large to capture rainfall pattern precisely. Therefore, there is a rising demand for weather radar that can observe precipitation intensity at a finer spatial resolution. Presently, Multi-Parameter Radar (MP Radar) is used as a radar-based rainfall observation system. This radar can estimate real-time raindrop particle size distribution and utilize them for rainfall intensity calculations. However, the accuracy of the estimated raindrop particle size distribution has not been sufficiently validated. In this study, we aim to validate the accuracy of the raindrop particle size distribution estimated by the MP Radar. To achieve this, we modified the Image Disdrometer developed by Onomura et al. (2019) to create an instrument capable of measuring raindrop size distributions at high altitudes. Specifically, we miniaturized the conventional Image Disdrometer to less than 1/4 of its original size and mounted it on a drone. Before conducting actual observations by flying the drone over a target region of MP Rader, we hovered it at a few meters above the ground to verify the accuracy of the raindrop particle size distribution observed by a conventional laser disdrometer and the UAV-mounted observation instrument. The results of this observation will be presented at AOGS2024.

The Orbiting Carbon Observatory-2 and the Orbiting Carbon Observatory-3 are NASA's first two missions designed specifically for the space-based measurement of carbon dioxide. OCO-2 was launched in 2014 and, as part of NASA’s “A Train”, has been making continuous global measurements of dry-air column CO₂ (XCO₂) and solar-induced fluorescence (SIF) from a sun-synchronous, low-earth orbit with a fixed 1336h equator crossing time. In 2019 OCO-3, the spare instrument copy of OCO-2, was installed on the International Space Station (ISS). Like OCO-2, OCO-3 also measures XCO₂ and SIF but does so at all times between dawn and dusk, without a fixed repeat cycle and limited to ±52º latitude due to the inclined orbit of the ISS. Both instruments perform observations in nadir, ocean glint, and target geometry, with an extra mode added to OCO-3 that is dedicated to map urban areas. This presentation will give an overview of the two missions, highlight some of the challenges of CO₂ monitoring from space, and provide a summary of the contributions OCO-2&3 observations have made to advance our understanding of carbon cycle science on global and local scales.

Despite inventory-based, or “bottom-up”, estimates of fossil fuel-CO₂ (CO₂ff) emissions likely being accurate to within 10% at annual, national scales for most developed countries, uncertainties can be much larger at urban scales. Top-down approaches can also provide additional information such as the seasonality of emissions and can be closer to real time than most bottom-up approaches. We will present CO₂ff emissions estimates based on atmospheric radiocarbon (¹⁴C) and CO₂ measurements for the U.S., with a focus on Los Angeles. Whether from ground-based in situ sensors, or via ground-based or satellite remote sensing, measurements of CO₂ concentrations alone are not sufficient to determine fossil emissions. This is because terrestrial ecosystem CO₂ exchange can influence, and even dominate, spatio-temporal gradients of CO₂, confounding the interpretation of CO₂. The rarest isotope of C, ¹⁴C, is completely absent from fossil fuels allowing us to use precise measurements of atmospheric CO₂ and its ¹⁴C:C ratio to separate the fossil and biospheric contributions to observed gradients. We will present estimates of CO₂ff emissions for Los Angeles for 2015, August 2021 and summer 2023 based on regular monitoring and intensive measurement campaigns. These top-down results will be compared to bottom-up emissions estimates from Vulcan and the GRAAPES emissions data products. We will also present a summary of results from other urban areas in the United States and elsewhere highlighting the significance of the urban biosphere. Finally, we will demonstrate the application of measurements of nitrogen oxides and carbon monoxide to improve the spatial and temporal disaggregation of total CO₂ into its fossil and biospheric fractions.

Urban areas are responsible for the vast majority of fossil fuel CO₂ emissions, thus mitigation actions are often taken at the city level. Detailed information about urban emissions and offsetting potential is needed to guide mitigation actions and to evaluate the efficacy of these actions. CarbonWatch-Urban is a multi-tiered approach to provide fine-scale CO₂ emissions and sink information for all of New Zealand’s urban areas. First, we have established a high resolution (street segments and buildings, hourly) bottom-up inventory of Auckland's fossil fuel CO₂ emissions from a variety of data sources and optimised the UrbanVPRM land surface model to estimate the biogenic CO₂ budget of the Auckland region. We use in situ and flask observations of CO₂, ¹⁴CO₂, CO, COS and black carbon to separate and quantify the various components of Auckland’s CO₂ flux, and an atmospheric inversion framework to rigorously validate and improve the bottom-up flux estimates. Yet the majority of New Zealand’s urban CO₂ emissions are from smaller towns and cities, and simply extending the Auckland observation framework across the country is not feasible due to topography, urban form and cost constraints. Therefore use Auckland experience to expand the bottom-up flux modelling framework nationally, applying the improvements identified in Auckland. We use a less rigorous but realistic campaign-style flask observations system from a range of towns and cities spanning New Zealand’s climate, topography and urban from to validate and improve the flux models and provide the best estimates of New Zealand’s urban emissions, with granular information in space and time. 

Cities are the major source of greenhouse gases and thus target for emission reduction. Recent reports on atmospheric measurements of methane and its fluxes in urban areas suggest that substantial emissions occur in many cities, and emission inventories are highly uncertain for urban sources such as energy and waste sectors. Tokyo is Japan’s and world’s largest megacity and urgently needs investigation of accurate greenhouse gas emissions to support mitigation actions. We developed a vehicle-based mobile measurement system with a mid-infrared absorption spectrometer for methane and ethane (MIRA ULTRA, Aeris Technologies). Ethane helps us identify leakage of natural gas since its representative methane/ethane composition is known. In September–October 2023, we conducted 3-week mobile measurements in Tokyo and surrounding areas with total driving distance of about 2400 km. About 1000 points with high methane elevations were identified, and ethane/methane enhancement ratio calculated for individual points were used to group them into biogenic, fossil fuel and combustion sources. The ethane-methane classification indicated that major contributions to Tokyo’s methane emissions come from biogenic and fossil fuel sources, while combustion sources are relatively minor. Strong biogenic methane peaks were found around landfill sites and wastewater treatment plants, and fossil fuel peaks were found frequently in residential and industry areas. Presentation will include discussion on emission magnitudes.

Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO₂) and radiation components remain uncertain. In this study, the co-variation of dust and CO₂ concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on March 15 and 28, 2021. During these events, hourly CO₂ concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m⁻³. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s⁻¹ and relative humidity decreased by 50–60%. The CO₂ variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO₂ level at SDZ. Biogenic CO₂ flux plays an indistinctive role on the large CO₂ variation at SDZ as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO₂ air from Northwest China, leading to the declines in CO₂. These findings demonstrate that meso-scale synoptic conditions significantly affect the regional transport and dispersion of CO₂, which can influence the prediction of terrestrial carbon balance on a regional scale.

The dynamics of atmospheric CO₂ concentrations in urban agglomerations have been a topic of interest in research on global climate change, yet there remain significant uncertainties within the estimates of CO₂ contributions from biogenic and anthropogenic sources. In this study, the Weather Research and Forecasting model coupled with the Vegetation Photosynthetic Respiration Model (WRF-VPRM) was implemented with local VPRM parameters to simulate the atmospheric CO₂ concentration in the Pearl River Delta (PRD) region of China during 2019–2021. The results show that (1) WRF-VPRM accurately simulates the distribution of the atmospheric CO2 concentration in the PRD region from 27-km grids to 4-km grids, with the hourly average bias of 4-km grids being −1.27 ppm. The accuracy of CO₂ simulations is significantly impacted by solar radiation, while ocean winds increase the CO₂ simulation bias in coastal areas. The increase in motor carbon emissions during evening rush hours also affects the CO₂ simulation accuracy in urban areas. (2) In spring, summer, autumn, and winter, the average CO₂ concentrations in the PRD region were 431.56±3.01 ppm, 427.55±3.51 ppm, 426.20±3.89 ppm, and 434.27±3.82 ppm, respectively. Anthropogenic emissions were the main factor, accounting for 20.59% of the total CO₂ concentration in the region; by contrast, the contribution from vegetation emissions was only 1.57%. (3) High CO₂ concentration centers (CO₂ exceed 436 ppm) occur throughout the year at the border of Yunfu and Zhaoqing, northeast Qingyuan, and southern Huizhou. CO₂ concentrations were below 426 ppm in the region around the Pearl River Estuary in summer and fall.

Northeast part of India including West Bengal (henceforth referred as NEI) and Bangladesh is facing the impacts of changing climate. A recent study revealed some interesting and crucial findings using a multi-model multi-scenario analysis on the complexity of future precipitation and temperature dynamics across NEI, including Bangladesh (Paul and Maity, 2023). Under the SSP585 scenario, the Simple Daily Intensity Index (SDII) is projected to increase by 2.4 mm/day over the last three decades of the present century (2071-2100) compared to the base value of 14.4 mm/day over 1981-2014. The analysis also reports significant changes with respect to Consecutive Dry Days (CDD), Consecutive Wet Days (CWD), Number of Summer Days Index (SU) and Warm Spell Days Index (WSDI). The maximum and minimum temperatures are expected to increase by 4.0˚C and 5.5˚C, respectively, with a higher rate of rise in minimum temperature, leading to a decrease in the Diurnal Temperature Range (DTR) by up to 1.5˚C. Other studies involving the entire Indian mainland established the most of the NEI as ‘Climate change Hotspots’ (Sarkar and Maity, 2024), with particularly pronounced exposure in multi-attributed precipitation and temperature characteristics over future than rest of the country. Further analysis including two crucial socioeconomic factors, namely, population density and Human Development Index (HDI) reveals most part of NEI to be ‘vulnerable’ to changing climate as well (Sarkar and Maity, 2024). We expect these findings to be helpful in climate-informed planning and management of multiple sectors such as, water resources, agriculture, infrastructure etc. across NEI and Bangladesh.

Snow-covered regions span over large portions of the Northern Hemisphere and polar regions. Also, alpine regions in high mountain ranges are often covered by snow. The snow cover of High-Mountain Asia (HMA) from the Hindu Kush Himalayan is undergoing an unprecedented and mostly irreversible state due to global warming. These changes pose a significant threat to two billion people and are exacerbating the risk of species extinction. Recent results indicate an overall decline in snow cover over the HMA. Our preliminary findings highlight the declining trend of snow persistence. Using current reanalysis data, satellite observations, and state-of-the-are climate simulations for the climate model intercomparison project phase 6 (CMIP6), we derived a significant decline in mountain snow persistence and its sensitivity to temperature changes. In our analysis, we identified global warming as the predominant factor contributing to the loss of snow, as opposed to regional warming. We investigated the stochastic link between the variability of the South Asian monsoon circulation and the snow cover in the HMA region. The results indicate a strong sensitivity of snow persistency and depth to regional and global warming and to the changes in accumulation rates determined by the differing strength of the monsoon systems. Further, we highlight the dependence of the results on the spatial resolution of the dataset.

In this work, we investigated the impact of the interannual variation in Tibetan Plateau snow cover (TPSC) on the summer heat wave frequency (HWF) over the Indochina Peninsula (ICP) (HWF_ICP) for 1981-2020. This study shows that the spring TPSC variation over the central TP is positively correlated with the summer HWF_ICP and can explain up to 30% of the summer HWF_ICP variance. Analysis of the apparent heat source shows that more-than-normal spring snow cover over the central TP has a cooling effect on the above atmosphere, which induces negative geopotential height anomalies in the upper troposphere. The anomalous spring TPSC and its cooling effect can persist until summer. In summer, the TPSC-associated low anomalies propagate eastward to the East Asia-Japan Sea area. The anomalous westerly winds along the south flank of these low anomalies strengthen the climatological westerly jet. Changes in the westerlies are accompanied by anomalous ascending and descending air motion north and south of 30°N over the coast of East Asia and the western Pacific. The anomalous descending air motion south of 30°N causes an enhanced subtropical high, less cloud cover, and more downward solar radiation, which are favorable conditions for a greater occurrence of heat waves over the ICP. The results of current study may provide useful information for improving the seasonal forecast skill of the summer HWF_ICP variation.

The influence of winter-spring eastern Tibetan Plateau snow anomalies on the East Asian summer monsoon has been the focus of previous studies. The present study documents the impacts of boreal summer western and southern Tibetan Plateau snow cover anomalies on summer rainfall over East Asia and the relationship between the eastern Tibetan Plateau snow cover and the North American air temperature in spring. Analysis shows that more snow cover in the western and southern Tibetan Plateau induces anomalous cooling in the overlying atmospheric column. The induced atmospheric circulation changes are different corresponding to more snow cover in the western and southern Tibetan Plateau. The atmospheric circulation changes accompanying the western Plateau snow cover anomalies are more obvious over the midlatitude Asia, whereas those corresponding to the southern Plateau snow cover anomalies are more prominent over the tropics. As such, the western and southern Tibetan Plateau snow cover anomalies influence the East Asian summer circulation and precipitation through different pathways. Moreover, a stable relationship is identified between the eastern Tibetan Plateau snow cover and the North American SAT in spring before the mid-2000s. Positive snow-cover anomalies over the eastern Tibetan Plateau induce cooling in the local atmospheric column. The atmospheric cooling stimulates a large-scale atmospheric wave pattern at the upper level that extends northeastward from the eastern Tibetan Plateau via northeast Asia and the North Pacific to North America. An anomalous high forms over North America, accompanied by anomalous descent. In the northwestern part, the horizontal advection by anomalous southerly winds along the west flank of anomalous anticyclone induces SAT increase. In the central part, the enhanced surface sensible heat flux following anomalous descent-induced downward shortwave radiation increase leads to SAT increase.

East Asian monsoon (EAM) exists substantial changes during April-July. As part of the EAM, the first distinct rainy period in Taiwan known as Meiyu occurs from pentad 27 (May 11-15) to pentad 36 (June 25-29). It makes up about one third of the annual total rainfall. The Meiyu is sensitive to a narrow rainband oriented in southwest-northeast direction from northeast Vietnam through Taiwan to Okinawa. In this study we aim to document the relationship between Taiwan Meiyu and the progression of the daily weather types and tropical cyclones (TC) in the EAM region (0°-37.5°N, 90°E-140°E). The progression is characterized by a monsoon weather calendar built on the wind patterns of 44 years (1979-2022) of April-July daily 850-hPa winds using the self-organizing map (SOM) method. The daily wind patterns are classified into 9 units of a 3×3 SOM. The occurrence frequency of each unit on the pentad basis suggests that the period of April-July can be separated to late spring (pentad 19-24), transition (pentad 25-33), and early summer (pentad 34-42) monsoon evolution stages. The monsoon weather calendar reveals the relationship between Taiwan Meiyu, TC and EAM. After separating the years to the groups with and without TCs, we found that over the SCS and the southern Philippine Sea the westerly flow is stronger and rainfall amount is larger during the years with TCs, whereas stronger northeasterly wind and less rainfall are observed over Taiwan. The results suggest that the monsoon weather calendar can be useful for identifying the unusual years of which the progression of the daily weather types is distinctly different from others and the extreme/rare events are more likely to occur.

Recent years have witnessed contrasting trends in summer total rainfall (STR) over the Tibetan Plateau (TP), with an increase in the northern and a decrease in the southern TP. This study identifies four significant centers of rainfall trends: eastern TP (“region A”), Qiangtang Plateau (“B”), Qaidam Basin (“C”), and the northern foothills of the Himalayas (“D”). Heavy rainfall dominates STR trends in regions A and D, accounting for 55.6% and 52.0%, respectively. In region B, moderate and light rainfall contribute almost equally, accounting for 37.3% and 44.8% of the STR trend, respectively. Region C is primarily influenced by light rainfall, explaining 71.2% of the STR trend. Notably, the contributions of different rainfall intensities to STR in each region vary annually, with region A experiencing more heavy rainfall, region B having moderate dominance but less light rainfall, and region C and D showing reduced and increased light rainfall contributions, respectively. Mechanistically, the strengthening of the upper-level westerly jet and the South Asian High, coupled with changes in moisture transport and convective available potential energy, collectively cause variations in rainfall intensity, characterizing the spatial heterogeneity in STR in the TP.

This study identifies break events of the South China Sea (SCS) summer monsoon (SCSSM) based on 42 years of data from 1979 to 2020, and investigates their statistical characteristics and associated atmospheric anomalies. A total of 214 break events are identified by examining the convection evolution during each monsoon season. It is found that most events occur between June and September and show a roughly even distribution. Short-lived events (3–7 days) are more frequent, accounting for about two thirds of total events, with the residual one third for long-lived events (8–24 days). The SCSSM break is featured by drastic variations in various atmospheric variables. Particularly, the convection and precipitation change from anomalous enhancement in adjoining periods to a substantial suppression during the break, with the differences being more than 60 W m⁻² for outgoing longwave radiation (OLR) and 10 mm d⁻¹ for precipitation. This convection/precipitation suppression is accompanied by an anomalous anticyclone in the lower troposphere, corresponding to a remarkable westward retreat of the monsoon trough from the Philippine Sea to the Indochina Peninsula, which reduces the transportation of water vapor into the SCS. Besides, the pseudo-equivalent potential temperature declines sharply, mainly attributable to the local specific humidity reduction caused by downward dry advection. Furthermore, it is found that the suppressed convection and anomalous anticyclone responsible for the monsoon break form near the equatorial western Pacific and then propagate northwestward to the SCS.

Diurnal variation of precipitation is distinct over the tropical region. In the Indian monsoon region, nocturnal rainfall is dominant over the upslope area of the Himalayas and Megalayas, where climatologically heavy rainfalls occur. There are several studies which investigated the mechanism of the nocturnal rainfall from observations and numerical modeling. However, investigations using in-situ weather radars have not been conducted. This study conducts a case study of a nocturnal rainfall on 20-22 July 2019, using the ISRO Cherrapunji Doppler radar, which was continuously operated in every 15 min. The radar reflectivity was corrected by the GPM DPR and in-situ disdrometer. The analysis period was in an active spell, and southwesterly wind was dominant. The convective activity became active in the south of the plateau in the nighttime and a small rainband with less than several tens kilometers was formed, approached toward the plateau.

The Libera Mission, named for the daughter of Ceres in Roman mythology, will provide continuity of the Clouds and the Earth’s Radiant Energy System (CERES) Earth radiation budget (ERB) observations from space. Libera’s attributes enable a seamless extension of the ERB climate data record. Libera will acquire integrated radiance over the CERES FM6-heritage broad spectral bands in the shortwave (0.3 to 5 μm), longwave (5 to 50 μm) and total (0.3 to beyond 100 μm) and adds a split-shortwave band (0.7 to 5 μm) to provide deeper insight into shortwave energy deposition. The Libera science objectives associated with continuity and extension of the ERB data record are to identify and quantify processes responsible for ERB variability on various times scales. Key to our understanding of outgoing shortwave radiation is the radiative effects of clouds aerosols. Libera’s stewardship of the ERB record begins in the latter part of this decade, at an important juncture in the monitoring of climate change trends. The Libera split-shortwave channel will provide additional information to help quantify shortwave cloud and aerosol effects. This talk will examine our latest understanding of radiative perturbations due to clouds and aerosols and the expected contributions and advances from the Libera mission.

Challenges persist in providing accurate estimates of Earth’s surface shortwave (SW) radiation budget at the global scale. This is partly because Radiation Transfer Models (RTMs) are needed to determine surface fluxes. In this study, clear-sky total, direct, and diffuse SW fluxes at the surface were calculated by two RTMs: the computationally taxing high-spectral resolution MODTRAN6.0.2.5 (M6.0); and a computationally efficient low-spectral resolution correlated k-distribution (CKD) (Li and Barker 2005). Both RTMs used the same inputs, which includes profiles of state variables, aerosol properties, and surface albedos. The computed SW fluxes from these RTMs were compared to the NASA CERES SYN1deg product that was computed by NASA Langley’s modified broadband Fu-Liou RTM, which is widely used to study Earth’s radiation budget. Initial comparisons between three surface SW fluxes are encouraging: global annual means are 246.5 for M6.0, 245.4 for CKD, and 242.3 Wm^-2 for SYN1. While regional differences can be large, most are less than CERES’s estimated uncertainty for monthly-mean surface SW irradiance of ~6 W m^-2. Sensitivities of clear-sky SW/μ₀ fluxes, where μ₀ is cosine of solar zenith angle, to precipitable water vapor (i.e., clear-sky water vapor radiative kernel) are about -0.7 W m^-2/(kg m^-2) over oceans from both M6.0 and CERES SYN1 products (Zhong et al. 2024). To quantitatively estimate their SW flux uncertainties, surface observations from seven sites in different climatic regions (polar regions, oceans, lands, desert) were used (see Brendecke et al. 2024). Computed SW fluxes from M6.0 and CKD are summarized in Table 1 (without aerosol), and three RTMs calculations with aerosols against the surface observations are listed in Table 2. Mean difference between two RTMs in Table 1 is 0.6 Wm^-2, whereas their differences in Table 2 are 1.9 and 3.8 Wm^-2 compared to surface observations.

Incoming solar radiation drives the Earth’s climate system. Long-term surface observations of solar radiation reaching the surface have shown decreasing or increasing trends in different regions of the world in the past several decades, indicating the change of atmospheric components that reflect and/or absorb the solar radiation. This study investigates the roles of aerosols and clouds play in determining the multi-decadal surface radiation trends through a series of model simulations with the NASA GEOS model and analysis of ground-based observations and satellite-derived data products. We will 1) assess the effects of climate variability on trends of cloud cover and aerosol amount, 2) estimate the effects of aerosols on cloud variations and trends through aerosol-cloud-radiation interactions, and 3) identify the roles of aerosol, cloud, and climate change in multi-decadal trends of solar radiation reaching the surface in different regions of the world.

Atmospheric environmental variables including the aerosols, the stratospheric ozone and large scale atmospheric circulations, can modify the surface radiation balance and then affect Arctic climate by changing cloud properties. Using observation data, we found that aerosols from mid-latitude via long-range transport can change the cloud properties by decreasing cloud droplet effective radius and increasing cloud liquid water path, particularly in winter. Correspondingly, aerosols from mid-latitude enhance cloud thermal emissivity, contributing to Arctic warming by 8-10 W/m2 in winter. Using reanalysis meteorology data, we further found that depletion of stratospheric ozone has a significant contribution to the Siberian warming occurred in Spring 2020. A good correlation is found between the surface warming in Siberian region and stratospheric ozone depletion. Further analysis shows that the depletion of ozone depletion increases the upper troposphere air instability, resulting in a significant increase of upper level clouds, further causing surface warming. Also using reanalysis data, we found the significance of North Atlantic Oscillation (NAO) on the surface radiation balance over Greenland ice sheet (GrIS) by modulating the spatial distribution of clouds. It shows that the spatial and temporal variations in clouds in different phases over the GrIS are closely related to the NAO, and the response of clouds to changes in the atmospheric circulation field during the NAO varies in different regions of the GrIS. Shortly, this study provides the potential warming contribution in the Arctic by modulating cloud properties from three environmental factors, aerosols, depletion of stratospheric ozone, and NAO.

The aerosol-cloud-precipitating interaction within the cloud-topped Marine Boundary Layer (MBL), are being examined using aircraft in-situ measurements from Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) field campaigns. SOCRATES clouds have a larger number of smaller cloud droplets compared to ACE-ENA summertime and wintertime clouds. The ACE-ENA clouds, especially in wintertime, exhibit pronounced drizzle formation and growth, attributed to the strong in-cloud turbulence that enhances the collision-coalescence process. Furthermore, the Aerosol-Cloud Interaction (ACI) indices from the two aircraft field campaigns suggest distinct sensitivities. The aerosols during ACE-ENA winter are more likely to be activated into cloud droplets due to more larger aerosols and strong vertical turbulence. The enriched aerosol loading during SOCRATES generally leads to smaller cloud droplets competing for available water vapor and exhibiting a stronger ACI. The ACI calculated near the cloud base was noticeably larger than the layer-mean and near-cloud-top, owing to the closer connection between the cloud layer and sub-cloud aerosols. Notably, the sensitivities of cloud base precipitating rates to cloud-droplet number concentrations are more pronounced during the ACE-ENA than during the SOCRATES campaigns. The in-cloud drizzle evolutions significantly alter sub-cloud cloud condensation nuclei (CCN) budgets through the coalescence-scavenging effect, and in turn, impact the ACI assessments. The results of this study can enhance the understanding and aid in future model simulation and assessment of the aerosol-cloud interaction. From this talk, we want to tentatively answer the following three questions: What are the characteristics of MBL aerosol and clouds properties, and their similarities and differences over these regions? What are the cloud microphysical responses to the sub- and above-cloud aerosols/CCN (ACIs) To what extent the drizzle drops influence the sub-cloud aerosols/CCN (ACPIs)?.

Dust and anthropogenic aerosols from East Asia are transported to the Northwestern Pacific (NWP) by westerlies and active extratropical cyclones, modifying clouds of different types and affecting the energy budget of Earth. These aerosols have diverse effects on cloud properties and radiation forcing due to their distinct optical and hygroscopicity properties. In this study, we investigate the variation in the cloud properties of different cloud types with dust and anthropogenic aerosols under specific weather patterns. We classify the clouds into regimes based on the "K-means" clustering algorithm utilizing the Himawari-8 satellite cloud products. Weather patterns are classified by the principal component analysis in T-mode (T-PCA) to identify the meteorological factors that favor the formation of the prevailing cloud regime under the weather systems. Events dominated by the two types of aerosols are identified based on the largest dust and anthropogenic AOD anomalies, respectively. Our analysis suggests that the sensitivities of cloud properties to AOD depend on aerosol types. Considering the influence of meteorology on the cloud processes, the relative contribution of aerosols and meteorological factors are assessed using multiple linear regression. This study helps to improve our understanding of the variation of clouds with different types of aerosols in NWP.

We present our study on the sub-seasonal variability of UTLS aerosols and CO that is a result of the variability induced by the sub-seasonal variability of the Asian summer monsoon (ASM) dynamics. We use the NASA global model GEOS simulations that incorporates emissions from anthropogenic, biomass burning, volcanic, and other natural sources to simulate CO, aerosols and related gases with model experiments separating source types (anthropogenic, biomass burning, volcanic) and source regions (East Asia, South Asia). Using the model results that are evaluated with observations from recent aircraft measurements (StratoClim, summer 2017 and ACCLIP, summer 2022) in the UTLS over the Asian summer monsoon regions, we will discuss (1) the sub-seasonal variability of transport pathways of surface-generated pollutants to reach UTLS, and (2) sub-seasonal variability of aerosol composition that is determined by the variability of source type and source regions, and (3) similarities and differences of ASM dynamics between two summers of 2017 (StratoClim) and 2022 (ACCLIP) in the context of ASM interannual variability.

The Tibetan plateau around by the Taklimakan and Gobi deserts in the West and North, through have limited human activities. The aerosol vertical distribution play a key role in radiative balance. In-situ measurements are needed to investigate the size distribution of the particles in the troposphere. Aerosol size (0.13-3μm) distributions and concentration were measured by balloon-borne sensors (POPS) launched Kunming (25.01°N, 102.65°E) since 2015. However, balloons payload with POPS were launched systematically every tow month from June 2020 to November 2023 in Lhasa (29.66°N, 91.14°E) or Golmud (36.48°N, 94.93°E) or Lijiang (100.22°E, 26.85°N) over the Tibetan Plateau. In total, 36 profiles will give a vertical distribution of particles from surface to the lower stratosphere (25 km) around the Tibetan Plateau. Concentration decreased for all particle sizes above the boundary layer where smallest particles increased in the Asian tropopause aerosol layer (ATAL) around the tropopause. Furthermore, volcano and dust storm have significant impact on aerosol size distribution around the tropopause.

As many reported, the strength of Asian Summer Monsoon Anticyclone (ASMA) has increased recently. ASMA is one of the pivotal pathways of surface chemical species into upper troposphere and lower stratosphere (UTLS) over Northern Hemisphere. Also, the previous research on the linkage between intensity of ASMA and Siberian wildfire activity has been presented. Therefore, we examined the long-term variation of hydrocarbons in East Asian UTLS related to Siberian wildfire during Asian summer monsoon period. For this purpose, we used Atmospheric Chemistry Experiments (ACE)-FTS v4.1/4.2 to investigate vertical profiles of hydrocarbons (HCN, CO, C₂H₂, C₂H₆ etc.) in East Asia, and Moderate Resolution Imaging Spectroradiometer (MODIS) MOD14/MYD14 and MCD64A1 to analyze wildfire activity (Fire Count (FC) and Burned Area (BA)) in Siberia. As a result, we found the enhancement of hydrocarbons in summer (June to August) over UTLS (8-13km altitude). In addition, Siberian wildfire occurred mainly in the summer season. These enhancements of hydrocarbons in north China show the highest correlations with FC and BA in Siberia (R = ~ 0.5 to 0.6), meaning that Siberian wildfire might affect variability of hydrocarbons composition in UTLS. Additionally, we found that activity of Siberian wildfire has increased in conjunction with expanded ASMA area recently, investigated from Modern-Era Retrospective analysis for Research and Application version 2 (MERRA-2). As the ASMA expanded, we confirmed higher geopotential height, warmer temperature, arid weather in Siberia near surface. Furthermore, interesting point is that hydrocarbon concentration in UTLS has increased when ASMA intensified. However, we need to carefully evaluate increased hydrocarbon concentration related to expanded ASMA since the pattern of enhanced hydrocarbon concentration by latitude is different. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006).

In this study, we show preliminary results about the comparison of summertime (August) ozonesonde measurements from 2021 to 2023. Ozonesonde measurements were performed in both western (Anmyeon, Seosan, and Osan) and eastern (Pohang) region of Korean peninsula. First, we found that the annual variation of tropopause height. Ozone tropopause heights are higher in 2022 compared to those in 2021 and 2023. Interestingly, surface ozone at Anmyeon is lower and tropospheric convection is more intensified in 2022, implying that the tropospheric stability affects the extent of stratospheric intrusion to the troposphere, which seems associated with the enhancement of surface ozone. If we compare the ozone vertical profile between western and eastern region (i.e.,, Anmyeon vs. Pohang), free-tropospheric ozone level is higher but near-surface ozone level is lower in the east of Korean peninsula, indicating that the effect of stratospheric ozone intrusion is not much significant to the enhancement of near-surface ozone. For understanding this discrepancy, we need to consider the different pattern of air pollutant emission between the west and east of Korean peninsula; A number of power plants and industrial complex are located near the Anmyeon and Seosan). Based on these results so far, we can conclude that the intrusion of stratospheric ozone-rich air mass should be considered for assessing the high level of near-surface ozone in the region where the large emission and chemical processes of precursors happen. Additional further analyses are still necessary for better understanding.

In order to understand the impact of the summer monsoon on ozone distribution over East Asia, 24 daily ozonesonde measurements were conducted at the Anmyeon site (36.54°E, 126.25°N), South Korea in August 2021. Electrochemical Concentration Cell (ECC) type ozonesonde was used for the ozone profiling from August 5 to 31, and it revealed an average Total Column Ozone (TCO) of 300 DU, consistent with regional climatology. Notably, TCO increased by approximately 17%, reaching 351 DU from August 17 to 19. This ozone concentration increase in the upper troposphere and lower stratosphere (UTLS) was linked to a Stratosphere-Troposphere Exchange (STE) event over the Korean Peninsula. The elevated ozone concentrations in the UTLS were examined through mid-tropospheric ozone observations, indicating a connection to the eastward expansion of the Asia summer monsoon anticyclone and subsequent anticyclonic wave breaking. Horizontal transportation also played a role in mid-tropospheric ozone concentration increases. Analysis of potential vorticity from reanalysis data revealed a coherent vertical structure with the intruded high ozone signature. Ozone data from chemistry reanalyses (MERRA-2 and CAMS) were compared to the ozonesonde measurements to assure their data quality. While TCO and stratospheric ozone are similar among the three datasets, the lower troposphere and surface values show significant spreads, revealing their uncertainty.

Lightning is one of the leading sources of upper tropospheric (UT) active nitrogen (NOx) which is an important catalyst for the production of ozone and destruction of methane in the UT. Tropospheric ozone is an important greenhouse gas and radiative forcing calculations are particularly sensitive to changes in ozone concentrations in the cold UT. Lightning NOx has a significant impact on the tropospheric ozone budget and global climate, and understanding its sources is of interest. Currently, a large range exists in estimated global emission rates of lightning NOx (2-8 Tg/yr) from models and more needs to be done to reduce this uncertainty. The Asian Summer Monsoon (ASM) has been identified as a strong source region for UT NOx. During the ACCLIP field campaign, there were numerous instances where pockets of high NO₂/NO concentrations were observed in the UT by instruments on the NCAR GV and NASA WB-57 research aircraft. Since deep convection in the ASM also pumps up NOx from boundary layer sources, separating the contributions from pollution and lightning are challenging. To tackle this issue, we explore an analysis of multiple datasets, taking advantage of the high spatial and temporal resolution GEMS satellite NO₂ data. With the assistance of a Lagrangian trajectory model, we examine the relationship of high NOx data from airborne in situ measurements with the GEMS high NO₂ hot spots and lightning data from the Earth Networks Global Lightning network. This exploratory study aims to identify and distinguish lightning and pollution sources for high NOx measurements and provide insight for general source identification.

This study investigates raindrop size distribution (RSD) characteristics during the Northeast Monsoon (NEM) and Southwest Monsoon (SWM) seasons in Southern Luzon, Philippines, from 2018-2023, using an OTT Parsivel disdrometer installed at the University of the Philippines Los Baños - National Agromet Station (UPLB-NAS) in Los Baños, Laguna (14.1647°N, 121.2501°E). The research reveals that smaller raindrops dominate in the NEM, while larger drops prevail in the SWM. Both monsoons exhibit predominantly stratiform rainfall, with convective rains in both SWM and NEM, displaying continental-like characteristics. Intensity-based analysis indicates a concentration of smaller drops during NEM and a higher occurrence of small drops in SWM during torrential events (> 30mm/hr). Larger drops (>3mm) are notably present in SWM across all intensity categories. Simultaneously, the study explores soil erosion processes during NEM and SWM, considering the region's susceptibility to heavy rainfall and erosion. Using the same disdrometer, the research established a relationship between rainfall intensity (R) and kinetic energy (KE). Kinetic energy expenditure (KEexp) and kinetic energy content (KEcon) expressions are introduced, exploring their relationships with R through linear, power, exponential, and logarithmic models. This integrated approach advances understanding of both precipitation dynamics and soil erosion during NEM and SWM in Southern Luzon. The implications extend to the development of accurate erosion prediction models and sustainable land management practices, enhancing the resilience of the region during these critical monsoon periods.

Marine biotoxins can be aerosolized into the atmospheric environment and cause negative health effects through respiratory exposure and other pathways. Characterizing marine biotoxins (MBs) composition in coastal aerosol particles has become essential to tracking sources of atmospheric contaminants and assessing human inhalable exposure risks to air particles. Here, coastal aerosol particles were collected over an almost 3-year period for the analysis of eight representative MBs, including brevetoxin (BTX), okadaic acid (OA), pectenotoxin-2 (PTX-2), domoic acid (DA), tetrodotoxin (TTX), saxitoxin (STX), ciguatoxin (CTX) and ω-Conotoxin. Our data showed that the levels of inhalable airborne marine biotoxins (AMBs) varied greatly among the subcategories and over time. Both in daytime and nighttime, a predominance of coarse-mode AMB particles was found for all the target AMBs. Based on the experimental data, we speculate that an ambient AMB might have multiple sources/production pathways, which include air-sea aerosol production and direct generation and release from toxigenic microalgae/bacteria suspended in surface seawater or air, and different sources may make different contribution. Regardless of the subcategory, the highest deposition efficiency of an individual AMB was found in the head airway region, followed by the alveolar and tracheobronchial regions. This study provides new information about inhalable MBs in the coastal atmosphere.

Aerosol Acidity largely regulates the atmospheric multiphase chemistry. In addition, recent studies have suggested the potential role of acidity on the biological components of particulate matter. Here, we investigated into this issue by simultaneous measurements of the chemical and biological components in Beijing, a megacity in China. Based on these measurements, the aerosol acidity can be estimated, and its potential influence on the biological components are investigated.

Atmospheric microorganisms are important components of bioaerosols and potentially impact terrestrial ecosystems and climate. The survival and metabolic activities of atmospheric microbes in suitable settlements may affect cloud chemistry and characteristics and functions of microbiomes in surface ecosystems. Therefore, the metabolic processes and characterization of metabolites occurring in atmospheric viable microorganisms settled suitable habitats remain highly explorable. This study used ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (ESI-) to characterize the exo-metabolites of typical bacterial and fungal strains isolated from atmospheric particles at the molecular level, and the molecules were annotated using the KEGG database to investigate their metabolic processes. Microbial culture results showed that Proteobacteria, Firmicutes, and Actinobacteria dominated the culturable bacteria, while fungi were dominated by Ascomycota, with Aspergillus and Penicillium being the predominant genera. The molecular compositions of exo-metabolites from bacteria and fungi were characterized by distinct differences, with bacteria having a higher molecular diversity than fungi. Bacterial exo-metabolites were mainly composed of CHON and CHONS compounds, which accounted for over 90% of all detected compounds. While, fungal exo-metabolites were dominated by CHO and CHON compounds. Pantoea vagans produced more unsaturated hydrocarbons compounds containing amino or amide groups. The exo-metabolites (CHO compounds) of Talaromyces sp. were mainly CRAM-like compounds. The metabolite formulas were annotated and enriched using the KEGG database, revealing significant differences in central metabolic pathways between the different bacteria and fungi. Lipid metabolism was predominant in Pantoea vagans and Bacillus subtilis, while carbohydrate metabolism was the main pathway in Pseudomonas baetica and Bacillus toyonensis. The methane metabolism and lysine biosynthesis were the primary pathways in Aspergillus, and Talaromyces sp. specialized in aflatoxin biosynthesis. This study further illustrates that atmospheric microorganisms could have intense metabolic activities and provides a basis for evaluating their impacts on cloud chemistry and surface ecosystems after deposition.

The adverse health effects of air pollutants are closely related to their components. Bioaerosols, as an integral part of particles, play a crucial role. However, the dynamics of bioaerosols during pollution processes are not well understood. In this study, we investigated the dynamics of bacterial aerosols over a one-week period. During the sampling period, haze and sandstorm events occurred sequentially, with a transition period in between. We applied 16S rDNA and 16S rRNA sequencing techniques to explore the total bacterial community and the active bacteria, respectively. A distinct profile of bacterial aerosols was observed during the haze and the sandstorm event. The highest bacterial diversity was observed in the sandstorm samples, while the lowest was observed in the haze samples. Furthermore, the bacterial aerosols during the haze showed the greatest difference from those during the transition period when compared to the sandstorm samples. The total bacterial profile, as revealed by 16S rRNA sequencing results, was found to be distinct from the active bacterial communities in the sandstorm samples. Similarly, the underlying ecological drivers shaping the bacterial community structures showed different patterns. We found that the selective forces played a role in shaping the active bacterial communities in the sandstorm samples, as well as the total bacteria in the haze samples. A common feature during the haze and sandstorm episodes was the long residence time of bacteria in the air, indicating that the prolonged residence can lead to changes in the structure of airborne bacterial communities. Furthermore, the results indicate that several pathogens or opportunistic pathogens were more prevalent in the active bacterial communities of the sandstorm samples and the total bacteria in the haze samples, suggesting an increased health risk for humans, animals and even plants.

Baijiu is one of the six primary distilled spirits in the world. It is produced through the solid-state fermentation of grains in the open environment, so high-quality Baijiu brewing largely depends on terrior. Environmental microbes are one of the most important factors affecting the quality, quantity, and flavors of Baijiu. As atmosphere is a pool and transport pathway for microbes from the ambient environment to Baijiu brewing ecosystems, we explored the functional microbes of Baijiu brewing in five important regions. The regions fell into two topographical types, namely, plain and river-valley. In total, 41 functional microbes were identified rich (relative abundance >0.1%) in at least one of the regions, such as the fungi of Aspergillus, Candida, Cladosporium, Debaryomyces, Penicillium, Pichia, Rhizopus, Saccharomyces, and Wickerhamomyces) and the bacteria of Acetobacter, Bacillus, Clostridium, Enterobacter, Lactobacillus, Methanosarcina, Methanobacterium, Methanobrevibacter, and Pseudomonas. However, some functional bacteria (e.g., Clostridia, Gluconacetobacter, and Weissella) and fungi (e.g., Dekkera, Eurotium, Issatchenkia, Mucor, and Phoma) were not rich or were not detected in the atmosphere. Airborne microbiomes and the Phylogenetic Diversity (PD) index were significantly different between the main brewing season (winter) and the summer break in each region, except for the fungi in one region. In winter, airborne microbiomes were significantly different among almost all the regions. The relative abundance of bacterial fermentation function in each region increased from summer to winter. The relative abundances of fungal yeast function were higher in winter for the plain regions but were higher in summer for the river-valley regions. In sum, our results suggested that: (1) atmosphere was one but not the sole important source of functional microbes for Baijiu brewing and (2) microbiomes in different regions might be quite different but they could share some major functions related to Baijiu brewing.

Urban ambient air contains a cocktail of antibiotic resistance genes (ARGs) emitted from various anthropogenic sites. However, what largely unknown is whether the airborne ARGs exhibit site-specificity or their pathogenic hosts persistently exist in the air. Here, by retrieving 1.2Tb metagenomic sequences (n = 136), we examined the airborne ARGs from hospitals, municipal wastewater treatment plants (WWTPs) and landfills, public transit centers, and urban sites located in seven China’s megacities. As validated by the multiple machine learning-based classification and optimization, ARGs’ site-specificity was found to be the most apparent in the hospital air, with featured resistances to clinical-used rifamycin and (glyco)peptides, whereas the more environmentally prevalent ARGs (e.g. resistance to sulfonamide and tetracycline) were identified being more specific to the non-clinical ambient air settings. Nearly all metagenome-assembled genomes (MAGs) that possessed the site-featured resistances were identified as pathogenic taxa, which occupied the upper-representative niches in all the neutrally distributed airborne microbial community (P < 0.01, m = 0.22 – 0.50, R² = 0.41 – 0.86). These niche-favored putative-resistant pathogens highlighted the enduring antibiotic resistance hazards in the studied urban air. These findings are critical, albeit the least appreciated till our study, to gauge the airborne dimension of resistomes’ feature and fate in urban atmospheric environments.

Packaging is necessary for preserving and delivering products and has significant impacts on human health and the environment. Particle matter (PM) may be released from packages and transferred to the air during a typical peeling process, but little is known about this package-to-air migration route of particles. Here, we investigated the emission profiles of total and biological particles, and the horizontal and vertical dispersion abilities and community structure of viable microbes released from packaging to the air by peeling. The results revealed that a lot of inhalable particles and viable microbes were released from package to the air in different migration directions, and this migration can be regulated by several factors including package material, effective peeling area, peeling speed and angles, as well as the characteristics of the migrant itself. Dispersal of package-borne viable microbes provides direct evidence that viable microbes, including pathogens, can survive the aerosolization caused by peeling and be transferred to air over different distances while remaining alive. Based on the experimental data and visual proof in movies, we speculate that nonbiological particles are package fibers fractured and released to air by the external peeling force exerted on the package and that microbe dispersal is attributed to surface-borne microbe suspension by vibration caused by the peeling force. This investigation provides new information that aerosolized particles can deliver package-borne substances and viable microbes from packaging to the ambient environment, motivating further studies to characterize the health effects of such aerosolized particles and the geographic migration of microbes via packaging.

The role of indoor microbial exposure in human health has received increasing attention, given that individuals spend a significant portion of their time indoors. However, the factors influencing the microbial dynamics within inhabited environments are not yet comprehensively understood. This study aimed to investigate the bacterial dynamics in both the air and floor dust within occupied environments. Residences, dormitory and office environments were chosen. And the sampling was performed over a month during which window openings were infrequent. We employed amplicon sequencing techniques to characterize the indoor bacterial and fungal communities. Our findings revealed a diverse microbial community present in both the indoor air and floor dust samples. Specifically, the bacterial communities in the air exhibited greater similarity compared to those in the floor dust, whereas the fungal communities showed less discrepancy. Analysis using the Sloan Neutral Model (SNM) indicated that stochastic processes significantly influenced the structure of both the airborne and floor-borne bacterial communities. Furthermore, we investigated the microorganisms associated with the dust on the soles of shoes and compared them to the indoor microbiome. Interestingly, we observed a high degree of similarity between the shoe-related microorganisms and those found in the floor dust. This suggests that shoe-associated microorganisms could potentially be used to “re-wild” the indoor microbiota.

Earth System Models (ESMs) exhibit varying spatial resolutions, distinct from satellite observations. We aim to determine the optimal spatial resolution for new simulations that enhance our understanding of regional climate change. Traditional upscaling, by averaging high-resolution data onto coarser grids, loses critical details. Our novel toolkit for climate model evaluation counters this by integrating Hierarchical and Topological Data Analysis (HDA and TDA) into the Jet Propulsion Laboratory's system, enabling multi-resolution analysis. The multi-resolution evaluation separately characterizes spatial features of key climate model variables at coarse and fine scales, examining their resolution-dependent performance. We leverage HDA based on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to evaluate temperature and humidity from climate models against state-of-the-art level 3 (L3) products from AIRS and CrIS. Unlike standard latitude-longitude representations, HEALPix grids consist of hierarchical and equal-area cells that can be efficiently upscaled. We have pioneered a prototype HDA for Earth science datasets, enabling dynamic spatial resolution changes and analysis of spatial variability at multiple scales, showcasing the value of high-resolution data. Topology, the study of shapes, and the emerging field of TDA, lie at the intersection of algebraic topology, machine learning, and statistics. Topological data can reveal latent structures and play a crucial role in understanding spatiotemporal patterns in datasets. Currently, no quantitative metric measures spatial pattern differences in Earth science datasets across spatial resolutions. We developed topological descriptors that summarize the spatial structures of climate models and satellite observations into persistence diagrams (PDs). Our TDA-based measure allows for systematic evaluation of multi-resolution data without the need for regridding. TDA's greatest advantage is its ability to quantitatively compare shape properties, which are invariant under continuous transformations, thus offering a significant step forward in climate model evaluation.

The NIMS/KMA has been actively contributing to the CMIP program since CMIP3. NIMS participated in CMIP6 by utilizing the UKESM developed by the UK Met Office to generate future climate change scenarios for four distinct Shared Socio-economic Pathways. NIMS also employed the KMA Advanced Community Earth (K-ACE) model, a modified version of HadGEM2-AO developed through in-house research, to analyze global climate projections. Five different regional climate models were used for the regional climate simulations: HadGEM3-RA, RegCM4, CCLM, GRIMs, and WRF, organized under the CORDEX-EA program. Furthermore, for the South Korean area, NIMS produced 1km resolution climate change scenario data using the statistical downscaling technique, PRIDE. These projections played a pivotal role in contributing to the preparation of the Sixth Assessment Report by the IPCC and provided crucial foundational data for national climate change adaptation efforts. Currently, NIMS has initiated preparations for CMIP7 participation. In this program, K-ACE will be employed for producing global climate projections, having undergone improvements such as coupling with an ocean-biogeochemistry model, TOPAZ, and modifications to the cloud-aerosol process, among other enhancements. NIMS plans to use a reduced number of RCMs compared to the CMIP6 phase but intends to increase the ensemble members by combining physical processes. Currently under consideration as RCM candidates are WRF and WRF-ROMS. To comprehend the impact of climate change on local-scale heavy rain, a Convection Permitting Model can be employed. For the South Korean region, our objective is to produce more high-resolution, detailed climate scenarios through sensitivity experiments and reliability verification studies. This presentation aims to introduce KMA's Earth System Models, aligning with recent trends and developments outlined in CMIP7, and presenting the overall plans for the generation and utilization of global-regional-local climate projections in line with CMIP7.

The double-intertropical convergence zone (ITCZ) bias (DIB) is one of the most prominent and long-standing tropical precipitation biases in coupled global climate models and its root causes are still unclear. Here, we examine the relative contribution of the DIB in atmospheric models and the tropical sea surface temperature (SST) bias in coupled models to the DIB in coupled models at monthly instead of annual time scale using 59 paired atmospheric and coupled models from Coupled Model Intercomparison Project Phases 5/6. We find the DIBs in atmospheric and coupled models are positively correlated at each month although with no correlation at annual mean. In contrast, the high correlation between the DIB and tropical SST bias in coupled models at annual mean becomes much weaker at each month. These changes are particularly large during boreal winter (December-April) and summer (July-September). Our results suggest that both the DIB in atmospheric models and the tropical SST bias in coupled models play an important role in producing the DIB in coupled models.

Near-term climate predictions that incorporate seasonal-to-decadal time scales have recently received much attention from policymakers, stakeholders, and the climate science community in the context of climate risk management. The primary source of seasonal-to-multiyear prediction skill is the low-frequency variability of sea surface temperature, such as the El Niño–Southern Oscillation (ENSO). This study assesses the seasonal-to-multiyear prediction skill of ENSO using large ensembles of the Coupled Model Intercomparison Project phases 5 and 6 retrospective decadal predictions. By taking advantage of large ensemble sizes, the relative importance of the ensemble size versus the multi-model ensemble average in predicting multi-year ENSO index is evaluated. The results show that a multi-model ensemble reforecast successfully predicts ENSO over a year in advance. While its seasonal prediction skill in the following spring and summer is achieved by multi-model ensemble averaging of relatively smaller ensemble members, the multi-year prediction of winter ENSO needs a larger ensemble size. The methodology used in this study, which verifies the impact of the ensemble size and multi-model ensemble average, could be applied to any climate prediction studies and would improve our understanding of climate prediction. (Reference: Choi, J. and S.-W. Son, 2022, Seasonal-to-decadal prediction of El Niño–Southern Oscillation and Pacific Decadal Oscillation, npj Climate and Atmospheric Science 5:29; https://doi.org/10.1038/s41612-022-00251-9).

In this study, we analyzed changes in teleconnection patterns associated with heat wave in East Asia using 1pctCO2 (ramp-up, 1%/yr increase) and 1pctCO2-cdr (ramp-down, 1%/yr decrease) simulations of the CMIP6 CDR-reversibility. To examine changes in teleconnection patterns associated with heat wave, Empirical Orthogonal Function (EOF) analysis was performed for the 250hPa geopotential height shifted by one year for every 30 years during the total period to show mode changes over time. The first mode represented a trend, showing consistent spatial patterns of increased and decreased geopotential height in response to change in CO2 concentration. The second mode exhibits a pattern similar to Circumglobal Teleconnection (CGT), gradually transitioning to a sub-mode during the ramp-up period with a weakening pattern. The pattern recovers during the ramp-down, but it remains in the sub-mode. The fourth mode is similar to the Arctic-Siberian Plain (ASP) pattern, which is responsible for the high-latitude East Asian heat wave pattern and is mainly in the sub-mode during the ramp-up but becomes the main mode after the transition to the ramp-down. These results suggest that the main mode of the teleconnection patterns that affect heat wave in East Asia may change as CO2 concentrations increase and decrease. [This work was funded by the Korea Meteorological Administration Research and Development Program under Grant (KMI2022-01311)].

This study investigates the inter-model spread of extratropical westerly jets between 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models in boreal winter. The results show that there is a substantial spread in latitude of upper-tropospheric westerly jet between models, characterized by large inter-model standard deviations to the poleward and equatorward of jet axis, although the multi-model ensemble mean (MME) of the models performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter-model jet position spread between the northern and southern hemispheres, based on the inter-model empirical orthogonal function (EOF) decomposition and correlation of regional-averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward than MME position in one hemisphere tend to also simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of regression analyses based on this index indicate that the models positioning the jets poleward than MME tend to simulate a wider Hadley Cell, a poleward-shifted Ferrel Cell in the southern hemisphere, and a wider intertropical convergence zone (ITCZ). Finally, the inter-model spread of ITCZ width is mainly determined by the spread of convective precipitations between the models, implying that different convection parameterization schemes may play a crucial role in inducing the inter-model spread of extratropical westerly jets and the concurrence of meridional jet shift in the two hemispheres.

The Hadley Circulation (HC) is a large-scale overturning circulation in the tropics that plays an important role in the Earth's energy and water cycles. Both observations and climate models show that the HC has been expanding poleward in recent decades, and this expansion is accelerating in future projections of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. However, the CMIP6 models differ widely in their projections of how much the HC will continue to expand, especially in the Southern Hemisphere (SH). In this study, we investigate the mechanisms by which the inter-model spread in SH HC expansion takes place. We use of the 22 CMIP6 model simulations with the historical and Shared Socio-economic Pathway (SSP) 5-8.5 scenarios. The HC is defined as the zonal mean mass streamfunction, and its edge is defined by the latitude where the sign of the streamfucntion at 500 hPa changes to positive in the subtropics. The intermodel spread in the HC trends is investigated by using the intermodel empirical orthogonal function (EOF) of the mass streamfunction trend patterns. We find that the first leading EOF (EOF1), which shows an anomaly at the HC edge, explains the 49.7% of the variance between the trend patterns and shows a significant correlation of 0.87 with the change of the HC extent. In the simulations with a relatively large HC expansion, the mean wind and temperature fields show an enhanced poleward jet shift and an increase in static stability in the subtropics. The difference in the mean fields results in intermodel spread and the eddy field. Analysis using the Eliassen-Kuo equation suggests that the meridional eddy heat flux dominantly explains the intermodel spread, while the eddy momentum flux contribution is about the half of the eddy heat flux.

The PCMDI Metrics Package (PMP) is an open-source Python-based framework that enables objective "quick-look" comparisons and benchmarking of Earth System Models (ESMs). The PMP’s connection to the Observations for Model Intercomparisons Project (obs4MIPs) enables considerations of multiple available observation-based reference datasets. The PMP has been used for routine and systematic evaluation of thousands of simulations from Coupled Model Intercomparison Projects (CMIPs), with atmospheric focus. Its results have been produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases, and we are making progress in preparing more seamless application of the tool and the result database for the next generation of CMIP (i.e., CMIP7) with particular attention to higher resolution simulations and regional-focused evaluations. The PMP offers capabilities for modeling groups to easily evaluate simulations and track changes during the development cycle, in the context of the range of structural errors in the multi-model ensemble. The latest version of PMP, version 3, emphasizes statistics of large- to global-scale annual cycles, tropical and extratropical modes of variability including ENSO and MJO, regional monsoons, cloud feedback, extremes, and high frequency characteristics of simulated precipitation. We are working toward implementing more metrics with extending scope of the realm, such as stratosphere (e.g, QBO), atmospheric dynamics (e.g. blockings), and high-latitude area (e.g, sea-ice). The PMP is prototyping the evaluation of higher resolution simulations such as from the HighResMIPs and cloud-resolving E3SM experiments. This presentation will introduce the PMP and discuss its future plan.

Impacts of local circulations and their interactions on ozone in Suzhou on 5 June 2019 are investigated using surface observations and three-dimensional air quality simulations. It is found that cities on the southeastern shore of Lake Taihu, such as Suzhou, Jiaxing, and Huzhou, have higher ozone concentrations compared with other cities in the Yangtze River Delta, although their emission levels are relatively low. Local circulations and their interactions are found to be the primary causes of the episode. The coupling of lake-land breeze circulation (LLBC), sea breeze circulation (SBC), and urban heat island circulation (UHIC) in regions on the southeastern shore of Lake Taihu such as Suzhou and Jiaxing create wind convergence zones, influencing the spatiotemporal distribution of ozone in the boundary layer. During the daytime, the LLBC plays a dominant role in maintaining the wind convergence zone. It transports pollutants to the surrounding regions of Lake Taihu, resulting in reduced pollution concentrations over the lake and worsen air quality in surrounding areas. The UHIC decouples with SBC at noon, leading to a stronger SBC, thus contributing to a reduction in pollution in upwind regions but hinders downwind diffusion. The UHIC enhances the convergence zones of secondary circulations, making it easier for the accumulation of ozone pollutants in areas between Lake Taihu and lakeside cities.

The health effects of air pollution have become more important and apparent in recent years. Exposure to air pollutants is associated with increased mortality hospitalization rates. However, it should be noted that the health effects of air pollution may vary in different regions. Here we focus on the health risks of air pollution in specific areas and the underlying attribution analysis. Considering that Day-to-day (DTD) temperature variability is an important characteristic of air temperature, which significantly affects human health, we examined the long-term trends of DTD over the past 26 years in the United States, and its association with changing air pollution. By using the observed data and The Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations, We demonstrate that the positive trend of the DTD index in US can be attributed to the anthropogenic aerosols, while the negative trend of which can be attributed to the natural forcing and greenhouse gas forcing. The observed DTD enhancement over 1997-2022 is dominated by the effect of anthropogenic aerosols, while natural forcing and GHGs partially counteract the effect of anthropogenic aerosols. Based on climate modeling experiments, we demonstrate that the reduced aerosol emissions in US can contribute to the enhanced trend of DTD in USA, Which may indicate that the effect of improved air pollution on health risks in US is not entirely positive.

We employed a modified the Cavity Attenuated Phase Shift NO₂ monitor and Chemical Ionization Mass Spectrometer, coupled with the eddy covariance method, to quantify NO_x and ClNO₂ fluxes in urban Beijing. We assessed the temporal and spatial emission characteristics of NO_x, and evaluating the anthropogenic NO_x emission inventory. Through ClNO₂ flux measurements, we also analyzed the impact of deposition on ClNO₂ concentration and yield. Our findings highlight NO_x flux exhibiting spatial and temporal heterogeneity in urban Beijing. Comparison results indicate that the current emission inventory overstates NO_x emission in the city of Beijing. For ClNO₂, our study reveals a significant influence of deposition on its concentration and yield.

Atmospheric amines are gaining more and more attention in the field of atmospheric chemistry owing to their important roles in new particle formation and growth. In this study, aerosol aminiums over a coastal city (Shanghai) and the Yellow and East China seas (YECS) were characterised. The concentrations of ammonium, dimethylaminium (DMAH) and trimethylaminium plus diethylaminium (TMDEAH) over Shanghai were all found to be higher in the winter of 2018 than in the summer of 2019, suggesting their non-negligible terrestrial contributions. DMAH and TMDEAH concentrations over the YECS in summer were closely correlated and linked to surface phytoplankton biomass, implying that marine biogenic sources might be a predominant contributor to aminiums at this time. Aminiums over Shanghai generally showed a bimodal distribution with a main peak in droplet mode and a secondary peak in condensation mode, suggesting the notable contribution of aqueous-phase or heterogeneous reaction to the formation of aminiums. In contrast, aminiums over the YECS often showed a unimodal distribution, which may be caused by the competition between amines and NH3 for reaction with acidic compounds. We estimated the contributions of marine biogenic sources, 73.6% to DMAH and 80.1% to TMDEAH over the YECS, using methanesulfonate/non-sea-salt SO₄^2– as an indicator. Our results suggest that marine biogenic emission of amines from China’s marginal seas may have a potential impact on coastal cities, and this source should be considered in modelling new particle formation and air quality in coastal areas.

Atmospheric ammonia emissions are deleterious due to their role in multifaceted environmental pollution. It has been demonstrated that volatile ammonia (NH₃), through interacting with sulphur dioxide and nitrogen oxide gaseous emissions, contributes to the formation of airborne fine particulate matter contributing to air quality degradation, reduced visibility, and regional haze. Further, NH₃ released by nitrogen (N) fertilizer applications (FA) worldwide leads to substantial economic loss in the agriculture sector. India, after China, stands as the world's second-largest producer and consumer of synthetic N-fertilizer. Despite the significance of NH₃ in the global nitrogen (N) cycle, notable inaccuracies and uncertainties persist in India regarding its atmospheric emissions. Current assessments of emissions from fertilizer applications rely on 'fixed' emission factors, introducing limitations and uncertainties in the estimation process. This study aimed to enhance the accuracy of NH₃ emission estimations by correcting the fixed emission factor with different modification factors (MFs). These MFs are functions of crucial influencing variables, including fertilizer type, soil pH, application method (basal and top dressing), application rate, and temperature. In 2018-19, the overall NH₃ emissions in India reached approximately 3.15 tera-grams (Tg). The considered fertilizer types encompass Urea (2959 Giga-grams (Gg)), DAP (Di-ammonium phosphate (112.6 Gg), AS (15.30 Gg), and NPK (65.96 Gg). Considering all fertilizer types, the agroclimatic regions of India characterized by the highest NH₃ emissions are the ‘Trans Gangetic Plain Region (525.61 Gg)’ followed by the ‘Upper Gangetic Plain Region (466.83 Gg)’. The total NH₃ emissions were found to peak during the monsoon season (Jul-Sep; 1217.35 Gg) followed by winter (Dec-Mar; 926.36 Gg). The current study addresses the exigency to minimize NH₃ emissions from FA and helps in implementing various stringent mitigation strategies and regulatory actions for sustainable environmental management.

Nitrate has become an increasingly important component of fine particulate matter (PM_2.5) in some major cities in China. It didn’t decrease as rapidly as the precursor nitrogen oxides (NO_x) did (or even increased), which has attracted wide interest. However, previous research mainly focused on elevated nitrate in cold seasons, leaving seasonal disparities in the trends of nitrate unclear. Here we investigated the interannual trends in nitrate at an urban site in Beijing for all seasons over 2015-2019 and explored the drivers by assembling in-situ measurements, machine learning, and chemical transport model (CTM) simulations. The results show that nitrate would increase in all seasons with the impacts of meteorology removed (i.e., de-weathered), at rates ranging from 1.1% per year in winter to 9.8% per year in summer. Such emission-induced increases in nitrate were inhibited by meteorology in the fall and winter, as a result of decreased humidity, while enhanced in the spring and summer. In addition, the de-weathered nitrate concentrations increased nonlinearly with NO₂, with steeper slopes of nitrate to the precursor in 2018-2019 than in 2015-2017, reflecting a higher nitrogen oxidation ratio (NOR) in more recent years. The reduced NOx emissions in spring, fall, and winter increased nocturnal NO₃ radicals, which could facilitate the heterogeneous formation of nitrate at night and promote NOR. In summer, NOR increased as well, but this might primarily be related to increased daytime OH radicals and enhanced NO₂+OH nitrate production. This study highlighted the distinct impacts of emission changes and meteorology on nitrate in four seasons, and the necessity for season-specific nitrate control strategies.

Nitrous acid (HONO) is a significant primary source of OH radicals in the troposphere. Thus it affects the oxidizing capacity of the atmosphere and the formation of ozone (O₃) and fine particulate matter (PM_2.5). However, the abundance of HONO is always not constrained in the chemical transport models (CTMs), indicating the sources have not been well recognized. In this study, we updated the parameterizations of HONO formation, particularly considering the impacts of light intensity and relative humidity on heterogeneous reactions of NO₂ on ground and aerosol surfaces, and added new sources (photolysis of nitrate, the heterogenous formation of HONO on soot, etc.) in the air quality model CMAQv5.3.2. We conducted simulations and investigated major sources of HONO at a rural site in the Yangtze River Delta region in the fall of 2020. The improved HONO chemistry case led to a substantial increase in HONO and reduced the gap between the simulation and observation (NMB from -0.83% to -0.40%). A sensitivity test indicated that negative biases of modeled NO₂ (most likely due to emissions) explained the remaining model-observation discrepancies. Overall, the main formation pathway of HONO at the site was the heterogeneous reaction on ground surfaces, accounting for approximately 90% of HONO formation at night and ~80% during the daytime. Meanwhile, we found a 2× increase in the daytime average OH concentration and a 1.8× increase of HO₂ with improved HONO simulation, resulting in enhanced nitrate, sulfate, ammonium, and SOA by factors of 1.3, 0.3, 0.8, and 1.1, respectively. This study emphasized the role of HONO chemistry in the formation of secondary pollutants, and future work needs constraint of radicals in the CTMs as well.

Volatile chemical products (VCPs) have been considered one of the major sources of reactive organic carbon (ROC, including volatile organic compounds (VOCs), and semi/intermediate volatile organic compounds (S/IVOCs)) in megacities. Among VCPs, emissions from domestic volatile chemical products (DVCPs, for instance, personal care products, cleaning products, and insecticides) and the associated impacts are not well understood, particularly in China. In this study, we compiled an emission inventory for DVCPs and explored the impacts of DVCP emissions on the atmospheric environment over the Yangtze River Delta (YRD) using an air quality model. The newly estimated emissions from DVCPs are four times higher than those in the traditional inventory and accounted for 10% of total residential emissions in the YRD. The simulation with updated DVCP emissions compared well with measurements in terms of DVCP tracers (e.g., siloxanes, isopropanol) in urban Shanghai. With improved gas-phase chemistry for oxygenated VOCs as well as parameterization for secondary organic aerosol (SOA) from compounds that had been overlooked earlier, DVCPs contributed to the daily maximum 1-hour O₃ and SOA by up to ~3 ppb and ~1 μg/m³ in November of 2019. The impacts were higher in the urban center of Shanghai and under unfavorable meteorological conditions (e.g., stagnation). This study revealed that DVCP emissions are sources that cannot be overlooked in urban centers. Considering the dynamic nature of DVCP emissions, which evolve alongside changes in formulas and our lifestyle, they need more attention in the future.

Since the residential type in urban areas have moved to the high-altitude along with increasing the number of the tall buildings, the public demand for information about atmospheric pollutants at those levels including ozone also have increased. As the first air quality monitoring station (AQM) at high altitude, the AQM at the top of POSCO Tower Songdo (37.39 °N, 126.64 °E, 305m a.s.l.) has been measured since December 2022. Because POSCO Tower Songdo is located in the upwind region of the Seoul Metropolitan Area (SMA) along with industrial complexes and large point source nearby, it is suitable location for identifying the effects from both local emission and long-range transport of ozone and precursors. During ozone alert periods (from May to June 2023), gaseous pollutants (O₃, NOx, SO₂, CO) and non-methane VOCs (NMVOCs) were measured using Air Quality Monitoring System (AQMS, T series, Teledyne API.) and Selected Ion Flow Tube Mass Spectrometer (SIFT-MS, Voice 200, Syft Tech.), respectively. Also, the nitrogen oxides (HONO and HNO₃) were obtained by Monitor for AeRosols and Gases in ambient Air (MARGA, 2060, Metrohm) We analyzed ozone formation characteristics at high altitude in SMA using the BOX Modeling eXtensions (BOXMOX) model by conducting the sensitivity test. We believed that these results could be scientific evidence for formulating ozone management policies.

This study emphasizes on the initial crucial step towards mitigating the particulate matter (PM_2.5) by scientifically quantifying and pinpointing its sources. The need for the accurate emission inventories is of paramount importance, and among the diverse methods available, ground-based activity data stands out for its high precision. However, preparing such emission inventories poses challenges as it is very resource and time intensive. This study focusses on the preparation of a detailed emission inventory for PM2.5, with a resolution of 300m × 300m, using ground-based activity data for eight non-attainment cities in Punjab, India (Jalandhar, Amritsar, Patiala, Mandi Gobindgarh, Khanna, Naya Nangal, Dera Bassi, and Dera Baba Nanak). Furthermore, using this in-house emission inventory, Emissions Database for Global Atmospheric Research (EDGAR) which is a widely acclaimed database, is updated and WRF-Chem simulations are performed, the results of which are also a part of this study. The findings of this study reveal Jalandhar is the highest contributor to PM2.5 emissions, estimated 4296 tons/year, respectively, while Dera Baba Nanak emerges as the least contributor with total PM2.5 emissions of 92 tons/year. The primary sources identified across all cities are predominantly vehicular, road dust, industrial, and domestic emissions, making them the top four contributors. Another important insight from this study is the identification of the hotspots within each city from the gridded total emission maps. The results of this study hold critical importance for policymakers, providing a basis for informed decisions and targeted actions to reduce emissions and protect public health. Ultimately, the study emphasizes the importance of thorough emission quantification and source identification in developing effective strategies for reducing particulate matter.

The OH reactivity (OHR), the reciprocal of the OH lifetime, indicates the total amount of reactive trace gases loading in the atmosphere. We examine the characteristics of OHR and the environment in South Korea during the Korea-United States Air Quality (KORUS-AQ) campaign by using the comprehensive NASA DC-8 airborne measurements. The data present that anthropogenic emissions control OHR in South Korea with significant contributions of CO, OVOCs, NO_X, and hydrocarbons showing the increase of observed total OHR (tOHR) near and downwind regions of the primary emission sources. However, we found that a 3-D chemical transport model (CTM), GEOS-Chem, underestimates the concentrations of CO and OVOCs compared with observations, thus causing significant OHR underestimation. To reconcile the discrepancy between observed and simulated OHR (missing OHR), we examine the model's processes, such as emission inventories and chemistry schemes. We first adopt the observationally constrained VOC emission inventory based on the preceding studies using airborne and satellite observations. The adopted emission inventories lead to the increase of simulated OVOCs (~16%), aromatics (~23%), and O₃ (~4%). Second, we update tropospheric chlorine chemistry to the model, resulting in the increases of simulated NO_X (~12%), O₃ (~2%), OH (~5%), and HO₂ (~2%). Furthermore, we quantify the impact of CO underestimation in the model on the NO_X-O₃-HO_X cycle and simulated OHR. As a result, we reconcile some parts of the missing OHR from ~52% to ~40%, but OVOCs and O₃ underestimation in the model still need to be figured out.

Secondary inorganic aerosols, including sulfates, nitrates, and ammonium, are known as the primary constituents of fine particulate matter (PM_2.5). Recently, the proportion of sulfates has diminished while that of nitrates has increased with the reduction in sulfur emissions due to stricter regulations. To investigate the impacts of nitrates on PM_2.5 concentration growth for pollution periods, we conducted field intensives at the Yongdang Campus of Pukyong National University, located in the coastal area of Busan, from January 2021 to June 2022. The measurement site is adjacent to a port and a small industrial complex. Measured components included water-soluble inorganic ions (PILS-IC, Metrohm Eco IC 883, Metrohm) and precursor gases (NO, NO₂, NH₃, O₃; TEI instruments). The aqueous liquid water content (ALWC) and pH were calculated using the ISORROPIA II thermodynamic equilibrium model. First, we found that nitrates played a significant role in the accumulation of high PM_2.5 concentrations, regardless of the season. This study attempted to elucidate the nitrate formation mechanism that contributed to the growth of PM_2.5 concentrations under highly polluted conditions. We further analyzed temporal variations and correlations among the concentrations of inorganic particles, excess ammonium, humidity, ALWC, and Ox (NO₂+O₃) to support the nitrate formation processes we propose here. The tentatively proposed nitrate formation process that increases PM_2.5 concentrations in this coastal site is as follows. Initially, the ALWC increased through the sea breeze system due to increasing specific humidity in the daytime, in turn, increasing relative humidity at night. Then, N₂O₅ reacted with the surface of aerosol H₂O to form HNO₃ (aq) at night, leading to the formation of nitrates. Thus, the heterogeneous formation increased the concentration of nitrates at night, which subsequently led to an increase in the PM_2.5 levels.

In urban coastal environments, mesoscale atmospheric circulation can potentially influence air quality by affecting the chemical processes of air pollutants. In this study, we measured air pollutant concentrations (NO₂, NO, and O₃) in Incheon and Ulsan, developed urban coastal areas in South Korea. Based on highly spatially resolved observations, we investigated the spatiotemporal variations of pollutants affected by local emission environments. Additionally, we examined the effects of sea breeze on surface ozone concentrations and the associated chemical processes. To verify emission environments in Incheon and Ulsan, we referred to land cover maps. Incheon is primarily traffic-oriented due to its proximity to cargo terminals, ports, and an airport, while Ulsan features extensive industrial areas along the coastline. We installed air quality sensors in representative areas for each microenvironment and conducted measurements for two weeks. We found that the variation of the air pollutant concentrations varied depending on the emission environments. Notably, an inverse correlation was observed in the NOx and O₃ changes during the diurnal pattern on weekdays, and this variation was more distinct in traffic and industrial areas. These variations of NOx and O₃ concentration were also evident during sea breeze periods. Measured O₃ near the coast was relatively high during sea breeze periods. However, in the industrial areas with high NOx emissions, O₃ concentrations showed a decreasing gradient. This suggests that NOx emissions from inland areas contributed to the reduction in O₃ concentrations. Based on the O₃ concentration gradient, we evaluated the impact of sea breeze on O₃ concentrations through quantifying O₃ advection rates, and examined approaches to the chemical budget of O₃ concentrations.

Although carbonaceous aerosols are higher health risk to humans than PM_2.5, there are still uncertainty in Physicochemical properties, transportation, and spatiotemporal distribution. This study used one year semi-continuous PM_2.5 chemical composition from the Gyeonggi Air Quality Research Center in Ansan, Korea (37.31°N, 126.80°E) to identify the major sources of carbonaceous aerosols in PM_2.5. Because Ansan is a representative industrial and urban mixture area nearby Seoul Metropolitan Area, it is suitable to monitoring the various type of PM_2.5 characteristics. Carbonaceous aerosols were analyzed using sunset OCEC analyzer based on NOISH (National Institute for Occupational Safety and Health) thermal optical transmittance (TOT) analysis protocol. Then, we classified the concentrations of organic carbon (OC; from OC1 to OC4) and elemental carbon (EC; from EC1 to EC6) detected depending on the analysis temperatures (310 °C to 870 °C). Additionally, the hourly-based 12 heavy metal components (Si, Ti, V, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Pb) in PM_2.5 was also measured using XRF (X-Ray Fluorescence spectrometer) and the major emission sources of carbonaceous aerosols will be identified using statistical approaches (such as principal component analysis (PCA) and/or a receptor model). Therefore, we believe that this research will be helpful to formulating effective regulation policies for the carbonaceous aerosols by estimating the contribution of emission sources, quantitatively.

To improve air quality, it is important to establish effective reduction and regulation policies for major pollutants by estimating their contributions and impacts on human health. For these purposes, comprehensive semi-continuous PM_2.5 measurements from the Gyeonggi Air Quality Research Center in Ansan, Gyeonggi-do (37.3202 ºN, 126.8283 ºE, 13 m above sea level), which is located in industrial and urban mixture areas, has been conducted since 2020 to identify characteristics of complicated industrial areas. We selected hourly-based chemical species of PM_2.5 from January 1 to December 31 in 2020, including inorganic ions from an ambient ion monitor (9000D AIM), trace elements from an online x-ray fluorescence spectrometer (XRF Xact 625i), and carbonaceous aerosol (organic and elemental carbon) from thermal optical transmittance analyzer (Semi-continuous OCEC Analyzer) based on a modified NOISH (National Institute for Occupational Safety and Health) Method. To estimate the contributions of sources, the Positive Matrix Factorization (PMF) receptor model was applied to PM_2.5 chemical speciation data which passed the strict QA/QC criteria. The highest source contribution was ammonium nitrate as 39.6% and followed by ammonium sulfate (21.3%), Gasoline (10.9%), Diesel (9.8%), Roadway emission (7.7%), Industry (3.8%), Coal Combustion (2.6%), Oil Combustion (1.8%), Soil (1.4%), and Nonferrous metal (1.0%). Additionally, conditional probability function (CPF) analysis was performed to understand the influence of local emissions. Most emission sources were located in the west direction where an industrial area and a highway are located. However, the diesel emission source was highly contributed from the east direction, where Ansan Bus Terminal and major commercial facilities are located. We believe the evidence of local contributions will be useful in formulating policies that improve the air quality in industrial/urban mixed areas in Gyeonggi Province, Korea.

NO₃ radical was found to play a critical role as a dominant oxidant in the nocturnal atmospheric chemistry, especially in the absence of the OH radical. It mainly oxidizes volatile organic compounds (VOCs) and NOx in an urban air mass during the night. Furthermore, N₂O₅, produced by the reaction with NO₃ radical and NO₂ in thermal equilibrium, contributes to the formation of nighttime secondary organic aerosol and the subsequent increase in daytime ozone production through a series of heterogeneous reaction. The chemistry of these reactive nitrogen oxides is significantly influenced not only by its source and sink mechanisms but also by complex weather conditions, including local and region circulations. Therefore, it is necessary to continuously monitor the chemical processes of NO₃ radical and N₂O₅ for all seasons. We performed to measure NO₃ radical in the summertime using a home-made Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS) in Seoul. Currently, we also successfully developed Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBB-CEAS) to observe N₂O₅. This study aims to simultaneously measure and characterize the NO₃ radical and N₂O₅ in the wintertime at Korea University in Seoul during the Asia-AQ campaign. Subsequent analysis will be conducted to assess the wintertime temporal variations and chemical mechanisms based on the production and loss of NO₃ radical and N₂O₅ in Seoul with the simultaneous measurements of key trace gases, particulate pollutants and meteorological parameters. The detailed data analysis will be presented during the conference.

Characterizing inflow structure is important to better represent tropical cyclone impacts in numerical models. While much research has considered the impact of storm translation on the distribution of inflow angle, comparatively less research has examined its distribution relative to the environmental wind shear. This study analyzes data from 3655 dropsondes in 44 storms to investigate the radial and shear-relative distribution of surface inflow angle. Emphasis is placed on its relationship with intensity change. The results show that the radial variation in the inflow angle is small and not significantly dependent on the shear magnitude or intensity change rate. In contrast, the azimuthal distribution of the inflow angle shows a significant asymmetry, with the amplitude of the asymmetry increasing with shear magnitude. The maximum inflow angle is located in the downshear side. The degree of asymmetry is larger in the outer core than in the eyewall. Intensifying storms have a smaller degree of asymmetry than steady-state storms under moderate shear.

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Taiwan island is one of the key regions that is highly exposed to TCs in the western North Pacific. Here we utilize the mainstream reanalysis datasets in 1979–2013 and propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TC landfalls on Taiwan island based on the environmental factors in the preseason. The comprehensive predictor sampling and multiple linear regression shows that the 850-hPa meridional wind over west of the Antarctic Peninsula in January, the 300-hPa specific humidity at the open ocean southwest of Australia in January, the 300-hPa relative vorticity at the west of the Sea of Okhotsk in March and the sea surface temperature in April at the South Indian Ocean are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.87. The model is validated by the leave-one-out cross validation and recent 9-year observations (2014–2022). The prediction of the SYSU Model exhibits a 98% hit rate in 1979–2022 (43 out of 44), suggesting an operational potential in the seasonal forecasting of TCs landfall on Taiwan island.

This study utilizes the HighResMIP-CMIP6 data to investigate future changes in tropical cyclone (TC) activity, including TC genesis frequency (TCGF) and track density (TCTD), in response to El Niño-Southern Oscillation (ENSO). The TC activity changes from uncoupled (atmosphere-only) and coupled runs are very diverse, indicating large uncertainty for future projections. Full-coupled models projected an El Niño-like Sea surface temperature (SST) warming over the equatorial Pacific, while uncoupled (atmosphere-only) models reveal a La Niña-like warming pattern. This SST difference further induced diverse atmospheric circulation anomalies, eventually contributing to distinct TCGF and TCTD changes during ENSO phases for future projections. Further detailed analysis reveals that the El Niño-like or La Niña-like warming patterns are two extreme cases. It shows a strong variance of simulated SST across different models. The accurate projection and simulation of central Pacific SSTs and Eastern Pacific SSTs is the key to reducing the diverse behaviors of TC activity among these models. We detected three crucial regions, that is the central Pacific, the subtropical eastern North Pacific, and the equatorial eastern Pacific, that could modulate TC activity in response to ENSO in numerical models. These results help to further improve the accuracy of models to simulate future TC changes.

We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018–19 in the western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days; however, from 4- to 3-day lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More “Fail” cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In “Hit” cases, 850-hPa streamfunction and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit–Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa streamfunction. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of background flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrant further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for TCG.

Typhoons affecting the Korean Peninsula are getting stronger and dying down (WMO, 2021; IPCC, 2021; Na and Jung, 2021). The National Oceanic and Atmospheric Administration (NOAA) and the National Weather Service (NWS) have established disaster prevention and changed the national management policy stance by providing information on risks in advance about the effects of extreme weather disasters, responding to them, and taking action in advance. A paradigm shift was attempted by declaring the Weather-Ready Nation (WRN), a national strategy to minimize damage, as a strategic plan for the next 10 years in 2011. Starting with the declaration of paradigm shift of WRN, various countries around the world (Britain, France, Japan, China, etc.) have conceptualized the words READY and PREPARE in relation to tropical cyclones such as typhoons and hurricanes, establishing disaster prevention. In addition, with the help of the World Meteorological Organization, several developing countries in East Asia (Philippines, Vietnam, Thailand, etc.), which are heavily affected by typhoons, are also actively introducing impact forecasts (Katie et al., 2016; Nguten et al., 2021; Nofal et al., 2021; Thuc et al., 2022; Do and Kuleshov, 2022).This study aims to evaluate the possibility of typhoon impact prediction in Korea by utilizing the risk index calculated by the Typhoon-Ready System, which was developed to establish the basis for typhoon impact prediction in Korea. The possibility and validity of the risk index were analyzed through the analysis of representative typhoon cases applying the complex weather disaster risk index based on impact prediction, and in particular, the possibility of typhoon impact prediction in Korea was evaluated through the risk level proposal for regional-specific risk indices, which are the core of impact prediction. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.RS-2023-00212688).

Genesis Potential Indices (GPIs) indicate the potential of the Tropical cyclone (TC) occurrence in response to large-scale environmental conditions. Recent studies use simulated GPIs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate the physical explanation of projected global TC changes. In this work, we first evaluate the ability of CMIP6 historical simulations (1979-2003) to capture long-term variations in GPIs (dynamic GPI and Emanuel GPI) compared to the GPIs from observational data sets. The presence of inherent biases in the models prevents both the GPI simulations and the individual terms of GPIs from adequately reproducing the observed variation. Thus, bias correction was conducted via direct approach using univariate (quantile delta mapping; QDM) and multivariate (N-dimensional probability density function transform; MBCn) methods and component-wise with QDM on CMIP6 simulated GPIs in historical and future projections (2075-2099) under high emission (SSP5-8.5) scenario. Overall model evaluation on all BC methods in historical GPI simulations under the direct bias correction exhibits a slightly better performance than the component-wise approach. Even though we observed no significant difference between the future changes from GPIs from bias-corrected CMIP6 and the raw models, we observed substantial model uncertainty reduction in the corrected models, emphasizing the improved reliability of the models for future projections. This study provides a comprehensive assessment of bias correction techniques used in CMIP6 models for GPI simulation, along with the projected potential for TC formation.

This study investigates the large-scale circulation anomalies induced by straight-moving tropical cyclones (TCs) over the western North Pacific (WNP) during winter. Corresponding to the straight-moving TCs, a quasi-stationary wave train is excited as alternative geopotential height anomalies in the upper troposphere stretching from East Asia to the North Pacific. Specifically, the anomalous anticyclones are initially formed over East Asia to the north of TCs and then lead to the subsequent anomalies in the downstream areas. Further analysis reveals that the upper-level anticyclonic anomalies are excited by negative Rossby wave sources, which are mainly attributed to the poleward vorticity advection by anomalous divergence relevant to TCs. In addition, the diagnosis indicates that the generation of wave source is caused by the product of the TC-induced divergent flows and the prominent meridional vorticity gradient in association with East Asian upper-tropospheric westerly jet. The above processes differ from the recurving TCs in summer and autumn, which undergo extratropical transition when they move northward into the mid latitude. These findings imply that the tropical disturbances over the WNP, such as straight-moving TCs, can remotely affect weather over the extratropics, and thus have implications for improving the weather forecast over the extratropics through improving tropical disturbance forecast.

Moderate tropical cyclone precipitation (TCP) is of great significance to regional water resource supply, while extreme TCP could bring significant adverse impacts to ecosystems and society, especially when tropical cyclones intensify rapidly, leaving no time to take prevention actions. Whether rapid intensification (RI) of tropical cyclones (TCs) affect TCP in both land and ocean remains unknown. Here we classified TCs which have undergone increases in the maximum sustained wind speed (MSW) by at least 30 knots within 24-h into RI category. We analyzed TCP totals provided by daily precipitation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and spatial extent from 1983 to 2019 in the four categories based on regions (land and ocean) and RI-experiencing characteristics (with- and without-RI). TCP totals and spatial extent was identified by the restricted moving neighborhood method and semi-variogram framework. The results show that TCP totals on the ocean are larger than those on the land, since RI-experiencing TCP are higher than TCP without RI experiencing, although RI processes tend to increase TCP totals in the extremely high percentiles more significantly on land than ocean. The four regions of the Northeast Pacific Ocean (EP), South Pacific Ocean (SP), Northwest Pacific Ocean (WP), and North Atlantic Ocean (NA) show increases in regional mean and extreme TCP totals. The highest increase in the extreme TCP totals (0.37 mm day−1 year−1) over the NA region occurs in the RI_ocean category, which is 2.6 times the average positive enhancement trend across all basins. The increasing rate of the extreme TCP totals over the WP region is higher in track points with RI-experiencing than without RI-experiencing. The category of RI_land over the regions of NA, EP and SP shows a significant increase in the regional mean TCP spatial extent.

The South China Sea (SCS) is the largest marginal sea in the Northwest Pacific Ocean, and it encounters frequent typhoons. The atmosphere and ocean will create significant thermal and dynamic responses during the intense disturbance caused by typhoons. However, these responses have not been thoroughly investigated owing to the complicated marine environment. According to the satellite data, the SCS Basin was observed to have a strong SST response to Typhoon Mangkhut, resulting in widespread SST cooling. A coupled model was used to investigate the atmospheric and oceanic responses to Typhoon Mangkhut. Best-track data, satellite SST, and ARGO measurements show that the coupled WRF-CROCO simulation displays better track, intensity, SST, temperature, and salinity profiles than those of the WRF-only simulation. Results show that the typhoon induced rightward intensifications in wind speed, ocean current, and SST. The following are some remarkable atmosphere and ocean responses: (1) the SST below the inner-core region is cooled by 1°C, resulting in a 37%–44% decrease in wet enthalpy, and the central pressure is increased by ~9 hPa. Therefore, the changes in SST below the inner-core region of the SCS Basin have a significant impact on air-sea fluxes under high-wind conditions. (2) the ocean boundary layer analysis shows that near-inertial oscillations on the right side of the typhoon track and a strong inertial current up to ~2.28 m/s in the upper ocean were observed, which resonated with the local wind and flow field on the right side and induced strong SST cooling. (3) a decrease in SST decreased the moist static energy of the typhoon boundary layer, thereby weakening the typhoon’s intensity. The difference in equivalent potential temperature and sea surface pressure have a good correlation, indicating that the influence of moist static energy on typhoon intensity cannot be overlooked.

This study examines the characteristics of PBL for landfalling TC cases using data from the Boseong Tall Tower. The Boseong Tall Tower, the second highest multi-level meteorological tower in Asia, provides vertical profiles of the lower planetary boundary layer (PBL), enabling investigation of the characteristics of the lower PBL over land. It is particularly useful when a tropical cyclone (TC) makes landfall, as observations with sonde over land can be difficult. Previous studies have suggested that the track, intensity and structure of a TC are influenced by turbulent processes in the Planetary Boundary Layer (PBL), and the parameterization of the TC boundary layer used in the ocean may not match well with land. Therefore, it is important to understand the characteristics of the PBL over land, especially when TCs make landfall, for better TC prediction and prevention. Based on our analysis, it appears that the log layer may not accurately represent the depth of the surface layer when assuming a near-neutral surface layer. This suggests that alternative assumptions may be necessary. In contrast to previous studies, our findings indicate that the fitted surface frictional speed (u_*s) slightly increases with wind speed, while the fitted roughness length (z₀) decreases with mean wind speed. These suggest that stronger surface wind speeds result in a wider range of wind speeds, which may be influenced by ocean surface features due to the proximity of the Boseong Tall Tower to the coastline. Additionally, we compare the vertical eddy diffusivity (K_m) as measured by observation and the parameterization used in the numerical model. [Funding. This study was supported by the Korea Meteorological Administration Research and Development Program (KMI2022-01312)].

A prediction system for wind and precipitation during typhoons affecting the Korean Peninsula was developed based on the characteristics of wind and precipitation areas along the track of past typhoons, and to improve the accuracy, sensitivity experiments were conducted based on the selection criteria for similar past typhoons. As a result of Typhoon KHANUN by selecting similar typhoon cases in the past, the wind prediction was good result, but precipitation prediction was relatively inaccurate. To improve the observation-based typhoon prediction system, we conducted a prediction experiment using an ensemble numerical model. Among the predicted ensemble members, we selected those whose typhoon centers were within 50 km of the official typhoon information and then averaged the precipitation/wind forecast fields. The wind prediction results showed over estimated, while precipitation prediction showed significant improvement in accuracy compared to observations.

The decrease in sea surface temperature (SST) with latitude is one of the factors that weakens the intensity of typhoons moving towards mid-latitude. Atmosphere-only models (i.e., WRF) with high horizontal resolution which use prescribed SST for low boundary conditions sometimes overestimate the intensity of decaying typhoons in mid-latitude. The air-sea interaction reduces this overestimate of typhoon intensity by producing realistic SST cooling below the typhoon. However, excessive SST cooling in coupled models often occurs and this can decrease the typhoon intensity. In this case, ocean vertical structure may be associated. We investigate the effect of air-sea interaction and the vertical structure of ocean below typhoon BAVI in 2020 by comparing 3-km high-resolution WRF and WRF-ROMS coupled model. For the WRF-ROMS experiment, 3D ocean data is necessary, and the HYCOM analysis data is usually used. However, the salinity of HYCOM in East China Sea has a large uncertainty because HYCOM does not fully reflect the Changjiang river freshwater. So we conducted WRF-ROMS simulation with Changjiang river discharge to create realistic salinity input for typhoon BAVI simulation. As a result, WRF with prescribed HYCOM SST overestimates the intensity of typhoon BAVI, but WRF-ROMS with HYCOM salinity underestimates the typhoon intensity. WRF-ROMS with realistic salinity shows the salinity in East China Sea is lower than normal due to high Changjiang river discharge, and the freshwater inflow makes ocean stratification in East China Sea. Ocean stratification makes a barrier layer and reduces the vertical mixing and SST cooling under typhoon BAVI, increasing typhoon intensity.

Tropical cyclones (TCs) in the western North Pacific (WNP) follow two distinct tracks: some move in a west-northwest direction, while others recurve in a north-northeast direction. Recurving TCs possess a major disaster risk for the densely populated coastline of Korean Peninsula and Japan. While there have been studies on recurving TCs in the WNP, the statistical characteristics of the recurving TCs and whether their features are changing due to climate change have not been thoroughly investigated to date. Apart from that, there are discrepancies in extracting the recurving TCs in this basin. Therefore, this study proposes a new methodology comprising a set of criteria to realistically extract recurving TCs in this basin. TCs extracted based on proposed new criteria, we investigate statistical characteristics such as the seasonal distribution and trend, recurving location (latitude and longitude), the number of occurrences, lifetime maximum intensity (LMI) and recurving time nexus, translation speed, and the recurving angle of recurving TCs during the period 1981–2021 among others. Moreover, we further examine the reasons of varying frequencies of recurving TCs in different years. Results suggest that position and strength of WNP subtropical high (WNPSH) plays a pivotal role in recurving TCs varying frequencies in WNP. Finally, we investigate whether recurving TCs characteristics change over time. These findings will contribute to understanding the characteristics of recurving TCs in the WNP, especially in the face of climate change.

This study investigates the water vapor transport to the Tibetan Plateau (TP) induced by Bay of Bengal (BOB) tropical cyclones (TCs) in May during 1979-2019. The water vapor around the BOB TCs can be transported to the south of the TP by the southerlies at the upper troposphere east of the TC center. Whether the water vapor can cross the southern boundary of the Tibetan Plateau (SBTP) may rely on the different configurations of BOB TCs and long-wave trough and ridge. We found that the water vapor may (may not) cross SBTP if the TC’s wind field converges with the westerlies in front of the trough (ridge). The physical mechanism is explored according to the momentum equation. When the TC moves northward around the SBTP, its wind field could interact with the trough (Type-T TC) and ridge (Type-R TC) in the westerlies. In the area close to the trough line (ridge line), the meridional acceleration is positive (negative), hence meridional component of velocity increases (decreases). Therefore, it is more favorable for the water vapor transported northward if a Type-T TC enters the westerlies from the area close to the trough line than the ridge line. The simulation of the BOB TC cases configuration using WRF model confirms the importance of the relative position between the Type-T TC and the SBTP to the water vapor transport over the SBTP. For the Type-T TC, the meridional moisture budget over the SBTP is positively correlated with the TC intensity and negatively correlated with the distance between the TC center and the SBTP. However, for the Type-R TC, the meridional moisture budget is not related to the TC intensity and the distance. The configuration of TC and long-wave trough or ridge may be predictable through the intraseasonal oscillation activity in the eastern BOB and the southern Arabian Sea. May and October to December are the bimodal phases of BOB TC frequency, which decreases month by month from October to December and is relatively low in May. However, the contribution rate of MMBa is the highest in May. Seasonal variation in the meridional position of the westerlies is the key factor affecting the contribution rate. The relatively south position of the westerlies in November and December results in the lower contribution rate of MMBa.

This study evaluates potential sites for wind power generation in the Taiwan Strait and inland areas of Taiwan, employing data from multiple satellites, reanalysis, and meteorological stations. The calculation of wind energy is based on transforming these wind speed data to the elevation of wind turbines, considering blade surface area and other coefficients for potential electrical energy estimation. In offshore areas, water depth determines the suitability of fixed or floating wind turbines, with depths exceeding 60 meters being suitable for floating turbines, and shallower waters being appropriate for fixed ones. Inland Taiwan's wind patterns are influenced by topography and buildings. Due to the stability of the wind, coastal areas emerge as prime locations for wind turbine installation. The spatiotemporal characteristics of wind at each potential site are the primary considerations in this assessment. Wind speeds in the Taiwan Strait range from 4 m/s to 15m/s, typically higher in winter. According to the Meteorological Information Based Green Energy Operations Center in Taiwan, the effective wind speed range for turbines is 3 m/s to 25 m/s, which means that wind turbines can operate throughout the year in the Taiwan Strait. Additionally, climatic-scale variations in wind speed are also considered. From 2012 to 2023, an annual increase of approximately 0.15m/s was observed in February, April, May, October, and December in the Taiwan Strait. Comprehensive assessments indicate that the central offshore areas of the Taiwan Strait exhibit higher wind energy potential, with water depths conducive to fixed wind turbines.

The demand for long-term climate analysis is increasing with the flow of global climate change. Accordingly, the need for Level 3(L3) data from satellite observation has also increased in climate studies. In particular, the Geostationary Environmental Monitoring Spectrometer (GEMS) mounted on geostationary satellites is highly evaluated in East Asia, and it is time to develop a L3 algorithm tailored to the model grid to effectively utilize GEMS for climate analysis. Therefore, this study developed a long-term climate analysis data production algorithm based on GEMS data. Data for January 1, 2022, were estimated using GEMS AOD, NO₂, and total O₃ data. To consider the homogeneity of data, all data that produce L3 data was collected by using only Full Mode Scan. Data quality correction was performed by applying Cloud Fraction and Final Algorithm Flag. In addition, we used the area weighted average calculation method to achieve stable production. In this data production, the most used spatial resolution, 0.25° X 0.25°, was adopted, and using this data, the combination with other satellites for long-term data production will be conducted.

The Tibetan Plateau Vortexes (TPV) can trigger mesoscale convective systems (MCSs) under appropriate conditions. These MCSs not only contribute most of the precipitation in the plateau area, but also move eastward along with TPV under appropriate conditions, triggering a series of mesoscale convective activities along the way. These precipitation events are often intense and long-lasting, often causing serious flooding disasters. However, due to the lack of observation data and complex terrain over plateau areas, the study of weather systems in plateau areas has always been a challenge. This work utilizes Fengyun-4 meteorological satellite and reanalysis data to study the cloud top structure characteristics of plateau vortices with different movement paths. A comparative analysis was conducted on the differences in cloud top height, cloud top temperature, cloud top brightness temperature, and brightness temperature difference of different satellite channels among three paths of plateau vortices: Colonel in situ, moving eastward but not descending to the plateau, and moving eastward beyond the plateau. The influence of these variables on the movement path of plateau vortices was also discussed.

The conventional emission estimation for air quality simulation was based on the bottom-up method, which relies on the accuracy of the emission inventory. During Taiwan's winter and spring seasons, the northeasterly monsoonal wind prevails, which transports the air pollutants from East Asia to Taiwan. However, the updates of emissions in East Asia is slow, making it challenging to promptly capture changes in emission sources. This leads to forecasting bias during transboundary pollution events. Recently, more real-time and high-resolution satellite data have been used to constrain the bottom-up estimated emissions. This study aims to use TROPOMI satellite data to update the bottom-up estimated emissions in East Asia. This correction intends to address the issue of underestimated transboundary air pollutants in Taiwan. Through a mass balance adjustment of emissions derived from satellite and model-retrieved NO2 column densities, the adjusted NOx emissions are higher than the bottom-up estimated emissions, which furthermore enhances the model performance of the transboundary pollution event.

As extreme weather events such as heatwaves and wildfires increasingly occur worldwide due to climate change, efforts are underway to reduce greenhouse gas emissions. Among various greenhouse gases, methane significantly impacts climate change, with a global warming potential approximately 82.5 times that of CO₂ over a 20-year period. Reducing methane gas emissions, which have a relatively potent warming effect, is essential for mitigating climate change, and continuous monitoring is crucial for identifying and managing sources and amounts of emissions. Satellites can periodically observe the same areas, enabling continuous monitoring of methane emissions, and their global coverage allows for the detection of methane gas in extensive or inaccessible areas. The Sentinel-2 satellite, with its short revisit cycles and high-resolution imagery across various spectral bands, is useful for methane gas detection. Previous research has conducted methane detection studies on well-known methane emission sources using the shortwave infrared (SWIR) bands of various satellites. In this study, we utilized the Google Earth Engine (GEE), which allows for rapid analysis of satellite data in a web browser, to leverage the short revisit cycles and broad spectral band coverage of the Sentinel-2 satellite. We detected and monitored methane plumes from the 2.8 Jikdong Coal Mine located in Suncheon, South Pyongan Province, North Korea. The results of this study are expected to provide important data for the establishment of environmental policies and strategies for climate change mitigation. Furthermore, it demonstrates the practicality of remote sensing technology in methane emission monitoring and highlights the potential use of Sentinel-2 satellite data in climate change studies.

Total Precipitable Water(TPW), a column of water vapor content in the atmosphere, is an important meteorological factor and play a critical role in the occurrence of precipitation and atmospheric river. The current TPW algorithm operational at NMSC calculates TPW based on AMI Aerosol Profile(AAP) in for clear-sky areas, and uses values calculated from numerical weather prediction (NWP) for cloudy-sky regions. The validation results for this algorithm with radiosonde show an RMSE 6.9mm and a bias of 0.377mm. To fuse the distribution of TPW in all sky conditions, this study proposes a retrieval algorithm based on the light gradient boosting machine (LGBM) model using GK2A/AMI data, Radiosonde, ERA5, and three NWP models data. Through statistical verification, with a root mean square error (RMSE) of 6.697~7.056mm, a bias of -0.929~0.377, and a Pearson's R of 0.942~0.946. We utilize the previously generated NWP prediction field to retrieve TPW in real-time. Detailed introduction of new TPW will be presented in the conference. This research was supported by the “Technical development on high-impact weather detection and prediction using meteorological satellite data”(KMA2020-00121) of “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” project funded by the National Meteorological Satellite Center, Korea Meteorological Administration.

The theoretically calculated radiance (B) in the process of utilizing satellite data into numerical weather prediction models plays an important role in various processes. For example, the difference between observed data O and calculated B is used to understand the error characteristics of the observed data or to determine whether clouds are present in the observed data. Therefore, accurately calculating B is one of the most basic processes for the use of satellite data. To achieve this, various efforts are being made, such as improving the accuracy of radiative transfer models as well as accurately inputting surface characteristics. In this study, we focus on using temperature and humidity profiles along the slant path considering the direction in which satellite data is obtained, rather than the vertical path for the input of the radiative transfer model. Previous studies on clear-sky conditions have shown that applying a slant path result in a noticeable deviation when the viewing angle is far from nadir and in regions with high variability in atmospheric temperature and humidity. This study extends this to investigate the effect of the slant path radiative transfer model in the presence of clouds. When the horizontal range of clouds is not extensive, the presence of clouds varies depending on the chosen path. Furthermore, application of the slant path explicitly considers the parallax effect of clouds in the simulation process, allowing for a more accurate calculation of B values. Taking all these factors into account, we aim to analyze the impact of slant path simulations on vertical temperature and humidity retrieval by analyzing the results of 1DVAR models for all-sky cases.

Heavy precipitation events are of great importance as large amounts of rainfall concentrated in a short duration can generate severe natural disasters, which then cause widespread damage to human societies. Many previous studies have shown an increasing trend in the atmospheric water vapor due to global warming, readily indicated by the Clausius-Clapeyron relation. However, most of studies on the occurrence frequency of heavy precipitation are generally conducted on regional or national scales.In this study, we use three satellite-based precipitation estimation products—Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Global Precipitation Climatology Project (GPCP)—and one rain gauge-based precipitation product, NOAA Climate Precipitation Center (CPC), and examine the global trend in occurrence frequency of heavy precipitation from 2001 to 2020. Using the number of days when the daily precipitation amount exceeds the 95th percentile of precipitation amount (FP95) and utilizing the Mann-Kendall test, Sen’s slope estimator, linear regression, and innovative trend analysis, we find that the trends of FP95 from the various analysis methods are generally comparable but highly vary depending on the precipitation products. The trends of FP95 in the lands from IMERG and GPCP are generally similar with those from CPC. However, the trends in the oceans are noticeably inconsistent across all the products. The FP95 from GSMaP increases significantly both in the lands and in the oceans except for some regions, but those from GPCP are decreasing on most of the oceans. Because satellite-based precipitation estimation products derive the amount of precipitation indirectly from atmospheric elements such as water vapor, liquid water path, and cloud top temperature, trends in the atmospheric parameters provided by a polar orbiting satellite are also analyzed to explain the discrepancies in trends of FP95 across the precipitation products.

Heavy rains occurring in a short time can cause significant damage, but it is difficult to accurately predict or observe its occurrence and dissipation. In this study, we investigated the possibility of understanding the development of convective precipitation clouds using thermodynamic variables (such as lifting convective level (LCL), level of free convection (LFC), stability index, etc.) with a 10-minute interval and 2 km spatial resolution produced by GK-2A. The accuracy of these variables produced by GK-2A is estimated by comparing with the collocated radiosonde data. In case of stability indices, the weighted linear regression coefficients for KI, LI, SSI, and TTI all showed values above 0.7, and the errors were randomly distributed without bias. The error for SSI and LI were larger at night than during the day, and the overall pattern of differences between satellite and radiosonde products was similar across stations. Furthermore, an analysis of 21 significant outlier cases that occurred during the comparison revealed that all outlier cases corresponded to one or more of the following: nighttime near-surface PBL, near-surface inversion layer, or instability in temperature and humidity profiles in the radiosonde data. LCL deviation ranged widely from -120 to 70, and RMSE varied between 30 and 130 based on initial ascent altitudes. Using 962.25 hPa as a reference, the deviation was -3.24, with an RMSE of 31.77, indicating the highest accuracy. Derived temperature and humidity profiles from GK-2A align closely with radiosonde data under suitable ascent altitudes. In selected convective cases, the LCL decreased by approximately 70.94 hPa (0.23 hPa/min) in the 6 hours before convective cloud formation, confirming a link between higher vertical convection altitudes and a heightened potential for strong convection. We also plan to present additional analysis on the relation of LFC and CAPE to the convective precipitation.

Convective clouds, accompanied by heavy rainfall and thunderstorms, can lead to substantial damages. Hence, the detection of Convective Initiation (CI) plays a crucial role in weather monitoring and prediction. While radar observations mainly detect precipitation particles in the mature stage of convective clouds, satellite data can identify convection cells before the formation of precipitation particles. Satellite data also offers the advantage of covering a wide area with high temporal resolution. In this study, we aimed to predict convective initiation by adopting the U-Net-based LightningCast model, originally developed for lightning prediction by NOAA/CIMSS, as our foundational model. Since the GEO-KOMPSAT-2A (GK2A) satellite lacks a lightning detection sensor like Geostationary Lightning Mapper (GLM), we employed ground-based radar reflectivity as ground truth. After aligning radar echoes with the GK2A Korean Peninsula area, we selected radar reflectivity that exceeded 35dBZ at least once within 60 minutes as our target data. We utilized observation data from GK2A Advanced Meteorological Imager (AMI) during the summer as input and conducted a study to identify the optimal channel combination through sensitivity testing. This research was supported by the “Technical development on high-impact weather detection and prediction using meteorological satellite data”(KMA2020-00121) of “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” project funded by the National Meteorological Satellite Center, Korea Meteorological Administration.

Prediction of wind vectors in the low troposphere is beneficial in many application fields. Conventionally, Supervisory Control and Data Acquisition (SCADA) has been used for the low-level (1000 hPa) wind estimation. On the other hand, a recent study has developed the algorithm that can predict clear-sky high-level (500-750 hPa) wind vectors a few minutes to hours ahead using the Korean geostationary satellite (GK2A) water vapor channels only. Here we find the correlation between the two level wind fields from the GK2A and SCADA by using Convolutional Long Short-Term Memory (ConvLSTM) model. Then this model is applied to predict low-level wind fields for the entire Korean Peninsula. Our results show that our low-level wind fields have higher accuracy than those from the numerical weather prediction. Also shown is a potential that GK2A high-level wind can be used to predict low-level wind where the observations are unavailable. We expect that this study can serve to improve weather and wind power prediction.

The Rapid-Scan Atmospheric Motion Vector (RS-AMV) provides satellite-based three-dimensional wind information, and it can be useful information for initializing numerical weather prediction(NWP) model through data assimilation. In addition, It can be useful for a forecaster to be tracking the convection cell and analyzing wind information around the typhoon. Advanced Meteorological Imagery (AMI) on board the GEO-KOMSAT-2A(GK-2A) can capture the imagery of East Asia every two minutes. With this advantage, the RS-AMV can be generated with better spatio-temporal resolution than AMV. RS-AMV is derived as following steps: 1) target selection via optimal method using largest standard deviation in specific area, 2) height assignment based on cross-correlation with Cloud Top Pressure (CTP) product, 3) calculate displacement between the similar pixels in earlier or later image, 4) assign quality information on each vectors in RS-AMV via comparison with surrounding pixels. In this study, the quantitative and qualitative evaluations were performed using in-situ observation and analysis fields from NWP model. As the results of the quality assessment, we found that the major issues of RS-AMV are height assignment and wind speed. Additionally, the cause of the problem and which parts of the algorithm caused the problem were presented. This study was carried out with the support of the Korea Meteorological Administration R&D program[Meteorological satellite forecast support and convergence service technology development] (KMA2020-00121).

Water vapor imagery is a suitable satellite data to track upper atmospheric flow. It is useful for analyzing synoptic meteorological phenomena as tracking cyclonic flows, vorticities, dry areas and wet areas that appear in water vapor images, and identifying the location of troughs/ridges by utilizing the strengthening and weakening tendencies of dry areas and anticyclonic air flows. Synoptic meteorological phenomena can be analyzed extensively using water vapor image. Infrared and visible images are useful for analyzing clouds distributed in the upper, middle, and lower layers of the atmosphere. In water vapor images, upper-level waves and darkening-related troughs, which are precursor stages of low pressure system that cannot be seen in clouds that appear in visible and infrared images, can be identified through air flow analysis. In this study, we specifically showed some methods to analyze troughs through the tendency of dry areas to strengthen or weaken using water vapor imagery. Through this study, we found that the troughs are located right in front of the maximum brightness temperature area of the dry zone and the ridges are located in the area where the wet region begins to gradually dry in the water vapor image, and where the clouds begin to dissipate in the infrared and visible images.

Hyperspectral infrared (IR) sounder measures radiances at CO₂ and H₂O bands with high spectral resolution, and these measurements can be used to retrieve atmospheric temperature/ humidity profiles which are important in short-term weather forecasts. GIIRS (Geostationary Interferometric Infrared Sounder) of FY-4B is the first hyperspectral IR sounder in the geostationary orbit, and measures wide area with high temporal resolution. GIIRS-measured radiances contain vertical information on atmospheric CO₂ and H₂O, allowing to estimate atmospheric temperature/humidity from space. Of course, development of relevant retrieval algorithm is necessary. Improving weather forecasting capabilities, the Korean National Meteorological Satellite Center developed a physical algorithm so-called AAP (Advanced Meteorological Imager (AMI) Atmospheric Profile) for the retrieval of atmospheric thermodynamical properties. The AAP algorithm is based on the 1D-Var scheme and was originally developed for GEO-KOMPSAT-2A AMI sensor. In this research, sensitivity tests were conducted to find the best set of GIIRS channels and observation/background error covariances. Then obtained results were used for adapting the original AAP algorithm for GIIRS sensor. It was found that the modified AAP algorithm performs well with 36 GIIRS channels. Results indicate that the rms error of the modified AAP algorithm compared with ERA5 atmospheric profile is reduced when the observation error is prescribed as larger value. In addition, artificial intelligence algorithm was also developed for temperature/humidity retrievals by training GIIRS-measured radiances with collocated ERA5 atmospheric temperature/humidity profiles. ANN, CNN, ResNet, and U-Net methods were used, and results were compared for that purpose. GIIRS-measured TBs were directly related to ERA5 profiles without any first guess profile. The test results show that the algorithm based on ResNet yields the highest performance compared to the other algorithms. However, it is noted that these neural network algorithms perform not better than the physical algorithm in terms of rms errors.

Geostationary Ocean Color Imager – II (GOCI-II) onboard GEO-KOMPSAT-2B has operated since 19 February 2020 and has monitored aerosol optical depth (AOD) over East Asia. Although GOCI-II has provided invaluable AOD datasets such as hourly measurements, biases of GOCI-II AOD are not sufficiently analyzed to exploit GOCI-II AOD data in a data assimilation system for air quality forecast. This study aims to analyze the characteristics of GOCI-II AOD data in terms of bias and root mean square (RMS) error from December 2021 to January 2023, by comparing GOCI-II data with aerosol optical properties derived from the AErosol RObotic NETwork (AERONET) data. To make comparable datasets between GOCI-II and AERONET, GOCI-II AODs were averaged within a square grid box centered on each AERONET site having size of x^o. The sensitivity test was conducted by increasing the grid box size x from 0.1˚ to 1.0˚ with the increment of 0.1˚. The bias of GOCI-II AOD to AERONET AOD depends on the selected grid box size in most AERONET sites, showing smaller biases as grid box size decreases, possibly due to data representativeness. It is expected that there will be certain threshold at which bias will not decrease further. In addition, bias of GOCI-II AOD according to the aerosol type was examined. Here, aerosol types were classified into 6 categories: dust, non-absorbing coarse, mixture, high-absorbing fine, moderating-observing fine, and non-absorbing fine. AOD over the eastern China showed higher value compared to neighboring countries during the study period. The characteristics of GOCI-II AOD identified by this study will be utilized to improve a design of pre-processing process of satellite-derived AOD for data assimilation.

Atmospheric Motion Vector (AMV) is a satellite product that tracks atmospheric current by monitoring the movement of clouds and water vapor in three sequential satellite images. It is valuable for enhancing the precision of forecast and analysis fields in numerical model data assimilation. Additionally, it's crucial for understanding wind structures in typhoons. The National Meteorological Satellite Center (NMSC) from Korea Meteorological Administration (KMA) is currently producing Rapid-Scan (RS)-AMV, which has an improved spatiotemporal resolution than AMV, using the rapid-scan data from Geo-Kompsat-2A (GK2A). This output features a height assignment technique that utilizes Cloud Top Pressure (CTP) to obtain local wind information with less reliance on numerical weather prediction (NWP) models. In this study, we focused on increasing the vector count by computing vectors through optical flow. We analyzed atmospheric current using these vectors and validated the accuracy of the optical flow AMV by comparing them with RS-AMV. Optical flow is a technique that calculates the movement of pixels between two image frames as vectors. we used the Farnebäck algorithm, which calculates this movement for all pixel values in the image. Height assignment using GK2A Cloud Top Pressure (CTP) was applied to the optical flow AMV. The optical flow AMV calculated in this way was verified qualitatively and quantitatively through case analysis and observation data such as radiosonde and wind profiler.

The GK-2A AAP (Geo-Kompsat-2A AMI Atmospheric Profile) and other 1D-Var atmospheric profile retrieval algorithms have common limitations related to ill-posed problems due to a lack of observed information, leading to unstable retrieved profiles or reliance on initial guess profiles. In our study, we addressed these challenges by employing Empirical Orthogonal Functions (EOFs) and Principal Components (PCs) for temperature and humidity profiles to mitigate the ill-posed equation problem through variable reduction. The number of components of vertical profile was reduced using main EOFs and PCs. Furthermore, we modified the GK-2A AAP based on Principal Component Analysis (PCA-AAP) to achieve optimized atmospheric vertical information. The temperature and humidity profiles were calculated using PCA-AAP, with the Unified Model (UM) and GK-2A radiance serving as inputs, mirroring the approach employed in GK-2A AAP. Validation of the PCA-AAP retrieved profiles was conducted using radiosonde data in August 2022. The accuracy of PCA-AAP was found to be nearly comparable to that of both the initial guess and GK-2A AAP. Notably, PCA-AAP successfully avoided retrieving inaccurate profiles observed in GK-2A AAP, attributed to the stabilizing effect of main EOFs. The results demonstrate that the PCA technique complements the GK-2A AAP algorithm, providing stable and continuous vertical profiles. Consequently, we plan to enhance the accuracy of EOFs by incorporating global radiosonde data and model analysis data. This improvement is anticipated to contribute significantly to supporting very short-range forecasting and nowcasting more effectively. This research was supported by the “Technical development for utilizing meteorological satellite data for numerical weather predication”(KMA2020-00122) of “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” project funded by the National Meteorological Satellite Center, Korea Meteorological Administration.

Extreme weather often causes major disasters, such as the flash flood brought by intense rainfall. To assess rainfall position and intensity, we usually use satellite images and radar reflectivity as a reference. Radar reflectivity provides high-resolution information of rainfall structure, but the range that radar can detect is limited based on the location and range of each radar site. In remote areas or places such as the Pacific Ocean, the radar data cannot cover. In contrast, satellite images offer a broader range of coverage, providing information in a global scale. Therefore, we propose a machine learning framework to train models that combines radar reflectivity and satellite image data. Firstly, the visible light and infrared data are obtained from the Himawari-9 satellite in Taiwan area from July to December, 2023. Then, the radar reflectivity is provided by the Taiwan Central Weather Administration (CWA) in Taiwan area as labels. In this study, the U-net model captures the features of satellite images to predict rainfall intensity and generate radar reflectivity. The reason we choose U-net is because of its excellent performance in image segmentation. Hence, it is an optimal choice to identify the edges of cloud layers and predict rainfall intensity. This research not only aims to extend the detection range of radars, providing more comprehensive weather information, but also helps observe the formation and causes of typhoons and other severe rainfall events. This efficient framework can contribute to monitor various extreme weather conditions and provide real-time warnings, thereby reducing the losses caused by disasters.

In recent times, global temperatures have been rising due to global warming, with East Asia being one of the regions experiencing the highest rate of increase. Furthermore, under high CO2 emission scenarios, the increasing evaporative demand in the East Asia region is projected to rise more rapidly than land surface evaporation. This land-atmosphere moisture difference exhibits high vapor pressure deficits, which can lead to extreme temperatures and droughts. The estimation of potential evapotranspiration, a key indicator of evaporative demand, remains unclear in the available reanalysis or model data, as they do not account for plant responses to CO2. We only focused on stomatal changes in response to CO2 changes, and the results showed an increase in surface resistance with increased CO2, the effect of which was largely offset by vapor pressure deficits, which affects the increase in potential evapotranspiration. In particular, in the case of drought, when plant responses are not considered, potential evapotranspiration tends to overestimate drought in East Asia by approximately 17% compared to scenarios that take vegetation into account. We examined the characteristics of land-atmosphere interactions in East Asia based on land types using a land-atmosphere coupling matrix. Specifically focusing on the characteristics of land-atmosphere coupling and its changes with and without considering plant responses, we analyzed the resulting changes in extreme climate events such as heatwaves and droughts.

We have successfully incorporated a 3-dimensional sub-grid terrain solar radiative effect (3D STSRE) parameterization scheme into a convection-permitting Weather Research and Forecasting model (WRF_CPM) in this study. Impacts of 3D STSRE scheme on the ability of WRF_CPM in forecasting the precipitation in summer over the Tibetan Plateau (TP) and nearby regions with complex terrain have been systematically addressed by conducting experiments without and with the 3D STSRE scheme. Results show that the application of 3D STSRE scheme can obviously mitigate the overestimation of surface solar radiation (SSR) and rainfall over TP and nearby regions, especially over the areas with much more rugged terrain (i.e., southern TP) in the WRF_CPM without 3D STSRE scheme. Further mechanism analyses indicate that the decreased surface heating induced by the reduction of SSR reduces the intensity of the thermal-low pressure over the TP, which leads to the diminished strength of southwesterly winds and thereafter the weaker convergence of moisture flux over the southern TP. Moreover, the weakened surface thermal forcing makes the local atmosphere more stable, suppressing the vertical water vapor transport and local convection. These effects greatly alleviate the overestimation of precipitation over the southern TP produced by the WRF_CPM without the 3D STSRE scheme.

Changes in the soil freeze–thaw status will inevitably affect the thermal conditions and properties of the Tibetan Plateau (TP), thereby affecting its upper atmosphere, and further afield in East Asia and even globally. In this study, using the soil temperature simulated by the Community Land Model Version 5.0, the timing and duration of the soil freeze–thaw status were divided into freeze start-date, freeze end-date, and freeze duration. Results showed: (1) The model effectively reproduces seasonal multi-layer soil temperature changes, showing strong correlation with observations. (2) There is a clear trend of delayed freezing, advanced thawing, and shortened freeze duration from northwest to southeast over the TP. From 1979 to 2018, the freeze start-date was delayed by 7.3 days (1.9 days/decade), the freeze end-date advanced by 6.4 days (1.7 days/decade), and the freeze duration shortened by 13.7 days (3.6 days/decade). The timing and duration of the soil freeze–thaw status vary across different regions of the TP. The freeze start-date in all areas of the TP has been delayed in the past 39 years. Except for the sub-cold zone and arid regions of the TP, the freeze end-date has occurred earlier and the freeze duration has shortened, with the most significant changes in the sub-cold zone and humid regions. (3) The timing and duration of the soil freeze–thaw status are significantly correlated with surface air temperature, elevation, and latitude. The strongest correlation is with surface air temperature, followed by altitude and latitude. The western TP shows a stronger correlation than the eastern TP. The rate of change in the soil freeze–thaw status increases with altitude to 3000 m above sea level, while this rate decreases with elevation above 3000 m. The greatest rate of change is observed at 29°N.

Satellite data shows increasing leaf area of vegetation since 2000, which may exert biophysical effects on near-surface air temperature (SAT). However, such effects remain largely unknown because prior studies either focus on land surface temperature, which differs from SAT, or rely on simulations, which are limited by model uncertainties. As a widely used metric in climate and extremes research, SAT is more relevant to human health and terrestrial ecosystem functions. Therefore, it is necessary to explore impacts of greening on SAT and extremes based on observations. Here, we investigate the greening effects on SAT and subsequent extremes over 2001–2022 based on an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5-land). We find that greening can cause cooling effects on the mean SAT and more pronounced cooling effects on SAT extremes in Europe, East Asia and Central North America, where evident greening was observed since 21^st century. An attribution analysis suggests that the main driving factor for the additional cooling effect of greening in hot extremes is the enhanced evapotranspiration. Moreover, the biophysical effects of vegetation on high temperatures vary with climate conditions and vegetation types. Our study reveals a considerable climate benefit of greening on SAT, which may have implications for climate mitigation in different regions around world.

This study used radiation observation data and remote sensing data to explore the different factors affecting radiation estimation and prediction based on machine learning algorithms. The results indicate that: (1) The estimation of radiation is influenced by weather condition levels and pollution levels, where the estimation of global solar radiation is linearly related to the weather condition level, while the diffuse solar radiation shows a non-linear relationship. In heavily polluted weather, the use of machine learning for solar radiation estimation performs poorly. (2) In all prediction scenarios, the feature selection (FS) of the Random Forest (RF) model is higher than that of the Support Vector Regression (SVR) model, and this performance advantage becomes more pronounced when the lead time exceeds 90 minutes. (3) Remote sensing data can assist in improving radiation prediction. Full-channel input of FY-4A remote sensing data can provide better prediction results, but considering time and computational cost, the optimal three-channel model is a better choice.

The snow cover on the Tibetan Plateau (TP) undergoes sub-seasonal changes, which exert a substantial influence on weather and climate in the surrounding and downstream regions. However, current climate models struggle to accurately capture the rapid changes of TP snow cover. Our research indicates that, most global climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) largely overestimate the snow amount over the TP, along with notable biases in the rapid fluctuations of TP snow cover. Similarly, these biases are also present in regional climate simulations. Through in-depth analysis, we identify snowfall and snowmelt as primary contributors to the observed rapid changes in TP snow cover. The simulated biases in rapid snow changes over the TP result from the excessive snowfall and albedo bias in both global and regional climate simulations. Our findings reveal the potential sources of simulated biases in rapid snow changes over the TP and offer a promising perspective for improving the accuracy of TP regional and global climate simulations.

Based on reanalysis data from ERA5_Land, MEERA2, HadISST, NCEP-NCAR reanalysisⅠ and other datasets, the response characteristics between interdecadal Eurasia snow cover in winter and spring and sea surface temperature in Northern Hemisphere (NHSST) in winter are investigated. Barnett and Preisendorfer canonical correlation analysis (BP-CCA) and regression analysis are used in this paper. Main conclusions are as follows: In the past 40 years, except for Equatorial East Pacific region, the NHSST has increased. And except for eastern Siberia, the snow cover basically decreased. The interdecadal influence of winter NHSST on Eurasia snow cover lasts until spring after removing the linear trend. In the first mode of BP-CCA, winter and spring snow cover increase in Europe and decrease in central Eurasia when the North Atlantic and Northwest Pacific Sea temperatures rise (AMO+, PDO-) in winter. At this time, the height field and surface temperature decrease, water vapor and precipitation increase, trough deepens in Europe, and then snow cover increases in winter and spring. In central Eurasia, the variations are revised. In the second mode of BP-CCA, there is a significant reverse change between snow cover over Eurasia and Northeast Pacific SST with a quasi-16a period, and the North Atlantic SST is a tripole type. When the SST increases in the Northeast Pacific Ocean and the mid-latitude Atlantic Ocean in winter, the trough and ridge in Eurasia all weaken, surface temperature increase, and then winter snow cover decrease. In spring, the water vapor flux and precipitation decreased significantly, and snow cover decreased.

The influence of soil moisture on atmospheric precipitation has been extensively studied, but few of these studies have considered the role of land-atmosphere coupling in afternoon precipitation events at a sub-daily timescale. Here, using in-situ observations and reanalysis datasets, we investigated the effect of the soil moisture anomaly (SMA) on warm seasons’ afternoon precipitation in the North China Plain (NCP), which is identified as a strong land-atmosphere coupling region. Afternoon precipitation events (APE) were separated from all precipitation events in the NCP during the warm seasons of 2010 to 2019. It follows from a comparative analysis that APE is more likely to be initiated on drier soil, which has little dependence on the thresholds used for identifying APE. However, no affirmative relationship is found between precipitation amount in the first hour of APE (APE_1hour) and the soil moisture. Further analyses indicate that larger amounts of APE_1hour result from higher convective available potential energy (CAPE), higher moist static energy (MSE), or weaker vertical shear of horizontal wind. When considering the joint effects of SMA and atmospheric variables, APE tend to occur on drier (wetter) soil with lower (higher) lower-tropospheric stability, CAPE, or MSE. This study highlights the significant roles of land-atmosphere interactions on local atmospheric precipitation, especially the joint roles of soil moisture and atmospheric variables on precipitation.

Evapotranspiration (ET) is a key parameter regulating land–atmosphere interaction processes and the water cycle. The seasonal and interannual variability of ET and its environmental controls over an alpine meadow in a subfrigid humid zone of the Tibetan Plateau (TP) are reported on. Direct measurements were made using the eddy covariance method over a 10-year period with a significant increase in growing season length (GSL). The results showed that annual ET for the alpine meadow site was 492 ± 66 mm in comparison with 635 ± 88 mm of precipitation (P), with a ratio of ET/P ranging from 0.63 (2017) to 1.04 (2010). The path analysis and Priestley–Taylor coefficient (ET/ET_eq) revealed that the daily ET experienced energy-limited and water-limited seasons within the year. During the water-limited non-growing season, the daily surface conductance (Gs) and ET/ET_eq increased positively with soil water content (SWC). During the energy-limited growing season, the 16-day average ET, ET/ET_eq, and Gs scaled positively with the normalized difference vegetation index (NDVI). ET/ET_eq increased nonlinearly with an increase in Gs, but was insensitive to increases in Gs greater than the threshold Gs_*. The mean Gs and Gs_* were strongly regulated by the maximum NDVI. The maximum NDVI, Gs_*, and annual mean Gs explained 46%, 80%, and 68%, respectively, of interannual variation in annual ET. Thus, we concluded that biophysical factors, rather than P and GSL, mainly controlled the interannual variability in annual ET. These findings are critical for understanding the response mechanism of ET to the changing biotic and abiotic conditions in the TP.

Soil moisture is one of the critical variables in the hydro-climate system; however, the gauge-based measurement poses a great challenge in its spatial coverage. To obtain more comprehensive soil moisture data over Taiwan, this study aims to leverage the Weather Research Forecasting model-Hydrological modeling system (WRF-Hydro). To ensure a more reliable regional simulation, the default soil texture, soil hydraulic parameters, and land use/cover data in WRF-Hydro are replaced with locally curated data to better reflect the actual surface characteristics of Taiwan. The Taiwan Hydraulic Digital Elevation Model (HyDEM) is used to configure catchment characteristics in the pre-processing of WRF-Hydro. Gauge- or radar-based precipitation is used as the meteorological forcing to drive WRF-Hydro. Simulated soil moisture will be compared and validated with gauge- and satellite-based data in Taiwan. We believe that the outcome of this work will provide more effective soil moisture information for ensuing hydro-climatic modeling and disaster monitoring.

This study examines the role played by aerosol in torrential rain that occurred in the Seoul area, which is a conurbation area where urbanization has been rapid in the last few decades, using cloud-system-resolving model (CSRM) simulations. The model results show that the spatial variability in aerosol concentrations causes the inhomogeneity of the spatial distribution of evaporative cooling and the intensity of associated outflow around the surface. This inhomogeneity generates a strong convergence field in which torrential rain forms. With the increases in the variability in aerosol concentrations, the occurrence of torrential rain increases. This study finds that the effects of the increases in the variability play a much more important role in the increases in torrential rain than the much-studied effects of the increases in aerosol loading. Results in this study demonstrate that for a better understanding of extreme weather events such as torrential rain in urban areas, not only changing aerosol loading but also changing aerosol spatial distribution since industrialization should be considered in aerosol–precipitation interactions.

Effects of an aerosol layer on warm cumulus clouds in the Korean Peninsula when the layer is above or around the cloud tops in the free atmosphere are compared to those effects when the layer is around or below the cloud bases in the planetary boundary layer (PBL). For this, simulations are performed using the large-eddy simulation framework. When the aerosol layer is in the PBL, aerosols absorb solar radiation and radiatively heat up air enough to induce greater instability, stronger updrafts and more cloud mass than when the layer is in the free atmosphere. Interestingly, even though the aerosol layer in the PBL intercepts more solar radiation reaching the surface, resulting in reduced surface heat fluxes, instability, updraft intensity, and cloud mass, the overall effect still leads to more cloud mass. Hence, there is a variation of cloud mass with the location of the aerosol layer, which arises from an interplay between aerosol-induced changes in the surface fluxes and those in radiative heating of air. Even without impacts of aerosols on radiation, there is still more cloud mass when the aerosol layer is in the PBL, because aerosol-induced changes in droplet nucleation induces aerosol-induced invigoration of updrafts. However, the variation of cloud mass is much smaller when there is only aerosol impacts on droplet nucleation than when aerosol impacts on both of radiation and nucleation are present. Hence, aerosol impacts on nucleation and those on radiation work together to amplify the variation of cloud mass with the altitude of the aerosol layer. This study reveals that this variation of cloud mass also reduces as aerosol concentrations in the layer decrease. Furthermore, the transportation of aerosols by updrafts reduces aerosol concentrations in the PBL. 

In middle and high latitudes, marine stratocumulus clouds often form after cold fronts pass, with cold air flowing from the continents during winter. Because these stratocumulus clouds persist over a relatively broad region and a relatively long duration compared to clouds associated with typical low-pressure systems, they can produce significant cloud radiative forcing, which is negative owing to their low altitudes and large cloud water contents. As they move downwind, they usually experience transition to cumulus clouds with lower cloud fraction. While increasing sea surface temperature has been recognized as the main factor to control the transition, some recent studies have pointed out that precipitation, which can be modulated by aerosols, is also important to the transition. In this study, we present the physical properties of boundary-layer clouds embedded in the cold-air outbreaks over the Yellow Sea occurred from 2013 to 2023. We identify the entire cases seen on the MODIS observations during the period, a total of 119 cases. The cloud fraction peaks at the middle of the cloud trajectories and then decreases as the clouds move downwind. The cloud top temperature generally increases along the trajectories although the cloud top height is also increasing, while the cloud water path remains relatively unchanged as the clouds move downwind. Beyond analyzing the mean properties of the entire cold-air outbreak stratocumulus clouds, we select some cases and categorize them as either ‘clean’ or ‘polluted’ based on the average carbon monoxide concentration over the upwind region of the trajectories. We then illustrate the effects of pollution on the cloud evolution by comparing the properties of the two cloud groups. Additionally, we conduct a series of numerical simulations and examine in detail how aerosols influence the cloud evolution and the transition to cumulus clouds.

Precipitation is an important process that contributes to the energy budget of the earth by modulating the water cycle and also assists in controlling the concentration of atmospheric pollutants by scavenging particulate matter (PM) (i.e., wet removal). Previous studies attempted to quantify the impact of wet removal mechanisms on PM concentrations, however, they performed the research based on short-term measurements, limited sites, or a few precipitation event cases. Thus, this study showed the spatiotemporal characteristics of precipitation and the concentration of PM (i.e., PM2.5 and PM10) in six regions (i.e., Seoul, Busan, Daejeon, Daegu, Gwangju, and Jeju) individually, and quantified the impact of the wet removal on the concentration of PM by precipitation events based on long-term measurements in six regions in Korea. Precipitation and PM10 surface measurements in six regions were used for 22 years (2001-2022), while PM2.5 surface measurements were only available for 8 years (2015-2022). All measurements were made with a one-hour resolution. The spatiotemporal variabilities (i.e., yearly, seasonal, and monthly averages) of precipitation and the concentration of PM were calculated in each region. The precipitation events were identified when the threshold time interval (i.e., 2 hours) was presented within the hourly precipitation data. Based on the duration, average intensity, and accumulated amount of precipitation events, the characteristics of precipitation events were analyzed. The variation in the concentration of PM during the precipitation events and the rate of wet removal were quantified as functions of the characteristics of precipitation events (i.e. duration, average intensity, and accumulated amount of precipitation events) for each of the six regions. The spatiotemporal characteristics of the regional wet removal effects that occurred by precipitation events were calculated, and the influence of the precipitation properties on the wet removal effects was evaluated.

Meteorological phenomena are highly nonlinear, and small differences in initial values can significantly affect the results. Therefore, ensemble calculations are a commonly used computational technique in weather/climate simulations to reduce the inevitable uncertainty inherent in individual simulations and to quantitatively evaluate the degree of uncertainty. However, obtaining an average value with sufficient accuracy requires a large number of ensembles, which in turn requires significant computational resources. For example, in the case of the atmospheric model NICAM with a resolution of 3.5 km and 1024 ensembles, 131072 nodes were used, but with this number of nodes, it was not possible to calculate 1024 ensembles at once, and a method of repeating 4 cycles of 256-member ensemble calculations per cycle was employed. Therefore, in our study, we coupled a low-resolution ensemble with single high-resolution computation. This method can replace high-resolution large-scale ensembles with fewer computational resources. Furthermore, we can expect to obtain more accurate results by coupling low-resolution calculations, which excel in reproducing large-scale fields, with high-resolution calculations, which excel in reproducing detailed fields. A general-purpose coupler, h3-Open-UTIL/MP, was applied to this study. Two execution modes are possible on this coupler: many-to-one coupling, where multiple ensemble runs are coupled to a single model, and many-to-many coupling, where multiple coupled models are executed as an ensemble. In this study, the later mode was applied. We coupled low resolution NICAM ensemble and high resolution NICAM single model, on the Wisteria/BDEC-01 of the University of Tokyo. For the calculations, optimization of the single model and variable resource allocation were applied to minimize computational resources (node-time) as much as possible. In this presentation, the details of the ensemble coupling will be explained and the computational performance will be discussed.

Speeding up the post-processes in addition to the weather and climate simulation itself is important for speeding up the overall workflow. There are a wide variety of post-processes, but one typical post-process required for many models is remapping from model native grids to latitude-longitude grids. Here, we developed a series of parallelized remapping algorithms for NICAM, a global weather and climate model with an icosahedral grid system, and demonstrated their performance with global 14-0.87-km mesh model data on the supercomputer Fugaku. The original remapping tool in NICAM supports parallelization only in reading and interpolating data. In our proposed algorithms, the process of data writing is parallelized by separating output files or using the MPI-IO library, both of which enable us to remap 0.87-km mesh data with 670 million horizontal grid points and 94 vertical levels. The benchmark with 14-km mesh data shows that the developed algorithms significantly outperform the original algorithm in terms of elapsed time (by 7.4-8.7 times) and memory usage (by 2.8-5.0 times). Among the proposed algorithms, the separation of output files, along with reduced MPI communication size, leads to a better performance in the elapsed time and its scalability, and the use of the MPI-IO library leads to a better performance in memory usage. The remapping year per wall-clock day, assuming a six-hourly output interval, is up to 0.56 with 3.5-km mesh data, demonstrating the feasibility of handling global cloud-resolving climate simulation data in a practical time. This study demonstrates the importance of IO performance, including MPI-IO, in accelerating weather and climate research on future supercomputers.

The Korean Integrated Model (KIM) is a global weather forecast model independently developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS). Comprising a spectral element-based dynamical core and a scale-aware physics package on cubed-sphere grids, KIM is currently deployed by the Korea Meteorological Administration (KMA) in a 12 km resolution, with data assimilation for weather forecasting. However, acknowledging the challenges in accurately simulating precipitation systems, particularly sudden and intense heavy rains around the Korean Peninsula, there is a growing need for a higher resolution model. In this study, we attempted to simulate KIM with a 3-4 km horizontal resolution, strategically avoiding cumulus parameterization specifically for cases of extreme heavy rainfall. The primary aim was to assess the possibility of positioning KIM as a Global Cloud-Resolving Model (GCRMs). As a result, KIM demonstrating a stable 10-day simulation of extreme rainfall case and more realistic compared to the 8 km simulation.

Himawari-8, as a geostationary satellite, carries Advanced Himawari Imager which can perform full-disk observations every 10 minutes. Due to coastal water being turbid, shallow, and mixed with the land, the aerosol retrieval product cannot accurately retrieve the Aerosol Optical Depth (AOD) over shallow and turbid coastal waters. To fill the gap in this area, we developed a coastal water retrieval algorithm with a spatial resolution of 2 km. The land mask is first performed by normalized difference water index (NDWI), and then the cloud mask is performed by a spatial variation test based on 2.3 μm and some traditional reflectance and bright temperature threshold tests. After that, the water pixels are classified into open ocean pixels and coastal pixels by power-law fitting algorithm and Elevation and Topography at 1 arc minute (ETOPO1) bathymetry. After performing gas correction, the AOD and aerosol properties were retrieved using the Dark-Target algorithm for the open ocean pixels. For the coastal pixels, the AOD was retrieved using the 2.3 μm top of atmosphere reflectance and the aerosol properties of the nearest open ocean pixel. This algorithm can improve the accuracy and coverage of the coastal AOD.

Since the industrial revolution, human beings have produced a large amount of air pollutants. Air pollution results in changes in the atmospheric composition, which in turn alters the energy budget of the Earth system and, consequently, causes the present-day climate crisis. The main climate active substances in the air pollutants include the greenhouse gases and atmospheric aerosols. The greenhouse gases absorb IR radiation and cause warming of the atmosphere, whereas aerosols could cause warming or cooling effects, depending on the microphysical and optical properties. The eastern Asia is among the most polluted regions in the world. Along the course of energy transition and air pollution mitigation, monitoring data provide the crucial evidence to evaluate the status of environmental changes. This study has been conducting observation of atmospheric aerosols (PM₁₀) and major greenhouse gases (CO₂, CH₄, N₂O) at three remote sites (Penghu, Green Island, Cape Fuguei) around Taiwan. The preliminary results of observation for 2018 – 2019 showed that, in terms of annual mean level, the background mixing ratios of CO₂, CH₄, and N₂O were 418 ppmv, 1.95ppmv, and 334.3 ppbv, respectively. Moreover, according to the observation data from the Penghu, Green Island and Cape Fuguei research stations, we estimated the background level of atmospheric aerosols (annual mean of PM₁₀) was 30.6 ugm^-3.

At the Meteorological Service of Singapore, Himawari geostationary data are used for transboundary haze monitoring and retrieval of Aerosol Layer Height with the application of different Random Forest architectures and Sentinel-5p/TROPOMI training data. The Numerical Atmospheric-dispersion Modelling Environment (NAME) is used for dispersal modelling and forecasting of forest fire emissions, and for further derivation of AOD. We evaluate performance of the system in recent haze incidents using the GEMS AOD product as reference. GEMS is the first satellite sensor dedicated to atmospheric monitoring in geostationary orbit, providing hourly AOD and Aerosol Layer Height information. The aim of this study is to explore the potential of GEMS to support operational, high frequency haze monitoring and validation of dispersion model outputs, specifically in the challenging environment of Southeast Asia.

Aerosols are closely associated with air quality, climate change, and public health. Moreover, aerosol concentrations exhibit significant regional variations and considerable temporal fluctuations. Therefore, studying the long-term changes in aerosol concentration and analyzing the trends are essential for air quality improvement and climate change research. In this study, we present the long-term trends of Aerosol Optical Depths (AODs) across Asia and compare the trends of Fine-mode Aerosol Optical Depths (FAODs) and Coarse-mode Aerosol Optical Depths (CAODs). AOD represents the attenuation of solar radiation due to aerosols in the entire atmosphere, serving as an indicator of aerosol quantity. The Aerosol Robotic Network (AERONET) is a ground-based observational network providing information on aerosol optical properties worldwide. AERONET holds long-term data and offers continuous observations through daily measurements. For this reason, we analyzed the long-term trends of AOD using AERONET data. To examine changes in aerosol quantity attributed to anthropogenic sources and yellow dust, we presented trends in Coarse-mode Aerosol Optical Depths (CAODs) and Fine-mode Aerosol Optical Depths (FAODs), categorizing aerosols based on size. By comparing CAODs and FAODs, common trends were identified in trend analysis across countries or sub-regions. Through this analysis, it becomes evident that aerosol concentrations vary significantly regionally. However, it is apparent that factors such as each country's local environment and air quality policies exert substantial influence on aerosol trends.

Since its launch in 2020, the GOCI-II (Geostationary Ocean Color Imager-II) onboard the GEO-KOMPSAT-2B (GK-2B) satellite has provided aerosol products using the Yonsei aerosol retrieval (YAER) algorithm (Lee et al., 2023). The GOCI-II YAER algorithm retrieves aerosol optical depth (AOD) at 550 nm using an inversion algorithm with a precalculated look-up table (LUT) over UV to near-IR wavelengths. The surface reflectance database is collected using the Cox and Munk method (Cox and Munk, 1954) and the minimum reflectance technique (Hsu et al., 2004) over ocean and land, respectively. When calculating the minimum reflectance, the minimum value of Lambertian Equivalent Reflectance (LER) of each wavelength is designated as the surface reflectance at each pixel. The 550 nm AOD is the weighted average of AOD of two aerosol types that minimize the standard deviation among the six pre-assumed types. In this study, we improved the performance of the GOCI-II YAER algorithm by optimizing the surface reflectance calculation method and tuning the aerosol type selection phase. First, the spectral AOD of the YAER algorithm was validated to the AOD from the AErosol RObotic NETwork (AERONET) to test the stability of the minimum reflectance. The wavelength on which its AOD showed the highest consistency with AERONET was used as the standard of the minimum reflectance to fix the surface reflectance of all other wavelengths. Second, aerosol type selection phase was tuned to take more aerosol optical information during the selection. As a result, the updated product showed improved validation statistics when compared to AERONET AOD in terms of % within expected error (EE), the correlation coefficient, and the root mean squared error (RMSE). The improved GOCI-II AOD can help mitigate the air quality issues and expand our knowledge of diurnal variations of aerosols over Northeast Asia.

There are remaining uncertainties from satellite remote sensing retrieval of nitrogen dioxide (NO₂), one of important pollutants for understanding urban air quality study. In this research, we analyze the impact for spatial resolution of observation pixels on retrieved differential slant column density (dSCD), regarded as one of uncertainty factor. Airborne measurement data which offers a higher resolution remote sensing data compared to satellites and useful for evaluating spatial variability were collected from KORUS-AQ and SIJAQ campaign. Comparing dSCD maps among different spatial resolutions, we confirm that some point sources and structures of NO₂ density distribution were diluted and smoothed at a low resolution. Also, we confirm increase of standard deviation values that shows deviation of information on pixels over 2 km spatial resolution. In future, we can study the impact of spatial resolution on other trace gases that has different spatiotemporal variability. Furthermore, we can advance the understanding of satellite data according to spatial resolution.

This study aims to quantify NO_x lifetimes and emissions in Seoul, South Korea, using data from Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO₂ observations in 2022 along with ECMWF wind field data. NO_x lifetimes are determined by analyzing systematic difference obtained through fitting NO₂ line density to a model function, considering wind directions and wind speed. The results indicate that, with a fitting interval of 100×100km², the calculated lifetimes ranged from 14.14 to 50.19 minute in spring, 16.23 to 53.61 minute in summer, 19.32 to 62.89 minute in autumn and 24.2 to 62.02 minute in winter (R²>0.8). When the fitting interval is 100×200km², lifetime ranged from 34.05 to 82.61 minute in spring, 33.81 to 80.28 minute in summer, 30.13 to 94.07 minute in autumn and 61.11 to 94.89 minute in winter (R²>0.8). The quantified NO_x emissions for 2022 were determined based on the lifetime fitting results, ranging from 210.66 Gg to 714.93 Gg with a 100×100km² lifetime fitting interval and from 174.33 Gg to 390.88 Gg with 100×200km² lifetime fitting interval. To verify these results, we used Clean Air Policy Support System (CAPSS) emissions inventory data. The mean of NO_x emissions for 2016-2020 within the fitting interval area was 203.7±21.22 Gg. In conclusion, adopting a 100×200km² fitting interval provided better visibility of the movement of the NO₂ plume, resulting in longer calculated lifetimes. The study acknowledges the high uncertainty in lifetime fitting results during winter and emphasizes the careful interpretation of winter emission results. Future studies are proposed to compare NO_x lifetimes and emissions in other East Asian regions for verification. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006).

Trans-Pacific transport is important because it not only affects air quality in East Asia but also in North America. In April, trans-Pacific transport is associated with the Western Pacific pattern (WP). When the WP is positive phase, favorable atmospheric circulation for trans-Pacific transport is formed over the North pacific, leading to an increase of the Aerosol Index (AI) over the North America. Climate change leads to extreme weather phenomena. WP is no exception. Changes in the WP due to climate change are expected to impact trans-Pacific transport as well. Coupled Model Intercomparison Project phase 6 (CMIP6) makes possible to examine the changes associated with climate change and thus predicting the changes in air quality. In this analysis, we investigated whether CMIP6 simulated the spatiotemporal pattern of Aerosol Optical Depth (AOD) according to WP and examined the tendencies of the WP phase under different Shared Socioeconomic Pathways (SSPs). CMIP6 effectively simulated the changes in atmospheric circulation and the AOD associated with the variations in the WP phase. When comparing the four SSPs (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), the frequency and intensity of positive WP phase were highest in the highest CO₂ emission scenario, SSP5-8.5. This suggests that the higher carbon emission, the more frequent and intense trans-Pacific transport. Further investigation is needed to understand the underlying reasons for increased frequency and intensity of positive WP phase in high carbon emission. Additionally, the analysis should be conducted using more model data. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006).

PM_2.5 denotes aerosol particles in the air with aerodynamic diameter of less than 2.5μm. PM_2.5 contributes to social and economic challenges and negatively affects human health. Research into the temporal and spatial variability of PM_2.5 is crucial for its effective management. The Ministry of Environment has operated a ground-based observation network, AirKorea, since the early 2000s to continuously monitor air pollutants, including PM_2.5 and its precursors (NO₂, SO₂, etc.). This study analyzed diurnal variations in PM_2.5 concentrations in South Korea using data from AirKorea observation network from 2016 to 2021. In South Korea, PM_2.5 levels typically begin to rise at 8 a.m. during rush hour, peak around 10 a.m., and then decrease after noon as radiation levels increase. A subsequent increase is observed starting around 6 p.m. during the evening rush hour, with another peak occurring at night. Many previous studies have indicated that the morning peak in PM_2.5 is influenced by increased anthropogenic emissions, and the afternoon decrease is affected by an increase in Planetary Boundary Layer (PBL) height. However, research specifically investigating the nighttime peak is limited. In this study, to determine the cause of the PM_2.5 nighttime peak in South Korea, we analyzed the differences in meteorological conditions (PBL height, wind speed, relative humidity) and air pollutants (PM₁₀, NO₂, SO₂) on days with the PM_2.5 nighttime peak compared to days without it. Additionally, we used PM_2.5 compositional data to compare the concentrations of its constituents on days with the nighttime peak to those on days without such peaks in Seoul, Ulsan, Jeju, Gwangju, and Daejeon. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006).

This study explores the key energy source between the Earth and the atmosphere, solar radiation, which directly and indirectly influences the weather. Changes in the Earth's system impact radiative equilibrium, and the concept of radiative forcing (RF) was introduced to quantify these effects. RF is influenced by specific atmospheric components such as trace gases, with significant impacts primarily attributed to changes in clouds and aerosols. To understand direct aerosol radiative forcing (DARF) caused by aerosols, we developed an algorithm considering aerosol optical depth (AOD) and aerosol types using the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK-2B (Geostationary Korea Multi-Purpose Satellite-2B) satellite. GEMS AOD algorithm version 2.0 and the libRadtran version 2.0.4 radiative transfer model were employed as input data. Aerosol types were classified as Black Carbon, dust, and non-absorbing aerosol, and the OPAC model was used for DARF calculations based on aerosol types. Surface (SFC), top of atmosphere (TOA), and atmospheric (ATM) DARF spatial distributions were examined in major Korean cities. To obtain city-specific DARF time series from November 2021 to April 2023, city boundaries were defined, and the average AOD values of all GEMS pixels within each boundary were used as input data. For validation purposes, a comparison with Aerosol Robotic Network (AERONET) inversion data for RF was also conducted. This study aims to enhance understanding of air pollution issues in South Korea and the variation characteristics of DARF in different regions throughout the day. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006).

The hydroxyl radical (OH) is a pivotal oxidant in the troposphere, playing a crucial role in determining the lifetime of critical pollutants such as carbon monoxide (CO) and methane (CH4). The primary generation of tropospheric OH occurs through the photolysis of ozone (O3) in the presence of water vapor (H2O), making tropospheric OH's abundance and oxidative capability heavily dependent on diurnal patterns. Yet, due to its ephemeral lifetime of approximately one second and its scant concentration in the troposphere, direct measurement of tropospheric column OH (TCOH) and its diurnal fluctuation remains unattainable through global satellite monitoring. Efforts have been made to use satellite observations as a proxy to study OH variability on temporal scales longer than a day, often in conjunction with chemistry transport or machine learning models. With the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) in February 2020, which provides hourly measurements of atmospheric pollutants like O3, nitrogen dioxide (NO2), and formaldehyde (HCHO) over Asia, our understanding of these pollutants' diurnal variations has significantly advanced. This study introduces a method for approximating the diurnal variation of TCOH by employing satellite data on OH precursors, including the hourly-changing observations from GEMS. Utilizing training data from satellite observations and TCOH values from chemical composition reanalysis fields, the supervised machine learning model adeptly predicts the diurnal variation of TCOH when provided with inputs of OH precursors. The findings also indicate a promising avenue for integrating this approach into high-resolution CH4 modeling for East Asia.

Volatile organic compounds (VOCs) are precursors to surface ozone and secondary organic aerosols. In East Asia, simulations from chemical transport models often miss significant VOCs due to uncertain emission sources. Formaldehyde (HCHO), a byproduct of VOC oxidation, can act as an indicator to provide observational constraints on VOC emissions. The Geostationary Environment Monitoring Spectrometer (GEMS) commenced hourly monitoring of HCHO vertical column densities (VCDs) over East Asia starting in August 2020. This study assesses the updated operational GEMS HCHO retrieval algorithm (version 3) by comparing it with TROPOMI and ground-based observations (FTIR, MAX-DOAS) throughout its three years of operation. We introduce several enhancements to the retrieval algorithm, particularly regarding background corrections and the input parameters that influence air mass factor (AMF) calculations. We observe that utilizing hourly background surface reflectance from GEMS, rather than OMI Lambertian equivalent reflectivity, results in 20–30% lower AMFs over land areas in East Asia. The revised GEMS HCHO data generally align with the spatial distributions observed by TROPOMI HCHO (r=0.67–0.88), despite slightly underestimating VCDs (NMB=-28% to -17%) across the scanning domain. Additionally, we explore factors affecting AMF sensitivity using HCHO vertical profiles obtained from airborne measurements during the KORUS-AQ campaign. We note that certain geometric conditions, particularly in the early morning (7–8 KST) and afternoon (15–16 KST), significantly impact the AMF, potentially increasing reliance on model accuracy in VCD calculations. Our findings underscore the necessity of precise model simulations in calculating HCHO AMF.

Nitrogen dioxide (NO₂) is a gaseous atmospheric pollutant that contributes as catalysts to the formation of ozone in the troposphere and plays a significant role in atmospheric chemistry. The ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) have been employed to investigate tropospheric trace gases (including NO₂) and aerosols at a fixed location with high temporal resolution and accuracy. The continuous measurements of ground-based MAX-DOAS are advantageous in effectively observing the high temporal variations of NO₂ Tropospheric Vertical Column Density (NO₂ TropoVCD). Aiming to obtain the reliable NO₂ TropoVCD in Seoul Metropolitan Area (SMA), two MAX-DOAS have been operated in Seoul (37.59°N, 127.03°E, 80 m) and Yongin (37.34°N, 127.27°E, 167 m), downwind of Seoul, since October 2023 and October 2021, respectively. During Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, the purposes of this study are summarized as follows: (1) validation of NO₂ TropoVCD from Geostationary Environmental Monitoring Spectrometer (GEMS) using the MAX-DOAS, (2) comparison of the differences in MAX-DOAS NO₂ TropoVCD between two sites, (3) assessment of NO₂ advection from Seoul to Yongin using the NO₂ vertical profile. We believe these results will contribute to improving the accuracy of GEMS NO₂ products and the understanding of NO₂ behavior in SMA.

In this study, we developed a ground-based sulfur dioxide (SO2) Slant Column Density (SCD) retrieval algorithm based on Differential Optical Absorption Spectrometer (DOAS) from hyperspectral measurements to monitor industrial stack plumes. Our algorithm has an advantage of a capability to continuously and remotely monitor industrial SO2 plumes rather than in-situ SO2 measurements. Also, it has a valuable advantage that we know uncertainty of instruments we use and experimental situation we have approximately. And we calculated the optical path length using the observation geometry for mixing ratio retrieval. In a industrial stack plume observation situation, the optical path length that considers the distance between the instrument and the stack and the plume thickness together can increase the accuracy of the mixing ratio retrieval. The experimental data to test this algorithm of SCD and mixing ratio was obtained in Haman, South Korea in August, 2022 and Gimhae, South Korea in November 2023. Since most in-situ measurements has a product in the unit of mixing ratio, it has a valuable advantage of being able to confirm the accuracy of observation through comparison of the retrieved mixing ratio and in-situ.

Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere (MLT) region. In this study, we conducted a comparative analysis of the mesosphere tidal results obtained from these two adjacent meteor radars at low latitudes in Kunming， China from November 2013 to December 2014 were analyzed and compared. These two radars operate at different frequencies of 37.5 MHz and 53.1 MHz, respectively. However, an overall good agreement was observed between the two radars in terms of horizontal winds and tide observations. The results showed that the dominant tidal waves of the zonal and meridional winds are diurnal and semidiurnal tides. Moreover, we conducted a statistical comparison of tide amplitudes and phases observed by both radars, revealing a high degree of correlation between the tidal parameters. Additionally, the variances and covariances of tidal amplitudes and phases were found to be quite similar for the two radars. Thus, mesospheric tides at low latitudes observed by two collocated meteor radars with different frequents exhibit strong consistency.

Various mean-flow oscillations in the equatorial middle atmosphere can be generated by interaction between the zonally averaged mean flow and numerous atmospheric wave modes. Quasi-biennial oscillation (QBO) is one of the most well-known examples in the wave-mean interaction in the tropical lower stratosphere. However, the QBO can be modulated by momentum forcing due not only to equatorial waves but also to horizontally propagating Rossby waves that originate from the extratropical region. In the northern winters of 2015/2016 and 2019/2020, the downward progression of westerly QBO phase was interrupted by localized and intensified easterlies, mainly due to exceptionally large wave momentum forcing due to extratropical Rossby waves. In this study, global wave modes represented by Hough and vertical structure functions are obtained from MERRA-2 reanalysis data by decomposing global winds and temperature into components associated with Kelvin, Rossby-gravity, inertia-gravity, and Rossby waves. Then, resolved wave momentum forcing (Eliassen-Palm (EP) flux divergence) due to each wave mode is estimated. To compute EP flux and its divergence, methods of obtaining temperature perturbations and vertical wind velocity induced by each wave mode have been developed. Comparison of EP flux divergence for climatological QBO indicates that dominant eastward (westward) wave momentum forcing is due to Kelvin waves (Rossby waves). The QBO momentum budget in terms of zonal wind tendency due to advection, the resolved wave momentum forcing due to each wave mode, analysis increments, the parameterized gravity wave drag, and the residual tendency will be discussed together with wave momentum budget in the disrupted QBO periods.

Many researches over the past decades have been shown that OH airglow known as the peak emission altitude of about 87km is very useful to study not only the propagation and dissipation of atmospheric gravity waves but also the mesospheric temperature variation with time. Recently, KOPRI (Korea Polar Research Institute) has started to operate Mesospheric Temperature Mapper (MTM) at King Sejong Station, Antarctica (62.2S, 58.8W) since its installation on Jan. 2023, in order to study the small-scale gravity wave activities and temperature variation near mesopause region. MTM instrument provides two dimensional OH airglow intensity and rotational temperature every 30s with a high spatial resolution of ~0.5km.In this study, we introduce the KSS MTM instrument and present some preliminary results which compare the temperatures obtained from MTM for 26 clear nights during 2023 austral winter season with the temperatures from co-located KSS Meteor Radar and Aura/MLS satellite.

This study presents regional climate simulations for East Asia and China using the Model for Prediction Across Scale-Atmosphere (MPAS-Atmosphere) driven by ERA-Interim reanalysis. In a 29-year simulation (1988-2016), MPAS-A with a global variable resolution (VR) configuration (92-25km mesh refinement over East Asia) is evaluated for precipitation, near-surface air temperature, and circulation against observed climate using combined observational datasets. Large-scale deviations, such as the northward displacement of rain belts, excessively triggered precipitation on the ocean, and stationary surface air temperature biases, are identified. These deviations are attributed to simulated circulation, moisture transports. Focusing on the summer monsoon with the highest uncertainty in East Asian climate, comparative experiments are conducted using a similar 92-25km grid under global variable resolution settings (MPAS-VR) and limited-area settings (MPAS-RCM) for simulations from April to August during 1998-2017. Constrained by the “reanalysis-based” lateral boundary conditions (LBCs), MPAS-RCM could well capture the evolution of large-scale fields and becomes a proper reference for evaluating MPAS-VR. MPAS-VR reproduced the conclusions of the previous 29-year simulation. MPAS-RCM, benefiting from a more realistic water vapor transport, alleviates the wet bias and presents better daily variations and rain belts’ evolution. However, MPAS-RCM did not exhibit a clear advantage in the distribution of climate indices related to extreme events over land. Additionally, its performance in terms of climatology and interannual variability of the surface temperature are insufficient compared to MPAS-VR. With the same dynamic core and model physics, those climate features are similarly resolved by the two simulations. the study underscores the robustness and potential of the VR approach, particularly regarding extreme rainfall and heat wave.

This study presents an in-depth evaluation of the Weather Research and Forecasting (WRF) model's performance in the context of gray-zone (9 km) dynamical downscaling over China, emphasizing the added value it brings to regional climate modeling. The term "gray-zone" refers to scales where traditional parameterization schemes are neither fully effective nor entirely redundant, a common challenge in atmospheric modeling. Our work bridges this gap, focusing on the transitional scale phenomena. The research utilized the advanced WRF model, incorporating recent updates in its physics and dynamics schemes, to conduct high-resolution simulations over China. The study period spanned multiple years to capture a comprehensive range of meteorological conditions, including extreme climate events. Our evaluation criteria involved a comparative analysis of the WRF model outputs against observational datasets and ERA5 reanalysis to assess its performance and added value. Key findings demonstrate that the WRF model, when applied in the gray-zone, significantly enhances the representation of mesoscale meteorological features compared to ERA5. This improvement was particularly notable in complex terrain areas, where the model accurately captured local near-surface air temperature patterns and precipitation distributions. Furthermore, the model showed a marked improvement in simulating extreme climate events, such as heavy rainfall and heatwaves, which are critical for regional climate impact assessments. The added value of the WRF gray-zone downscaling is evident in its ability to provide more accurate and detailed climate information at a regional scale. This is particularly beneficial for China, where diverse topography and climatic conditions pose a challenge for conventional models. The significant advancements and potential of gray-zone dynamical downscaling using the WRF model over China facilitate better decision-making in sectors like agriculture, water resource management, and urban planning, where precise climate data is crucial.

Future dry-wet changes of Northwest China and their mechanisms remain controversial. Therefore, this work projected seasonal and annual dry-wet conditions in the arid and semi-arid regions by analyzing the variation of water availability, which is defined as precipitation minus evaporation, based on the downscaled future regional climate change under 1.5/2.0°C stable warming scenarios (1.5s/2.0s) using Weather Research and Forecasting (WRF) model. The results showed that, the water availability in arid increased by 1.09 and 1.24 mm/month under future warming scenarios of 1.5s and 2.0s, respectively, while in semi-arid, the increase was lower than in arid and even decreased in summer. The results of changes in moisture transport suggested that the stronger increase in water availability in arid may be related to the inflow of moisture from arid and the outflow from semi-arid in summer. The moisture budget analysis further demonstrated that, the increase of water availability in arid was mainly due to the enhanced contribution of the thermodynamic term caused by warming, which further increased by 8% with an additional warming of 0.5°C. Whereas the decrease in summer semi-arid was from a negative contribution of the non-linear term, which may be related to reduced moisture transport due to changes in the East Asian summer monsoon.

In this study, Weather Research and Forecasting (WRF) based dynamic downscaling simulation was performed over China at a horizontal resolution of 25 km. To reduce the systematic large-scale biases from lateral boundary conditions, a dynamic blending (DB) technique was introduced in downscaling, and its performance was compared with downscaling without DB (NO_DB) as well as the driving global climate model (GCM, i.e., HadGEM3). In the present-day simulation for verification, added values were found in DB, relative to the GCM and NO_DB, in simulating the precipitation extremes, especially over southeastern China. Possible causes responsible for this improvement were further analyzed. In the GCM, excessive moisture and atmospheric heating in the upper troposphere in conjunction with abnormally strong deep convection resulted in excessive extreme precipitation over southeastern China, while in NO_DB, insufficient moisture and atmospheric heating in the whole troposphere in conjunction with abnormally weak convection suppressed extreme precipitation there. In comparison, DB showed a closer representation of observations in terms of vertical velocity and vertical profiles of atmospheric moisture and heating, accounting for the improved simulation of extreme precipitation. In the future projection under the SSP5-8.5 scenario, both the GCM and DB predicted that extreme precipitation would increase in most parts of China; however, DB indicated a larger increase over southeastern China relative to the GCM. Stronger moisture flux convergence over southeastern China in DB accounted for this larger increase, and in addition, the thermodynamic effect associated with more precipitable water dominated the stronger moisture flux convergence.

High-resolution climate simulations has gained importance in recent climate change impact studies. This approach allows for the investigation of the "gray zone," denoting resolution higher than 10 km where model exhibit uncertainties. Therefore, our objective is to determine the added value high-resolution model simulations over the Peninsular Malaysia based on high resolution simulations using RegCM4 driven by the Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). Two one-way nested domains were employed. The first domain covers the entire Southeast Asia and was configured at 25km grid resolution. The second domain covers the entire Peninsular Malaysia and was configured at 5km grid resolution. Given the complex resolution of Peninsular Malaysia there is potential for significant advantages in adopting high-resolution simulations. Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Climate Hazards Group Infrared Temperature with Stations (CHIRTS) data were used as references data for precipitation and temperature respectively. The analysis covers the period 1990 to 2014 and extreme climate indices were calculated using Expert Team on Climate Change Detection and Indices (ETCCDI). Extreme precipitation used were RX1day, RX5day, CWD, CDD, R95Pptot, R99ptot and SDII. Fo temperature, it is TNN, TXN, TXX, TXN, TN90P, TN10P, TX10P and TX90P. The 5km resolution model shows remarkable improvements in simulating rainfall distribution and characteristics during the DJF season in the Northeastern part of Peninsular Malaysia. High-resolution 5km models demonstrate better capabilities in simulating extreme wet indices. Additionally, the high resolution simulations also improve the rainfall diurnal cycles over Peninsular Malaysia. The 5km resolution model also shows enhancements in simulating extreme temperatures, especially over the high-altitude regions. These findings show the relevancy of the high resolution model in simulating climate extremes. Moreover, it highlights the need of high resolution simulation in studying the climate over a complex topography in Peninsular Malaysia.

Tibetan Plateau (TP, with the height > 3000 m) is a region with complex topographical features and a large diversity of climate both in space and time. Future climate change over TP and the surrounding areas is investigated based on the ensemble of a set of the 21st century climate change projections using a regional climate model, RegCM4. The model is driven by five different GCMs at a grid spacing of 25 km. Results show the RegCM4 greatly improves the temperature and precipitation simulations by providing finer scale spatial details of them over the region. The topographic effects are well reproduced by RegCM4 but not the GCMs. General warming and increase in precipitation are found in both GCM and RegCM4 simulation, but with substantial differences in both the spatial distribution and magnitude of the changes. For temperature, RegCM4 projected a more pronounced warming in DJF over TP compared to its surrounding areas. The increase of precipitation is more pronounced and over the basins in DJF for RegCM4. For the extreme indices of snowfall, RegCM4 generally reproduces the spatial distributions although with overestimation in the amount. General decreases in SNOWTOT and S1mm, with greater magnitude over the eastern part are projected. Both S10mm and Sx5day show decrease over the eastern part but increase over the central and western parts. Notably, S10mm shows a marked increase (more than double) with high cross-simulation agreement over the central TP. Significant increases in all four indices are found over the Tarim and Qaidam basins, and northwestern China north of the TP. The projected changes show topographic dependence over the TP in the latitudinal direction, and tend to decrease/increase in low-/high-altitude areas.

This study presents future projection of extreme temperature over the Korean Peninsula (KP) under the global warming levels (GWLs) of 2.0℃ and 1.5℃. For projection, bias-corrected large ensemble of the Regional Climate Model in CORDEX-East Asia Phase 2 is used. Under GWL of 2.0℃ (1.5℃), extreme minimum and maximum temperature intensity indices (TNn and TXx) would increase by 2.61℃ (1.91℃) and 2.38℃ (1.58℃) compared to present period. Extreme temperature frequency indices (TN10p and TX90p) would decrease 17.7 days (14.0 days) and increase 30.9 days (20.1 days). In addition, extreme cold and warm events occurring once every 20 years during present period are expected to change a reoccurrence of 28.9 years (22.6 years) and 5.9 years (7.2 years). Meanwhile, there are several noteworthy points in the projection. The extreme temperature over the KP would increase over the northern KP than the southern KP, which is more distinct in TNn than TXx. The spatial pattern in TX90p is similar from that of TXx but the pattern in TN10p is different from that of TNn. In other words, a smaller increase in TN10p is expected over the inland KP.

This study comprehensively examines the evolving feedback mechanisms shaping water availability in Africa, particularly emphasizing the critical need for adaptive measures in response to severe drought events. Drawing from an extensive dataset that incorporates satellite-based information, observational data, CORDEX AF44 RCP8.5, and CMIP6 SSP585 scenario, the research employs advanced indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI). Methodologically, the investigation analyzes drought trends spanning 1983-2005 and projects into the future (2051-2100). Noteworthy findings reveal a conspicuous reduction in drought severity anticipated from the 2070s-2080s afterward, underscoring the necessity of addressing evolving drought conditions. The study highlights the intricate nexus between water availability and soil moisture; it even recorded a positive sensitivity coefficient, emphasizing the pivotal role of considering soil moisture feedback and moisture convergence in estimating water availability. Mean flow convergence is a substantial contributor, constituting approximately 55% of the overall moisture convergence across the African continent. These nuanced insights advocate for a holistic understanding of evolving feedback mechanisms, emphasizing the necessity for proactive measures to mitigate climate-induced water scarcity. This research has significant implications for informing effective water resource management and adaptive strategies, especially in the context of prolonged drought episodes in Africa.

In East Asia, about half of the annual precipitation occurs during the summer, with the East Asian Summer Monsoon (EASM) region being affected by various temporal scale variability factors, making its prediction challenging. Therefore, understanding the characteristics of the East Asian summer precipitation and improving simulation performance through optimal experimental design are essential meteorological tasks. In particular, summer precipitation in East Asia, mainly convective, requires consideration of heat, moisture, and momentum exchanges between the land, ocean, and atmosphere, and mixing processes in the boundary layer triggered by the complex topography of East Asia. Hence, elements such as the Cumulus Parameterization Scheme (CPS) and the Planetary Boundary Layer (PBL) scheme significantly impact the simulation of these processes. The 2022 East Asian summer precipitation was similar to the normal year, but the Korean Peninsula recorded the highest hourly rainfall since observation by the Korea Meteorological Administration (KMA) began, showing a different pattern from the normal year. This study examined the differences in the simulation of summer precipitation in the East Asian region in 2022 using the Weather Research and Forecasting (WRF) model based on PBL schemes and CPS combinations. The PBL scheme is vital in describing the interactions between convection and horizontal flow within the boundary layer. Among various PBL schemes, this study employed the nonlocal Yonsei University (YSU) scheme and the combined local and nonlocal closure Asymmetric Convective Model version 2 (ACM2) to compare the simulation performance and precipitation characteristics according to different physical schemes. We also investigated the impacts on weather prediction through differences in the vertical structure and large-scale environmental field.

In this study, the performance of five RCMs and their ensemble mean for present-day climate simulations are evaluated. RCMs have relatively high performance for climatology patterns for Korea peninsular, East China and Japan, while relatively low performance is observed for the Tibetan plateau and India. Future climate changes under four SSP scenarios are analyzed for late 21st century (2081 ~ 2100) compared to present day (1995 ~ 2014). East Asia is expected to experience temperature increases of 2.4℃ to 6.2℃ and precipitation increases of 6.7% to 12.6%, with stronger changes in higher-emission scenarios. Among the five RCMs, HadGEM3-RA projects the largest increase in temperature while GRIMs is characterized by the strongest increase in precipitation. In line with mean warming rates, warm extreme days (TX90p) are projected to increase by 35.7 ~ 93.3 days and cold extreme days (TN10p) are projected to decrease by 23.4~35.2 days. The results of this study can be used as a reference for future detailed analyses of East Asian climate changes and its impacts as well as for assessing the importance of carbon neutrality.

In the climate scientific community, there has been a recent emergence of deep learning as a new approach for downscaling large-scale atmospheric fields to a regional scale. Our study focuses on the added value of integrating a coupled-monsoon-trained deep learning architecture in downscaling the drivers of Compound heatwave and drought (temperature and precipitation) during the passage of the West African monsoon flow. In our study, we design three deep learning frameworks (CNN, UNET, and UNET++) following the perfect prognosis approach with a primary target of exploring each model's transferability ability in warm and cold conditions. Our study's findings highlight the significant contribution in terms of the added value of deep learning coupled-monsoon architecture to high-elevation regions, particularly in the central Sahel region. Conclusively, by implementing this approach, we intend to improve the accuracy of climatic information for present and future compound events risk over the West Africa region.

In forthcoming scenarios, the African continent is expected to face significant challenges in water availability due to soil dryness and low air aridity index, with higher vapor pressure deficits (VPD). This study examines the complex interaction relationship between soil moisture (SM), precipitation (P), and evapotranspiration (E), and how they relate to drought. Specifically, the focus is on understanding changes in SM, VPD seasonal dynamics, and drought conditions using the Standardized Precipitation-Evapotranspiration Index (SPEI). Using various data sources such as observations, satellite-based data, reanalysis data, CORDEX RCMs under the Africa 44 domain, and CMIP6 GCMs, we analyze historical 1981-2005 and future climate scenarios 2051-2100. Positive feedbacks in the SM-(P-E) relationship show a positive varying coefficient, except for an unusually high level observed in satellite data. According to the SPEI with a 1-month interval, a shift from wet to dry conditions is projected around 2085. Future conditions under RCP8.5 suggest less severe "Extreme" and "Very" dry periods compared to the past, while the study recorded GCMs indicate a higher intensity and frequency of drought than RCMs. Additionally, VPD is notably active in the Sahel and Southern Africa, with future projections anticipating a stronger relationship between SM and VPD in summer compared to winter. These findings underscore the pressing need for proactive water resource management strategies in the face of anticipated challenges, emphasizing the importance of understanding the intricate dynamics between land-atmosphere interaction for effective drought mitigation and adaptation efforts.

As temperatures rise, businesses and households will need more energy to keep cool, increasing the cooling demand. Several studies used climate model simulations to show that climate change will likely increase cooling demand in many parts of the world. Climate models are the primary source of long-term climate information and are known to have biases. However, these biases are not explicitly discussed while investigating the cooling demand. The present study addresses how climate model biases and bias adjustment of heat stress may play a role in assessing cooling demand. Cooling degree days are calculated as a proxy for temperature and human discomfort to excess heat to investigate the cooling demand. Estimation is performed using climate information from an ensemble of general circulation models (MRI-CGCM3, MIROC5, CCSM4), reanalysis data (JRA25), and regional climate models (NHRCM, NRAMS, WRF) forced by general circulation models and reanalysis before and after the bias adjustment. Results are validated using cooling degree days based on observed temperature data from NARO. Reduction in temporal deviations for cooling degree days based on forcing data and corresponding regional climate models is noticed after the bias adjustment, indicating its effectiveness. Detrended cooling degree days suggest that changes in fluctuations after bias adjustment may be attributed to reduced temperature biases. The change is more prominent in cooling degree days determined from forcing data, indicating that the biases in coarser resolution data may significantly impact estimating cooling demand more than in regional climate simulations. These findings show how model biases and bias adjustments may affect the representation of cooling demand, highlighting the need to consider such uncertainties in future studies.

Compound hot extremes (CHEs)—the concurrence of daytime and nighttime heat—have been increasing under anthropogenic warming, causing serious damage to human society and ecosystems. However, the anthropogenic fingerprint in past and future changes in daily CHEs and the corresponding fingerprint-constrained population exposure remain unclear. Here, using a fingerprint-based detection and attribution method, we quantify contributions of different external forcings to the historical increase in CHEs by defining three daily-scale metrics: the probability ratio (PR) of CHEs, and the proportion of CHEs in the number of extreme hot days/nights (PTday/PTnight). All three metrics have increased significantly by 98.04%, 139.67%, and 141.91% globally from 1950 to 2014, especially in Europe and North America. We find it is very likely (>90%) for human to leave fingerprints in increasing daily CHEs. The increases in PR, PTday, and PTnight that are attributable to greenhouse gas emissions are 113.62%, 144.88%, and 145.26%, respectively. By the end of the 21st century under a high-emission scenario, our fingerprint-constrained projections indicate that these metrics would increase to approximately 131.23, 0.41, and 0.43, respectively, which is a significant reduction in magnitude and uncertainty, relative to the raw projections. In some regions such as the mid and high-latitudes, almost all daytime or nighttime extreme-heat events would become CHEs. We further find that CHEs disproportionately affect densely populated areas in fingerprint-constrained projections of population exposure. Our results indicate that adaptive measures are required to alleviate the increasing proportion of CHEs and the disproportionate population exposure in densely populated areas.

The Madden-Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are prominent components of the intraseasonal oscillations over the tropical Indo-Pacific Ocean. This study examines the tropical cyclone (TC) genesis over the Bay of Bengal (BOB) and the South China Sea (SCS) on an intraseasonal scale in May−June during 1979−2021. Results show that the convection associated with the two types of intraseasonal oscillations simultaneously modulates TC genesis in both ocean basins. As the MJO/QBWO convection propagated, TCs form alternately over the two basins, with a significant increase (decrease) in TC genesis frequency in the convective (non-convective) MJO/QBWO phase. Based on the anomalous genesis potential index associated with the MJO/QBWO, an assessment of the influence of various factors on TC genesis is further assessed. Middle-level relative humidity and lower-level relative vorticity play key roles in the MJO/QBWO modulation on TC genesis. The MJO primarily enhances large-scale cross-equatorial moisture transport, resulting in significant moisture convergence, while the QBWO generally strengthens the monsoon trough and induces the retreat of the western North Pacific subtropical high, increasing the regional lower-level relative vorticity. The potential intensity and vertical wind shear make small or negative contributions. This study provides insights into the neighboring basin TC relationship at intraseasonal scales, which has a potential to improve the short-term prediction of regional TC activity.

Under global climate warming and local anthropogenic influence, urban areas are threatened by increasingly serious heat extremes across the globe, posing a great threat to human health and the survival of city residents. Using global climate reanalysis and local daily air temperature datasets since the 1960s, we disentangle the synergies between urban heat island (UHI) and compound heat extremes in Chinese megacities of different climate zones. We find that the heat extremes in China have nearly tripled in frequency, intensity, and duration since this century. The nighttime- and compound-heat extremes remarkably amplified in cities, and account for 76.3% of extreme heat events in the past decade. After classifying the weather stations into urban and non-urban types with LULC maps and quantifying the urban impacts of amplified heat extremes in four metropolitan areas of China, we find the BTH region seems to be the most vulnerable region suffering from urbanization-accelerated heat extremes, with a relative contribution of the frequency, intensity, and duration by nearly 55.3%, 51.7%, 55.4%, respectively. Overall, urbanization has a positive contribution to the frequency, duration, and intensity of high temperatures in the BTH, YRD, and SCB regions. The PRD does not clearly match, possibly due to the strong influence of land-sea circulation and the geographical location near the equator. Urbanization-accelerated heat extremes are more pronounced in temperature climate zones, which may be attributed to the stronger UHI intensity resulting from lower wind speed and less evapotranspiration.

Sub-seasonal prediction of regional compound heatwaves and their predictability sources remain unclear. In this study, the underlying mechanisms associated with a long-lasting compound heatwave occurred from 1-18 July 2010 over southern China, and the major sources of its sub-seasonal prediction skill are identified. The result shows that both the development and decay of this compound heatwave are mainly dominated by atmospheric processes (i.e., adiabatic heating associated with anticyclonic circulation), whereas the land-atmosphere coupling processes play an important role in sustaining the heatwave. Further analysis indicates that the tropical intra-seasonal oscillation with periods of 30-90 days and 10-30 days produce anticyclonic circulation over southern China and facilitate the occurrence and maintenance of the heatwave during its entire and second half periods, respectively. The National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) shows a low skill in predicting the 2010 compound heatwave over southern China when the lead time is longer than 2-pentad leads, which is mainly attributed to the model’s bias in representing the intensity and phase of intra-seasonal oscillations.

The frequency and intensity of summer heatwaves have been increasing in China over the past few decades. Long-term records of such extreme events can be used to put the recent changes in a long-term context. That is essential to understanding the drivers of the long-term variations and changes of the high-temperature extremes. However, the observational records of the heatwave events are short, and cannot cover the long time period, especially before the industrial era. The tree-ring proxy has the potential to infer the variability of summer heatwaves over the past time, and has been successfully extend the variation of the frequency of the summer heatwaves over inner East Asia. Here we quantify the relationships between the variability of the radial growth of trees and the variabilities of the frequency, intensity and duration of the heatwaves over China on various timescales based on the publicly-available tree-ring width data in China, and the heatwave features calculated based on the ERA5 dataset. The results show overall increasing trends in the frequency, intensity and duration of the summer heatwaves over all the tree-ring sites in China for the past few decades. In contrast, the trees over the different sites exhibit distinct trends in their radical growth, with some showing increasing trends and some showing radical declines in the growths over the recent two decades. The statistical analysis shows that the correlation between the tree-ring growth and the heatwave frequency in summer changes from negative to positive with the increase in the elevations of the tree-ring sites. Despite an overall positive response, tree-ring growths show negative responses to the heatwave frequency in some high-elevation sites. Further results will show the spatial pattern and reasons of the response diversity of the tree-ring growth to the summer heatwaves in China over the past decades.

Meteorological data assimilation (DA) combined with the online coupled chemistry meteorology model (CCMM) can improve the air quality forecasts, as it helps reduce the uncertainties of meteorological initial conditions while considering the interaction between meteorology and air quality. In this study, the effects of meteorological DA on weather and particulate matter (PM) forecasts of the online CCMM are examined. Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and WRFDA three-dimensional variational (3DVAR) system are used. The nesting domains of the model represent East Asia (outer domain) and the Korean Peninsula (inner domain), respectively. The two high PM concentration cases were selected, and four experiments with different DA domains were conducted for each case. When the meteorological DA was performed in the outer domain, the forecast errors (root-mean-squared error (RMSE) and bias) of meteorological variables were the smallest, while the errors were the largest in the experiment without DA. In addition, when the meteorological observations were assimilated in the outer domain, the forecast errors of PM concentration were the smallest. The effect of the meteorological DA on the PM forecast error reduction lasted approximately 58-66 hours. The synoptic meteorological forecasts were improved by performing meteorological DA in the outer domain. The improved forecasts in the outer domain enhanced the meteorological initial and boundary conditions of the inner domain, which consequently led to the improvement of the meteorological and PM forecasts in the inner domain. Acknowledgements: This work was supported by a grant from a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2021R1A2C1012572) and Yonsei Signature Research Cluster Program of 2023 (2023-22-0009).

China has experienced increasingly serious ozone (O₃) pollution in recent years, posing a great threat to agricultural ecosystem and human health. Here, we analyzed ozone trends across China using multi-source observations combined with multi-model calculations. Ozone increases steadily in China between 2013−2022, with a fast increase rate of 4.4 μg m⁻³ yr⁻¹ in Phase I of Action Plan and a much smaller 0.6 μg m⁻³ yr⁻¹ in Phase II. Results highlight that the deteriorative O₃ pollution in Phase I and early Phase II is dominated by the nonlinear O₃-emission response. Persistent decline in O₃ precursors has shifted its chemical regime in urban areas and began to show a positive influence on ozone mitigation in recent years. Meteorological influence on O₃ variations is minor until 2019 (~10%), but it greatly accelerates or relieves the O₃ pollution after then, showing comparable contribution to emissions. Strong connections between surface O₃ levels and SSC synoptic weather patterns are identified, with the dry tropical (DT) weather pattern featured by dry and hot air masses as the main contributor to 35.3% of the high ozone occurrences and partly explaining the observed ozone trends in recent decade (e.g., +9.7% ozone increase per tripling of the DT frequency). Using the agricultural statistical data and empirical algorithms, it is estimated that the annual yields of winter wheat, single-cropping rice, double-cropping early rice and late rice are reduced by 9.0−17.0%, 6.0−8.2%, 4.8−9.4% and 3.9−7.0%, respectively, in China during 2014–2022 due to the ozone exposure. Epidemiological model predicts totally 0.8−3.0 thousand yr⁻¹ more deaths across China with altered anthropogenic emissions since clean air actions, and additional health burdens by −1.5~0.3 thousand yr⁻¹ from perturbated meteorology. This study calls for stringent emission control and climate adaptation strategies to attain the ozone pollution mitigation in China.

The rapid development of Chinese cities is accompanied by air pollution. Although the implementation of air pollution control strategies in recent years has alleviated PM_2.5 pollution, O₃ pollution and the synergistic pollution of PM_2.5 and O₃ have become more serious. To understand the underlying chemical interaction mechanisms between PM_2.5 and O₃, we applied the modified Weather Research and Forecasting model with Chemistry (WRF-Chem) to study the effects of aerosol-photolysis feedback and heterogeneous reactions on the two pollutants and revealed the contribution of different mechanisms in different seasons and regions in Yangtze River Delta (YRD) in eastern China. We found that, through the aerosol-photolysis feedback, PM_2.5 decreased the surface photolysis rates J_NO2 and J_O1D, resulting in a decrease in O₃ concentration in the VOC-sensitive area and a slight increase in the NO_x-sensitive area. The heterogeneous reactions reduced O₃ concentration in the YRD in spring, autumn and winter by consuming H_xO_y. While in summer, the heterogeneous absorption of NO_x decreased O₃ in the NO_x-sensitive areas and increased O₃ in the VOC-sensitive areas. Heterogeneous reactions also promoted the secondary formation of fine sulfate and nitrate aerosols, especially in winter. Through the combined effect of two chemical processes, PM_2.5 can lead to a decrease in O₃ concentration of -3.3 ppb (-7.6%), -2.2 ppb (-4.0%), -2.9 ppb (-6.3%), and -5.9 ppb (-18.7%), in spring, summer, autumn and winter in YRD. Therefore, if the PM_2.5 concentration decreases, the weakening effect of PM_2.5 on the ozone concentration will be reduced, resulting in the aggravation of ozone pollution. This study is important for understanding the synergistic pollution mechanism and provides a scientific basis for the coordinated control of urban air pollution.

Land-sea atmosphere interactions (LSAIs) are important processes affecting ozone (O₃) pollution in coastal areas. While the effects of small-scale LSAIs, such as sea-land breezes, have been widely studied, the impact of large-scale LSAIs on O₃ pollution is not yet fully understood. Here we investigated an O₃ episode to illuminate the role of large-scale LSAIs in O₃ pollution in the southeastern coastal, Bohai-Yellow Seas and adjacent regions through observations and model simulations. The results are presented below. 1) O₃ pollution in southeastern coastal cities and the northern Bohai Sea’s coastal regions was mainly caused by transport from over the eastern China seas (ECS), the Bohai-Yellow Seas, respectively. 2) Affected by the Mongolian High, the study regions initially experienced a typical unimodal O₃ diurnal variation. Precursors emitted from Beijing-Tianjin-Hebei, Northeast China (NEC) and Japan-Korea were transported to ECS, and precursors from Beijing-Tianjin-Hebei, Shandong, and NEC were transported to the Bohai-Yellow Seas. Photochemical reactions produced O₃ in marine air masses, causing higher O₃ levels over the sea than in coastal regions. As the Mongolian High shifted eastward and expanded, northeasterly winds transported O₃-rich marine air masses from over ECS to the southeastern coastal regions, and southerly winds transported them from over the Bohai-Yellow Seas to their coastal region, which prolonged pollution and weakened diurnal variations. 3) In the southeastern coastal, Japan-Korea contributed the most, with an average of about 5 ppb and a peak of up to 30 ppb. The contributions of Beijing-Tianjin-Hebei and NEC were comparable, with an average of about 2 ppb and hourly peaks of 19 and 10 ppb, respectively. In the northern Bohai Sea, Shandong’s emission contributed significantly in both phases (27.5% and 26.1%, respectively), and emissions from the Korean Peninsula and marine shipping had a notable impact on O₃ during the second phase (10.7% and 13.7%, respectively).

The urban landscape pattern, which can affect the air pollutants distribution in urban areas, was significantly changed due to the expansion of built-up land. Deeply understanding of the mechanisms by which urban landscape patterns influence the PM2.5 concentration distribution is fundamental for urban pollution control. However, the relationship between the built-up land landscape pattern and PM2.5 concentration distribution is still unclear at different grid scales in different seasons. In this study, four landscape metrics were calculated to quantify the distribution characteristics of built-up land in Nanjing, and the impact of built-up land landscape patterns on PM2.5 concentration distribution at different grid scales in different seasons was further analyzed. Results showed that the dominant landscape pattern metrics affecting PM2.5 concentration in different areas of Nanjing was closely related to the topography: in the main urban areas with flat terrain, the aggregation, shape, and proportion of built-up land significantly affected PM2.5 concentration, while the influence of the shape and proportion of built-up land was more noticeable in the hilly southwest suburbs. The effect of built-up land landscape pattern on PM2.5 concentration in winter was more significant than that in summer in general, and the relatively greater seasonal differences in this effect can be seen in the aggregation and proportion of built-up land. With the grid increasing, the correlation between the built-up land landscape pattern and the PM2.5 concentration distribution increased in the main urban areas but mostly decreased in the southwest suburbs, indicating that the PM2.5 concentration distribution in the main urban areas was mostly contributed by the spatial transmission of pollutants, while the PM2.5 concentration distribution in the southwest suburbs was mostly dominated by local emissions. We suggest that the planning of flexible urban strategies with respect of PM2.5 pollution control in different areas should be implemented considering the influence of topography.

Monitoring and predicting fine particulate matter (PM2.5) concentrations is crucial for public health, especially in urban areas where air pollution is a significant risk. Understanding factors influencing PM2.5 variabilities – such as synoptic-field changes, interannual and seasonal variability, and climatic indices – is vital for effective air quality management and public health protection in densely populated and industrially advanced regions like East Asia. The Arctic Oscillation (AO) significantly influences regional weather patterns, potentially affecting air quality in urban areas like Seoul, Korea. This study investigates the relationship between PM2.5 concentrations in Seoul and the AO using ECMWF Reanalysis v5 (ERA5) and the Weather Research and Forecasting (WRF) model. From 2015 to 2023, we analyzed daily average PM2.5 concentrations from 48 air quality stations in Seoul, focusing on November to April, which typically experience numerous high concentration events. A regression analysis was conducted to understand the impact of AO phases on Seoul's air quality. This analysis identified that PM2.5 concentrations correlate with various weather variables, showing different patterns during positive and negative AO phases. These correlations uncovered the influence of the AO on air quality. Moreover, accurate forecasting of high PM2.5 concentration events is vital for public health and environmental management. Thus, we also evaluated the WRF model's performance in forecasting weather variables during high pollution events to enhance predictive accuracy and preparedness. This research found the impact of the AO on Seoul's air quality, verifying the interaction between synoptic-scale weather variables and urban-scale air pollution. Consequently, forecasting local-scale PM2.5 concentrations in East Asia must consider dealing with climate indices, particularly the AO phase.

Particulate matter (PM_2.5) is generated not only by primary emission sources but also by secondary processes from precursors in the atmosphere. For this reason, the Ministry of Environment has recently implemented measures to reduce precursor emissions from power plants and on-road mobile sources nationwide. Despite the intensive efforts, the PM_2.5 concentration remains steady both in the east and west regions of Gangwon province. To establish effective PM_2.5 reduction measures in the Gangwon province, it is necessary to identify not only its emission sources in the regions but also the PM_2.5 contribution of precursors. In addition, the Yeongseo (west) and Yeongdong (east) regions have different emission source characteristics in Gangwon province. This study aims to evaluate the PM_2.5 contribution to air pollutants emitted from the Yeongseo and Yeongdong regions, separately in February 2023. We utilized the CMAQ-BFM(Brute Force Method) to calculate the PM_2.5 contribution of air pollutants. PM₁₀, PM_2.5, SO₂, NO_X, NH₃, and VOCs are chosen for emitted air pollutants in the contribution analysis. The simulation results of PM_2.5 concentration were verified using data from urban air quality monitoring stations in the study area. Results showed that emission contribution from the Yeongseo region was approximately 20%, while that from Yeongdong region was approximately 10%. We estimate the contribution of each air pollutant for each region individually. Acknowledgment" "This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)."

Fine particulate matter (PM2.5) episodes in Korea were primarily known to be originated from the inflow of pollutants from upwind countries and regions or occur due to atmospheric stagnation. To effectively manage such high PM2.5 concentrations, this study classified the occurrence types of high-concentration PM2.5 cases by utilizing an atmospheric recirculation factor (RF) and the concentrations of PM2.5 components (SO42-, NO3-, NH4+). Additionally, we quantitatively analyzed the dominance of inter-regional influence through time-lag correlation analysis. The target regions included Seoul, Ansan, Seosan, Daejeon, Gwangju, Ulsan, and Jeju, where the atmospheric environment research institutes operated by the national institute of environmental research are located. The study period is from January to December in 2021. Hourly concentration data of PM2.5 and its composition components, along with local meteorological forecasting model (LDAPS) data from the Korea Meteorological Administration, were analyzed. Four types of high-concentration cases were identified: inflow from abroad, stagnation after inflow, inflow from other regions, and atmospheric stagnation. As a result, the frequencies of inflow from abroad and atmospheric stagnation after inflow were high in the western coastal and metropolitan regions. Additionally, it was confirmed that sulfate and nitrate components are suitable indicators for foreign inflow and atmospheric stagnation, respectively. This work was supported by “the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2023M3G1A1090663).” and "This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE) (C10168180002)."

As one of the precursors of severe storms, convective clouds are a special type of clouds that play a critical role in the global hydrological cycle and radiative energy budget at a local or global scale. Due to complex physical and dynamic processes and large spatiotemporal variability, convective clouds especially for the small spatial scales contribute to significant uncertainties in existing weather and climate models' simulations. Convective cloud detection and tracking is one of the crucial tasks to understand their development and evolution characteristics. Based on the Himawari-8 data with higher spatial and temporal resolutions, a convective cloud detection and tracking algorithm was constructed by combining machine learning, region-growing, area-overlapping, and Kalman filter algorithms. First, a complicated and strict spatiotemporal matching strategy between the Himawari-8 AHI data and CloudSat cloud profile radar was established, such as parallax correction model was constructed to minimize the impact of parallax. In addition, to expand the sample volume of convective clouds, region-growing algorithm is further used. Then, a convective cloud detection model suitable for both day and night time was constructed based on the XGBoost machine learning model. The convective cloud detection results are extensively evaluated by comparisons with cloud property products of JAXA AHI, traditional threshold algorithm, and CloudSat data. In addition, to achieve tracking of large-scale, small-scale, and fast-moving convective clouds, the automatic tracking of convective clouds algorithm is achieved by comprehensively using the area overlapping and Kalman filter algorithms. Validation results indicate that the algorithm developed can detect convective clouds of different scales with high accuracy, and the results show good continuity during day-to-night transitions.

Effective Decadal/near‐term climate prediction can provide key climate information one or several decades in advance for policymakers and stakeholders, it has recently developed a high profile in the scientific community and beyond. However, current decadal climate prediction skill is far from successful. Our studies provide a new decadal increment method, which can skillfully predict the decadal variabilities of east Asian summer monsoon，summer precipitation over North China, extreme precipitation over South China and Pacific Decadal Oscillation. There are three steps in the increment method. First, the decadal variability (DV) of predictand is obtained by applying a 5‐year running mean. Second, the 3‐year decadal increment (DI) of the DV (DV at the current year minus the DV at the 3 years before, DI_DV) is predicted by a statistical forecast model with the leading several years of predictors in DI form. Third, the predicted DI_DV is added to the observed DV at 3 years ago to obtain the final DV prediction. This decadal increment method provides a promising and valuable approach to decadal climate prediction.

Winter precipitation anomalies in South China (SC) frequently result in severe disasters. However, the evaluation of prediction performance and distinctions between positive precipitation anomaly events (PPA, wet condition) and negative precipitation anomaly events (NPA, dry condition) in current operational models remains incomplete. This study employed the Climate Forecast System version 2 (CFSv2) to assess winter precipitation prediction accuracy in SC from 1983 to 2021. Differences in predicting PPA and NPA events and the underlying physical mechanisms were explored. The results indicate that CFSv2 can effectively predict interannual variations in winter precipitation in SC, as there is a significant time correlation coefficient of 0.68 (0.62) between observations and predictions, with a lead time of 0 (3) months. The model revealed an intriguing asymmetry in prediction skills: PPA outperformed NPA in both deterministic and probabilistic prediction. The higher predictability of PPA, as indicated by the perfect model correlation and signal-to-noise ratio, contributed to its superior prediction performance when compared to NPA. Physically, tropical signals from the ENSO and extratropical signals from the Arctic Sea ice anomaly, were found to play pivotal roles in this asymmetric feature. ENSO significantly impacts PPA events, whereas NPA events are influenced by a complex interplay of factors involving ENSO and Arctic Sea ice, leading to low NPA predictability. The capability of the model to replicate Arctic Sea ice signals is limited, but it successfully predicts ENSO signals and reproduces their related circulation responses. This study highlights the asymmetrical features of precipitation prediction, aiding in prediction models improvement.

In order to improve the ability of the CMA global ensemble prediction system (CMA-GEPS) to forecast extreme weather, aiming at the difficulty of reasonably calculating model climate due to small samples of historical forecasts from CMA-GEPS together with the lack of the re-forecast dataset, a method to build the model climate required by extreme forecast index (EFI) was investigated via using the insufficient samples of deterministic forecasts through extending the forecast samples in both time and space. By employing the operational forecast data of CMA-GEPS and the ERA5 reanalysis dataset, the forecast ability of CMA-GEPS for extreme high temperature in four representative regions at home and abroad for the summer of 2022 was evaluated. Results from the relative operating characteristic curve showed that CMA-GEPS EFI had the ability to discriminate extreme high temperature within the short- and medium-range forecast lead times of 1-10 days. Taking the maximum TS score as the criterion, the critical thresholds of EFI for issuing warning signals of extreme high temperature was determined. The forecast ability of EFI was decreased with the increase of forecast lead times, and different performance was exhibited in different regions: the forecast ability of extreme high temperature in the middle and lower reaches of the Yangtze River in China was higher than that in North China for all lead times; the forecast ability of EFI in western Europe was better than that in central Europe for the 1-7-d lead times, meanwhile the EFI forecast ability in central Europe for the 8-10-d lead times was better. Evaluation results from the economic value model revealed that risk decisions based on the EFI forecast information owned certain economic value. A case study further demonstrated that the CMA-GEPS EFI could provide early warnings for extreme high temperature in the medium forecast range.

The Korea Institute for Atmospheric Prediction Systems (KIAPS) was established in 2011 with a mission to develop a global atmosphere-only numerical weather prediction system for operational use at the Korea Meteorological Administration (KMA). This system was completed on schedule, and made operational at KMA in April 2020, immediately giving a world-class performance. The system is based on a new atmospheric model called the "Korean Integrated Model" (KIM), which is based on a cubed-sphere grid and uses the spectral element method within its dynamical core. Deterministic data assimilation (DA) is based on a hybrid-4DEnVar algorithm, and forecast uncertainties are modelled by a 50-member Ensemble Prediction System (EPS). The EPS is based on a local ensemble transform Kalman filter (LETKF) DA algorithm, and further schemes are included to account for deficiencies and uncertainties in the DA process and the forecast model. In this presentation, we examine the sensitivity to stochastically perturbed physical tendencies (SPPT), which aims to account for uncertainties in the forecast model and test a three-scale pattern, which consists of a linear combination of three independent random patterns, each describing a different correlation scale. To reduce computational costs, the sensitivity tests were carried out within a low-resolution framework, and we analyzed the effect of the schemes on the ensemble mean error and spread in temperature, geopotential height, and specific humidity in extended medium-range forecasts. In regions where the control experiment, including only initial perturbations, did not adequately reflect the ensemble spread, it is anticipated that the SPPT approach will result in increased forecast skill.

The Afro-Asian monsoon is a major component of the global monsoon system, with a large-scale rainbelt covering Africa, South Asia, and East Asia. Previous studies have demonstrated that the Afro-Asian summer monsoon (AfroASM) rainfall exhibits prominent decadal variability. By investigating observations and ensemble simulations, this study reveals that the decadal variation of the rainfall in the Sahel and eastern China is mainly caused by the interdecadal Pacific variability (IPV) and the Atlantic multidecadal variability (AMV), with positive (negative) phase of the AMV contributing to intensified (weakened) AfroASM rainfall, and the IPV corresponds to opposite impacts. The AMV and IPV influence AfroASM rainfall by modifying the tropical easterly jet (TEJ). Positive AMV favors a stronger TEJ, but positive IPV leads to a weaker TEJ, vice versa for their negative phases. The AMV elicits more contribution in the exit region of the TEJ, while the IPV contributes more in its entrance region, which concurs that the rainfall in the Sahel receives a larger impact from the AMV, and the IPV influences eastern China rainfall more strongly.

Extensive observational and modeling studies demonstrate the influence of urbanization on the intensity of convective storms and extreme precipitation. Nevertheless, the impact of urbanization on the tornado triggering environment is relatively lacking at present. Here we examine the impact of anthropogenic aerosol emission and land surface changes on the triggering environment of an EF3 supercell tornado on 14th May, 2021 in Yangtze River Delta, a rapidly urbanized region in China. The event is simulated by Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) with an updated land use category. Significant Tornado Parameter (STP) is used to assess the tornado-triggering environment based on evaluation of convective available potential energy, wind shear, storm relative helicity, and lifting condensation level. Sensitivity tests show that anthropogenic aerosols due to urbanization enhances tornado-triggering environment, with the increase of storm relative helicity (SRH1) dominates the enhancement. Meanwhile, condensation of aerosol releases more latent heat, changes the airflow, and leads to the increase of SRH1. Moreover, change of land surface alters the sensible and latent heat fluxes, thus affecting the dynamical and thermodynamical conditions. Together, changes of aerosol and land surface due to urbanization influence the environment that are conducive to tornado formation.

Urban areas worldwide are increasingly threatened by climate-driven risks. In response to the need for urban climate adaptation, an explicit urban representation in global climate models contributes to realistically simulating urban heat and water fluxes across regions. This study attempts to enhance representation of urban surface heterogeneity within the Community Earth System Model (CESM), by developing a new scheme to represent urban areas based on local climate zones (LCZs). In CESM's land component, the Community Land Model (CLM), we incorporated the urban LCZ scheme as an option alongside its default scheme. The CESM-LCZ scheme increases the subgrid-level urban land use types to 10 LCZs, with each LCZ represented by a unique set of urban canopy parameters (UCPs). The modified model is verified by present-day simulations and applied for future urban climate projections. We envision that the global-scale simulations of urban climates will provide scientific support for broader communities, enabling them to conduct assessments of LCZ-related impacts and inform urban planning decisions. 

Hydrometeorological disasters caused by extreme precipitation pose significant hazards in the Philippines, underscoring the urgent need for a robust monitoring system to safeguard human lives and assets in the country. This study proposes a tool for identifying potential extreme rainfall events using average recurrence interval (ARI). To enhance the reliability of ARI statistics beyond historical records, we employed Regional Frequency Analysis (RFA) across the Philippines, utilizing TRMM 3B42 rainfall data. Following the methodology outlined by Hosking and Wallis (1997), rainfall data were first pooled into homogeneous regions before estimating precipitation quantiles for different ARIs. Our findings reveal that RFA produced a lower Root Mean Square Error (RMSE) compared to traditional frequency analysis, signifying the reliable implementation of resulting ARI statistics for monitoring applications. To illustrate, three past cases of extreme rainfall events that vary in intensity and impact were examined. The equivalent ARI maps of these events captured areas that experienced flooding and landslides associated with the extreme rainfall event. Expanding our analysis, we applied ARI thresholds for extreme rainfall detection to our experimental WRF forecasts, comparing the outcomes with results based on monthly climatological rainfall thresholds. This comparative evaluation demonstrated the added value of ARI thresholds in detecting rainfall amounts surpassing monthly climatological levels, providing crucial insights into extreme weather monitoring. Lastly, recognizing the importance of medium-term rainfall events lasting up to a week, initial assessments exploring the physical and statistical properties of medium-term events revealed a seasonal pattern in different regions, particularly demonstrating improved homogeneity in areas affected by tropical cyclone disturbances, while variability remains over Mindanao and the Eastern Philippines during the northwest monsoon season. Despite these regional nuances, there is potential in using ARI for effective extreme weather monitoring applications, emphasizing its relevance across diverse temporal scales and geographical contexts.

Climate change leads to more frequent and intense extreme temperature events, causing a significant number of excess deaths in cities in China. Using an epidemiological approach, we analyze all-cause deaths related to heatwaves and cold spells in 2,852 Chinese counties from 1960 to 2020. Economic losses associated with these events are determined through the value of statistical life. Findings reveal that cold-related cumulative excess deaths (1,133 thousand) are approximately 2.5 times higher than heat-related deaths, despite an increase in heat-related fatalities in recent decades. Monetized mortality due to heat-related events is estimated at 1,284 billion CNY, while cold-related economic loss is 1,510 billion CNY. Notably, cities located in colder regions experience more heat-related excess deaths, and vice versa. Economic development does not significantly reduce mortality risks to heatwaves across China. This study provides insights into the spatial-temporal heterogeneity of heatwaves and cold spells mortality, essential for policymakers ensuring long-term climate adaptation and sustainability.

Studying on the impacts of block-scale urban morphology on strong wind holds significant implications for predicting local severe winds, which have the potential to trigger safety and economic hazards. In this study, the strong wind induced by typhoon "Muifa" (2212) in Shanghai, China, was simulated using the Weather Research and Forecasting (WRF) model coupled with Parallelized Large-eddy simulation Model for Urban applications (PALM-4U). The findings were as follows: (1) Frontal area fraction (λf), building area fraction (λp), building surface to plan area ratio (λb) and height-to-width ratio (hw) showed a significant correlation with the strong wind indices (gust factor (GF) and relative wind speed (RS)). (2) The impacts of block-scale urban morphological indices on the average strong wind indices can be mainly categorized into two stages: acceleration and stabilization, and the transitional zone correspond to λf (0.07-0.09), λp (0.35-0.45), λb (1.3-1.6) and hw (2.2-2.4), respectively. (3) The accelerating effects of the urban morphological indices on the extreme features of the strong wind indices were manifested as the shifting and broadening of the probability density functions. In extreme cases, the average values would be significantly surpassed by the extreme values during the stabilization phase. The strong wind indices with 1% probability of occurrence can result in differences reaching λf (GF: 0.83, RS: 1.12), λp (GF: 1.03, RS: 1.34), λb (GF: 0.81, RS: 1.17) and hw (GF: 0.92, RS: 1.16), respectively.

In this study, four extreme precipitation indices with the highest impact on Ulaanbaatar's flood risk were chosen, and their changes over the last 60 years of climate data have been investigated. In addition, Future trends in maximum daily precipitation indices( RX1day), which are used as parameters for flood risk estimation, were estimated as well using regional ECAM5 and HadGEM2 model data. According to the findings, the number of consecutive rainy days increased from 1975 to 1994 and then decreased beginning in 1995. Throughout 1966 and 2022, observed daily maximum precipitation (RX1day) and extreme precipitation (R95p) indices rose by 2 mm and 7 mm, respectively. However, there was not a clear pattern in the number of days with precipitation greater than 20 mm (R20) across the years. However, there was no discernible trend in the number of days with precipitation of more than 20 mm (R20) over the years.

The interactions between urban heat islands (UHIs) and heat waves have been studied in many cities around the world because of their scientific interest as well as their influences on urban heat stress. However, few studies examined how these interactions vary with the characteristics of heat waves. Using observation data in Daegu, South Korea for 2001–2022, this study reveals that the interactions between UHIs and heat waves are contrasting under two distinct heat wave types (dry heat waves and moist heat waves). Dry (Moist) heat waves are defined as heat waves with daily mean relative humidity lower (higher) than its 10th (90th) percentile. Dry heat waves exhibit significantly higher temperature, lower cloud fraction, and lower soil moisture than those under moist heat waves. The mean UHI intensity, which is defined as the mean urban-rural difference in daily minimum 2-m temperature, under dry heat waves is significantly stronger than that under non-heat waves by 1.07 °C, but that under moist heat waves is weaker by 0.24 °C. This indicates that the interactions between UHIs and heat waves are primarily synergistic (negative) under dry (moist) heat waves. Interestingly, the temperature-humidity index, humidex index, and discomfort index in the urban area are not significantly different between dry and moist heat waves during the nighttime. This implies that heat waves that synergistically interact with UHIs do not necessarily lead to higher urban heat stress than those that negatively interact with UHIs. Therefore, monitoring changes in humidity under heat waves is indispensable for assessing extreme urban heat stress as well as interactions between UHIs and heat waves.

There are limited studies investigating the health implications of coarse particulate matter (PM_10-2.5). Accurate exposure assessment is important for conducting PM_10-2.5–related epidemiological studies. In this study, we aimed to develop an ensemble machine learning method to estimate PM_10-2.5 concentrations in mainland China during 2013–2020. The study was conducted in two stages. In the first stage, we developed two methods: indirect method refers to developing models for PM_2.5 and PM₁₀ separately and subsequently calculating PM_10-2.5 as the difference between PM_2.5 and PM₁₀, which was commonly used in previous studies; and the direct method refers to establishing a direct model between PM_10-2.5 measurements and relevant predictors, which few studies have done so. In the second stage, we integrated predictions from the indirect and direct methods with the generalized additive model. Internal cross validation (CV) and external CV were performed to validate the model performance. Overall, the indirect method performed better in internal CV with higher R² and lower RMSE (R²=0.91; RMSE=10.02 μg/m³), while the direct method performed better in external CV (R²=0.62; RMSE=18.48 μg/m³). Predicting accuracy was improved in ensemble model regarding both internal CV (R²=0.95; RMSE=7.17 μg/m³) and external CV (R²=0.63; RMSE=18.30 μg/m³), comparing to indirect and direct methods. The predictions produced by the ensemble model captured the spatiotemporal pattern of PM_10-2.5, even in sand and dust storms seasons. Our study provides an ensemble machine learning strategy with advantages of both indirect and direct methods for estimating PM_10-2.5 concentrations, which holds significant potential to support future epidemiological studies and address knowledge gaps in health effects of studies.

Atmospheric gas-phase chemistry simulation is crucial for assessing environmental impact, crop growth, and human health. However, numerical models' computational cost has led to simplifications or even the omission of crucial chemistry. Previous attempts to use artificial intelligence (AI) algorithms have faced the curse of dimensionality and error propagation issues. Here, we present MHSA-CSolver, a novel approach that uses Multi-Head Self-Attention to replace the SAPRC-99 mechanism's numerical solver in WRF-Chem. It marks the first successful effort that an entire complex mechanism has been successfully replaced with a single AI solver and seamlessly coupled into a numerical model, enabling fast, accurate, and stable simulations without the need for separate solvers for each species as previously required. When we compared it with other AI solvers based on Multi-Layer Perceptron and Residual Neural Network architectures, our analysis revealed significant deviations in the simulation of ETOH, C2H2, and C3H6 using all AI solvers, which could be attributed to incomplete input variables we use neglecting key intermediate species involved in their reactions. Remarkably, MHSA-CSolver outperformed other AI solvers, delivering high simulation accuracy (mean R² of 0.99, mean RMSE of 5.20 ppb, mean NMB of 1.88%, excluding ETOH, C2H2, and C3H6 from calculations) while maintaining fast simulation speed (14.3 seconds to predict concentrations of 74 chemical species across 675,000 grid cells on a single CPU). Moreover, we observed that adjusting the learning rate significantly enhanced the model's performance. Furthermore, upon coupling MHSA-CSolver into WRF-Chem, we achieved a remarkable speed improvement with a simulation time of 15.62 seconds, which is ~10.2 times faster than the original SAPRC-99 mechanism (158.93 seconds). The hybrid model successfully reproduced the spatial distribution and temporal sequence of various chemical species, particularly those challenging to simulate in previous works, such as NOx and HNO3.

The air pollution weather (APW) in Taiwan can be recognized as a specific weather type that involves intricate interactions between the weather systems across multiple scales. In this study, we are developing a data-driven framework that maps the evolution of synoptic weather to local air pollution episodes and identifies the air pollution weather in Taiwan. A volume-to-point (VTP)-based autoencoder model is established with ERA5 reanalysis from 2006-2010 as the training dataset. The VTP-based autoencoder model can identify subtle differences in synoptic weather patterns. Within the latent spaces, the autoencoder model effectively distinguishes 19 distinct types of synoptic weather patterns in boreal winter, each displaying distinct configurations of mean sea level pressure (MSLP) and geopotential height on 500 hPa (z500). Among these synoptic weather patterns, we first identify seven types that are highly correlated with APW based on the statistical analysis of air pollution index (API) in Taiwan over each synoptic weather type. For instance, W1, W2, W12, W15 and W16 are exhibiting different propagation patterns of Rossby Wave and the Siberian-Mongolian High pressure (SMH) and the air pollution dispersion in Taiwan is regulated by the propagation of the edge of SMH. W6 is characterized with deep troughs favoring the extratropical cyclogenesis and the cold fronts that passes through Taiwan, the air pollution typically occurs in the intermittence between cold fronts. W14 manifests that the pattern that the major high- and low-pressure systems stay distant from Taiwan, and the continued weak-synoptic condition favors the air pollution. This framework can also be applied on future climate projection. The mitigation of synoptic weather patterns under various future climate scenario leads to the changes in climate distribution of APW in Taiwan. This framework can serve as quantified diagnosis tool but also a potential downscaling method.

Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of the anthropogenic and meteorological drivers of surface ozone (O₃) change in China by integrating deep learning (DL) and chemical transport model (CTM) methods. The DL method suggests volatile organic compound (VOC)-limited regimes in urban areas over northern inland China in contrast to strong nitrogen oxides (NO_x)-limited regimes in GEOS-Chem simulations. Sensitivity analysis indicates that the inconsistent O₃ responses are partially caused by the inaccurate representation of O₃ precursor concentrations at the locations of urban air quality stations in the simulations. The DL method exhibits possible weakened anthropogenic contributions to surface O₃ rise in the North China Plain, for example, 1.53 and 0.54 ppb/y in 2015-2019 and 2019-2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O₃ concentrations driven by the decline in nitrogen dioxide (NO₂) concentrations. Furthermore, both DL and GEOS-Chem models suggest the reverse of meteorological contributions to the observed O₃ change in the North China Plain in 2019-2021, which is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. This work highlights the importance of DL as a supplement to CTM-based analysis. The derived O₃ drivers are helpful for making effective regulatory policies to control O₃ pollution in China.

Heatwave dynamics exhibit unique spatial heterogeneity, posing cascading environmental and societal challenges. Employing bias-adjusted climate models, we unveil these complexities by investigating heatwave dynamics, population exposure, and compound hydrometeorological extremes across 50 global regions within historical (1979-2014) and future epochs (2025-2060 and 2065-2100) under Shared Socio-economic Pathways (SSPs) 245 and 585. Our analysis reveals intricate couplings between heatwave occurrence and interlinked hydrometeorological extremes, including droughts and synergistic dry-hot events, regulated by large-scale atmospheric and terrestrial processes. Each region presents a unique climatological fingerprint, emphasizing the imperative for region-specific assessments over generalized approaches. For instance, robust lower-tropospheric moisture advection from the Atlantic Ocean into West and Central Africa, amplified by monsoonal winds, co-occurs with a Saharan monsoon trough and a weak Mediterranean ridge, mitigating heatwave frequency in these regions. Moreover, vertically integrated moisture flux convergence emerges as the dominant driver modulating heatwave frequency in the South American Monsoon zone, accounting for 41% of its variability. Relative humidity at 700 hPa and zonal winds at 1000 hPa also play significant roles, contributing 32% and 27%, respectively. Future projections depict a quadrupling of both heatwave frequency and compound events, exacerbating thermal stress and amplifying regional vulnerability. Population exposure to heatwaves is projected to escalate, exceeding fourfold and tenfold in the near- and far-future, respectively. Notably, South America and Europe exhibit substantial climate-driven exposure across all scenarios, underlining the urgency of regionally tailored adaptation and mitigation strategies in a warming planet.

Impact of climate change on hydrological regime in the Himalayan region is become a significant issue in Indian context. In the Himalayas, the hydrological processes keep on continuously disrupted due to decreasing rainfall, increasing temperature, reduced snow cover depth, population pressure, hydropower generation, dam construction, transportation development, road construction, intensive agricultural activities, and many others. Climate change has accelerated the melting of Himalayan glaciers, with profound impacts on the planetary health realms of the Himalayan region and that now threaten hundreds of millions of people. Rapid temperature rise in the Himalayas amplifies glacial retreat. Rising temperatures linked to increasing landslides in the Himalayas. Changing precipitation patterns in the Himalayas have a lot of implications for water resources and ecosystems. Accelerated biodiversity loss in the Himalayas due to climate change and habitat fragmentation. Rapid shifting of habitats in the Himalayas have implications for alpine flora and fauna, mounting threats to endangered species in the Himalayas. Altered species distributions provided evidence about the climate-induced disruptions. Himalayan ecosystems act as crucial carbon sinks, storing vast amounts of carbon. A call for urgent conservation efforts is necessary to preserve the pristine Himalayan environment.

Compound dry and hot events can cause aggregated damage compared with isolated hazards, especially those with a long duration. Based on observations during 1961-2014, the spatiotemporal characteristics of compound long-duration dry and hot (LDDH) events in China during the summer season are investigated on both a grid basis and a 3D event basis. Grid-scale LDDH events mainly occur in eastern China, especially over northeastern areas. From a 3D perspective, 146 spatiotemporal LDDH (SLDDH) events are detected and grouped into nine spatial patterns. Over time, there is a significant increase in the frequency and spatial extent of SLDDH. Consistent with the grid-scale LDDH events, hotspots of SLDDH events mainly occur in northern China, such as Northeast, North China and Qinghai clusters, which are accompanied by high occurrence frequency and large affected areas greater than 300,000 km². We then investigate the associated large-scale atmospheric circulation patterns and the physical processes causing their precipitation and temperature anomalies. Moisture budget diagnosis shows that precipitation deficits during the SLDDH events are produced primarily by the suppressed vertical moisture advection associated with the dynamical contribution of anomalous subsidence. In most regions, adiabatic warming due to abnormal subsidence plays a dominant role in determining the near-surface high temperatures during the long-lasting warm and dry periods. The future changes in LDDH events are analyzed using fourteen GCMs from CMIP5 downscaled by four statistical downscaling methods (BCSD, BCCI, BCCAQ, and CDF-t). The downscaling methods can efficiently improve the accuracy over the driving GCMs in terms of spatial variability, bias, and inter-annual variability of LDDH characteristics. In the mid-21st century (2041-2070), the number of SLDDH events under RCP4.5 and RCP8.5 is 2.5 and 3 times that of the present-day level, respectively. A substantial increase in spatially contiguous regions simultaneously experiencing LDDH events is seen by mid-century under both scenarios.

Heat stress poses a growing challenge to the world, particularly in Southeast Asia (SEA), where the projected increase of population, urbanization, and economic expansion expose the region to escalating heat extremes. The compounding effects of humid- and dry-heat extremes amplify the social and environmental impacts in SEA countries. Despite these challenges, a comprehensive assessment of future changes in and exposures to these climate extremes in SEA is lacking. This study utilizes recent high-resolution regional climate change projections dynamically downscaling from CMIP6 from the Centre for Climate Research Singapore. Our primary objective is to assess the projected changes in return periods for the occurrence of heatwaves and the co-occurrence of humid- and dry-heatwaves under three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results are analyzed using an ensemble mean of regional climate projections weighted by a skill score for simulating extremes. Comparative analyses with global climate projections are undertaken to contextualize the findings. Our findings indicate a consistent increase in the frequency of heat and compound heat extremes across all scenarios in SEA, with a sustained longer duration. Furthermore, we evaluate the exposure of population in SEA countries and megacities to these heightened heat extremes. The implications on climate change adaptation policies in SEA are discussed, emphasizing the urgent need for proactive measures to mitigate the adverse heat impacts.

Tropical night events in Seoul metropolitan area were classified into two types: pure-TN (no heatwave before tropical night) and HWTN (tropical night following heatwave). The Sea Surface Temperature (SST) in the Yellow Sea (YS), which has significantly increased over the past 30-year, demonstrated a high correlation with the frequency of tropical nights in Seoul. To investigate the impact of YS SST warming on tropical night in Seoul, the numerical experiment was conducted using the Weather Research and Forecasting (WRF) model. Two experiments were performed for 2013 and 2018, when pure-TN and HWTN were most prevalent; a control experiment (CTL) and a Cooling SST experiment (CSST) which reduced the SST anomaly (2013:1.3K, 2018:2.7K) for the YS area (34.5-38 N, 121-127 E). The impact of YS SST warming was examined by comparing the two experiments (CTL-CSST). The difference in dawntime temperature in Seoul between CTL and CSST was more pronounced in 2013 (0.48K) than in 2018 (0.37K) despite YS SST being lower in 2013 than in 2018. As of 2013 case, the longwave radiation and temperature advection, key synoptic factors for pure-TN, had opposite effects on dawntime temperature in Seoul. In CTL, less low-level cloud cover was simulated, leading to a decrease in net longwave radiation compared to CSST, which induced a cooling effect on temperature in Seoul. Meanwhile, the cold advection in CTL from the Yellow Sea to Seoul was weaker than in CSST, which could be interpreted as temperature advection played a role in preventing the cooling in Seoul in YS SST warming. This result indicated that YS SST warming influenced the increase in dawntime temperature in Seoul through decreasing cold temperature advection, particularly under pure-TN-like synoptic conditions. Furthermore, it suggests that the YS SST warming could be a key factor in the increased pure-TN in Seoul.

This study discovers a global teleconnection pattern of cold nights (TN10p) in boreal winter by teleconnection method, which is termed as the Circum–Hemisphere Teleconnection of extreme cold events (CHTe), and investigates its spatial-temporal characteristics and possible causes. The CHTe exhibits five identified centers of action, namely the Southeastern North America, Baffin Bay Coast, Northern Europe, Middle East–North Africa, and Eastern Siberia. The CHTe index depicts that the CHTe displays significant interannual (~3a) and decadal (~10a) variabilities. Besides, the CHTe differs from the known atmospheric teleconnection patterns of the Northern Hemisphere in boreal winter in terms of temporal variability, occurrence year, spatial structure, the TN10p effects and removing the signals. During the CHTe events in boreal winter, the five regions of the CHTe are characterized by significant anomalous geopotential height at the mid-upper levels. This atmospheric circulation may influence the local surface air temperatures by modulating expansion or compression of atmospheric column, further impacting the local TN10p over these regions.

Quantifying carbon emissions in high resolution is essential for understanding diverse emission characteristics across regions and, based on this understanding, supporting the establishment of carbon-neutral strategies for policymakers. In this study, we introduce ISAAC, the Inventory System Accounting for Anthropogenic Carbon, which estimates bottom-up carbon emissions from the Energy, Industrial Process and Product Use, and Waste sectors at a 1km² per hour resolution for the years 2020-2022, starting with Seoul and with plans for nationwide coverage. In that, we categorized carbon emission sources into point, line, or area types and developed sector-specific algorithms to disaggregate activity data into these elementary resolutions on an hourly basis. Carbon emissions were calculated using the methodology of the highest applicable tier and then converted into a gridded format to integrate emissions for the entire sector. For uncertainty assessment, uncertainties associated with spatial and temporal allocations were taken into account. The estimated carbon emissions were observed to be greater than those reported by the Seoul Metropolitan Government but smaller than the ones in the Open-Data Inventory for Anthropogenic Carbon Dioxide (ODIAC) dataset. The findings of this study contribute to both political and scientific significance, serving as a basis for developing effective policies to mitigate carbon emissions and providing foundational data for research on understanding urban-scale climate change.

Global fossil fuel CO₂ (FFCO₂) emissions accounted for more than 77% of the carbon emissions and cities alone account for more than 70% of the global FFCO₂ emissions. Mitigation actions at the city level remain a challenge as the urban comprehensive greenhouse gas monitoring system is still under construction, and single monitoring data cannot achieve high-precision estimation and verification of urban carbon emissions. This study, using four high-resolution global-scale FFCO₂ emission inventories (ODIAC 2022, EDGAR 8.0, PKU-CO₂-v2, GRACED), established the connection between the global and regional scales by quantitatively analyzing the significant differences and variabilities, which are integrated into the constructed Kalman filter fusion algorithm to form FFCO₂ emissions that present the best scale of the region. We selected the Greater Bay Area (GBA) as the study area. Sentinel-2 10m Annual Land Use Land Cover data assisted in generating the Urban-Rural Divide of the GBA. Nighttime light (NTL), Gross Domestic Product (GDP), and population density (POP) data were utilized to analyze emission factors from fossil fuel use. Before data reconstruction, FFCO₂ emission data shows 140% at the average difference of grid cells in the region, greater than 130% at half of the grids, and greater than 80% at more than three-quarters of the grids. And the coefficient of variation (CV) reaches 16.3%. The reconstructed 3 km data reduce the uncertainty from ±15%-20% to ±10%. The analysis of the FFCO₂ emission pattern, urban emission transfer paths, and the factors affecting urban and rural emissions in the GBA will provide a scientific basis for the optimal layout of energy and resources in the GBA and is of great significance to low-carbon transformation and high-quality development and the construction of a “Beautiful Bay Area”.

It is crucial to prioritize climate actions with accurate carbon emission estimates to achieve net-zero carbon emissions by 2050. However, the uncertainty of carbon emission estimates at urban scales is substantial due to limited access to activity data and outdated emission factors. The Bayesian inverse method, coupled with atmospheric CO₂ measurements, can be employed as an independent verification method to enhance the accuracy of emission estimates. In this study, we verify Seoul’s CO₂ emissions using the Bayesian inverse model and ground- and space-based measurements. We develop a Bayesian inverse modeling framework with input data, including anthropogenic CO₂ emissions, biogenic CO₂ fluxes, atmospheric CO₂ measurement, a Lagrangian transport model, and error covariances of both prior emissions and observations. We validate existing CO₂ emissions by estimating their optimal values and quantifying uncertainties through sensitivity tests. Finally, we assess the utility of this inverse modeling framework for science-based carbon neutrality policies by examining the contributions of anthropogenic and biogenic activities to CO₂ concentration, as well as the spatiotemporal changes in CO₂ emissions due to COVID-19. The abundance of ground and space observations over Seoul can sufficiently constrain urban CO₂ emissions and support mitigation policies.

The diversity of land types in urban areas introduces challenges in accurately identifying emission sources and sink areas in CO2 fluxes. This study addresses two fundamental questions: How do land-use type characteristics differ within footprint areas? And how can we better characterize the sources and sinks of urban CO2 fluxes within these footprint areas? Our overarching objective is to examine the spatiotemporal characteristics of urban CO2 fluxes, account for the varied land cover types within flux footprint areas, and propose an approach. This approach aims to provide insights into the challenges associated with emission source and sink identification in urban settings. For this study, we utilized approximately four years (2017–2020) of CO2 flux data from the National Institute of Meteorological Sciences, which were observed at five sites (Gajwa, Yongin, Nowon, Sungnam, and Tukseom). To directly characterize the influence of land-use types on flux, we rely on the footprint model proposed by Kljun et al. (2015). Each footprint was calculated for each CO2 flux recorded every 30 minutes. The footprint was polygonized and overlaid with the land-use type map, and the results were expressed as ratios within the footprint range. As a result of the study, YIN's forest ratio per unit footprint and CO2 flux in August were not correlated (r = 0.01), but vegetation effects were reflected (r = -0.43) when the effect of the built-up area was minimized. This suggests that detailed observation of land cover to transport air is necessary based on the footprint analysis. Our overarching objective is to contribute to policy decision-making for climate change mitigation in urban environments. This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Project for developing an observation-based GHG emissions geospatial information map, funded by Korea Ministry of Environment(MOE) (RS-2023-00232066).

Actual emission amounts of greenhouse gases such as carbon dioxide (i.e., CO2) are fundamental to cope with the net-zero emission agenda worldwide. The greenhouse gas emission has been estimated using bottom-up approaches statistically, which is relatively uncertain and time-consuming. Recently, many observation-based methodologies have been proposed (i.e., top-down approaches) to estimate the greenhouse gas emission accurately in near-real-time. However, the observation-based ones have too low spatial resolutions to identify the exact location of emission sources or too small spatial coverage to guarantee the representative emission amounts at a city scale. This study aims to develop an observation-based methodologies for estimating city-scale CO2 fluxes based on two vertically located monitoring sites, which is known as a flux-gradient method. Chuncheon, a mid-size city situated in a basin, is an optimal place for assuming negligible horizontal fluxes of greenhouse gases below an altitude of 300 m, compared to its vertical fluxes geographically. We installed two greenhouse gas monitoring towers at the center of city and at the top of hill located at the boarder of city. By applying a proper atmospheric diffusivity between two altitudes, the net CO2 flux has been continuously monitored at a city scale. We will present the CO2 footprint at the background monitoring site for evaluating the suitability of city-scale CO2 flux estimation and daily/weekly/monthly variations of CO2 flux from the Chuncheon city. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Project for developing an observation-based GHG emissions geospatial information map" funded by Korea Ministry of Environment (MOE) (RS-2023-00232066) and "Particulate Matter Management Specialized Graduate Program" through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)".

To achieve carbon neutrality, it's crucial to proactively monitor and mitigate greenhouse gas (GHG) emissions in the industrial sector. The CANIFFER (CArbon sNIFFER) project conducts an intensive GHG monitoring campaign from January 6-14, 2024 (9 days) at the Daesan Petrochemical Industrial Complex, a major industrial hub in South Korea. This campaign aims to assess the current status of GHG emissions by monitoring carbon dioxide (CO2) and methane (CH4) concentrations in the atmosphere of the Daesan Petrochemical Industrial Complex using ground-based spectrometers (EM27/SUN), mobile, in-situ, aircraft, and satellite measurements, as well as GHG modeling methods. This campaign is the first to conduct GHG monitoring research on a large-scale petrochemical industrial complex in South Korea at a time when discussions on quantification and verification of GHG emissions through observations are actively underway. The results of the CANIFFER project will be presented at the AOGS 2024 21^st Annual Meeting. This work was supported by Korea Environmental Industry & Technology Institute (KEITI) through "Project for developing an observation-based GHG emissions geospatial information map", funded by Korea Ministry of Environment (MOE)(RS-2023-00232066).

Future rainfall changes in India are of paramount importance for crop production and water management, but to date longer-term changes have not been evaluated beyond the year 2100. Here, we leverage a 10-member extension of the CESM2-LE under relatively strong emissions to identify projected rainfall changes and their underlying drivers out to 2500. Our main finding is that after 2100 there are substantial changes in the large-scale atmospheric circulation patterns that are distinct from mechanisms identified for 21^st century changes. In particular, we test the hypothesis that under substantial thermal perturbations to the climate system, after 2100 changes in atmospheric stability caused by tropospheric land-sea thermal contrast over India induce a northward shift of large-scale monsoon circulation, which, in turn, results in the northward shift of moisture transport and ultimately leading to increased summer monsoon rainfall over India. These changes reflect local expression of large-scale climate dynamical changes and provide more broader understanding of the underlying mechanism of long-term rainfall changes over India. 

Previous studies have pointed out that the tropical easterly jet (TEJ) core varies longitudinally or latitudinally. Whether there is a linkage between longitudinal and latitudinal variations of the TEJ core remains unclear. We found that, on the interannual time scale, the northward (southward) movement of the TEJ core is typically accompanied by a westward (eastward) shift, characterized by a noticeable northwest–southeast (NW–SE) displacement. This NW–SE shift is most evident in July. A locational index is defined to capture this shift by the difference of area-averaged 200-hPa zonal winds between the western Arabian Sea (AS) and the southern tip of the Indian Peninsula. Observations and numerical simulations demonstrated that the northwestward- shift (southeastward-shift) TEJ core is caused by the joint and individual influences from the enhanced (suppressed) convective activities over the eastern AS and suppressed (enhanced) convective activities over the northern Bay of Bengal–South China Sea (BOB–SCS). Enhanced (suppressed) convective activities over the eastern AS can induce upper-tropospheric divergence (convergence) and anticyclonic (cyclonic) circulations to the northwest of the convection, leading to anomalous easterly (westerly) over the western AS. The suppressed (enhanced) convective activities over the northern BOB–SCS can further facilitate the northwestward (southeastward) shift through inducing anomalous cyclonic (anticyclonic) circulation centering at the BOB and the associated anomalous westerly (easterly) over the southern tip of the Indian Peninsula. The NW–SE shift of the TEJ core may have an implication for the change in the area of the intense rainfall in South Asia.

Prediction of the South Asian summer monsoon (SASM) has remained a challenge for both scientific research and operational climate prediction for decades. By identifying two dominant modes of the SASM, here we show that the unsatisfactory prediction may be due to the fact that the existing SASM indices are mostly related to the less predictable second mode. The first mode, in fact, is highly predictable. It is physically linked to the variation of the Indian monsoon trough coupled with large rainfall anomalies over core monsoon zone and the northern Bay of Bengal. An index is constructed as a physical proxy of this first mode, which can be well predicted one season in advance, with an overall skill of 0.698 for 1979–2020. This result suggests a predictable prospect of the SASM, and we recommend the new index for real-time monitoring and prediction of the SASM.

The increase in the atmospheric water-holding capacity with temperature, as explained by the Clausius-Clapeyron (CC) relationship, elucidates shifts in extreme rainfall intensities under warmer atmospheric conditions. Our investigation focuses on the attributes of extreme rainfall events (EREs) during the Indian summer monsoon season concerning thermodynamic variations and precipitation scaling across the Indian subcontinent and its homogeneous rainfall zones. We use data from both present-day climate simulations and future climate change projection experiments of a high-resolution global climate model. Noteworthy transformations are observed, particularly for very extreme rainfall events (vEREs), indicating their susceptibility to high temperatures. In the future, modified radiative forcing is anticipated to warm the upper atmosphere, introducing stability and counteracting the impact of increased humidity on precipitation intensity. Our analysis further suggests that the interaction of increased moisture content, circulation patterns, and the prevalence of convective clouds will contribute to future changes in EREs.

South Asian Monsoon (SAM) circulation projections under global warming are known to be highly uncertain among CMIP models, which largely contributes to the uncertainty in SAM rainfall responses. Here, we found that the long-standing uncertainty in SAM circulation changes in CMIP6 models’ future projections arises from two robust and compensating mechanism: a weakening of circulation due to thermodynamic control, and a northward shift of meridional overturning circulation. The thermodynamic weakening cancels out some of the projected increase in SAM rainfall expected from the wet-get-wetter mechanism, and the northward circulation shift explains more than half of the multi-model mean SAM rainfall anomaly. From an energetic perspective, the robustness in the regional northward circulation shift (and thus the increasing SAM rainfall) arises from the interhemispheric asymmetry in atmospheric energy budget originated from extra-tropics: the positive cloud feedback over the extra-tropical Eurasia Continent and the anomalous heat uptake in the Southern Ocean. Our partially-coupled CESM1 simulations supports that the land-sea contrast is not only controlled by the differences in heat capacity between land and ocean, as suggested by previous literature, but is also modulated by extra-tropical processes. When suppressing Eurasia Continent cloud feedback and Southern Ocean heat uptake in 4xCO2 simulation, the anomalous land-sea temperature contrast is reduced from 2.1K in the standard 4xCO2 simulation to 1.6K and the rainfall is reduced in most regions over South Asia. The study highlight that the energetic perspective is a powerful tool to understand monsoon variations. It allows a quantitative attribution of the shift in regional precipitation as the energy budget can be linearly decomposed; moreover, it links the potential root causes of circulation changes and their uncertainty to the TOA and surface fluxes that are tied to model physics and parameterizations, which is valuable for climate model developments.

Over a past few decades, the global hydrological cycle has been significantly impacted by factors such as Greenhouse Gases (GHGs), Anthropogenic Aerosols (AA), Land Use and Land Cover changes (LULC), and climate variability. Notably, the trends in monsoon precipitation in the northern hemisphere are closely tied to the presence of GHGs and AA. This study aims to comprehensively analyze the regional precipitation trends. The investigation is carried out by exploring the relationship between downward solar radiation and evapotranspiration using simulations from the CMIP6 General Circulation Models. The study covers historical data as well as separate experiments only involving GHGs, AA, and Natural Forcings spanning from 1850 to 2014. Analysis of regional trends in downward solar radiation highlights significant reductions in India and Eastern China, particularly from 1960s onwards. The trend analysis of evapotranspiration and precipitation over south and east Asia from the 1950s to 2010s showed a drying trend in Eastern China, while India had an increase in annual total evapotranspiration and rainfall in the same period. These opposing responses in these two regions are due to, more so than the greenhouse gas effect AA emissions having considerable control over Eastern China’s precipitation. In contrast, the greenhouse effect has strong controls on the Indian land region’s hydrological cycle exceeding the forcing brought on by the AA emissions. The models are categorized using a hierarchical tree clustering technique to analyze the model's internal uncertainty, revealing that certain models exhibit energy-limited biases. These biases lead to heightened evapotranspiration responses to insolation changes. These tendencies might be responsible for inducing aridity in the studied areas, consequently leading to an increase in simulated climate extremes. 

We devise a pattern-aware feedback framework for the forced climate response using a suite of Green’s function-based solar radiation perturbation experiments to overcome the caveat of the existing climate feedback literature that disregards the co-variation between circulation and the radiative processes. By considering the energy balance at the top-of-atmosphere, a linear response function (LRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, lapse rate etc., is learned from the experiment data. The learned LRF decodes the efficiency of the energy diffusion in both the ocean and atmosphere, and the pattern-aware feedbacks from the radiatively active processes. The LRF can then be decomposed into forcing-response mode pairs which are in turn used to construct a reduced-order model (ROM) describing the dominant dynamics of climate responses. These mode pairs capture the nonlocal effects in the climate response and feedback. An intriguing outcome of our approach is that the most excitable mode of the LRF captures the polar amplified response of the climate system and this mode is explainable in the data-learned, pattern-aware feedback framework. The ROM can be used for predicting the response for a given forcing and predicting the forcing from a given responses, with important bearings on geoengineering.

One of the most striking and robust features of future climate change projections in the tropics is the massive increase in precipitation simulated over the equatorial Pacific. Reaching levels of more than 20% per degree of global warming, the precipitation enhancement will inevitably reduce ocean stratification, which may lead to changes in upper ocean dynamics and temperatures. In our study we document, using a series of freshwater perturbation experiments with the Community Earth System model (CESM), that future equatorial rainfall intensification and its effect on ocean salinity play a fundamental role in establishing the enhanced eastern Pacific warming pattern, and in reducing the Pacific Walker circulation. The rainfall forcing and the corresponding shift in stratification and thermocline slope also explain the subsurface cold wedge, that has been seen in greenhouse warming simulations since the late 1990s and that had remained unexplained. Our results demonstrate that anomalous freshwater forcing does not only play a crucial role in high-latitude North Atlantic ocean dynamics but also as a key mechanism that shapes future climate change in the tropics.