Pub Date : 2025-03-02DOI: 10.1016/j.atmosres.2025.108032
Jincai Xie , Jinggao Hu , Xuancheng Li , Jing-Jia Luo , Haiming Xu , Yanpei Jia
In the boreal winter of 2023/2024, the 10-hPa stratosphere experienced unprecedented planetary wave activity. This led to two major sudden stratospheric warming (SSW) events occurring on January 16 and March 4. The onset processes of both SSWs demonstrated clear stratosphere-troposphere coupling. On one hand, notable precursor signals were detected in the troposphere before each SSW. Approximately 15 days prior to the first SSW, a markedly intensified 500-hPa high pressure emerged over the high-latitude Atlantic, resulting in the strongest wave 3 in the troposphere since 1979. Additionally, an anomalous high over eastern Europe served as a precursor signal for the second SSW, contributing to a rapid increase in waves 1 and 2 approximately 20 days before its occurrence. On the other hand, these two SSWs exhibited distinct impacts on subsequent surface air temperature (SAT) over China. Specifically, the first SSW was characterized as a reflecting event. Following its onset, strong planetary wave reflections were observed in the troposphere over the Siberian and North Pacific regions, leading to local high-pressure ridge development. The two ridges, along with troughs between them, created an “inverted Ω-shaped” circulation pattern and caused a cold wave across China during January 18–22. In contrast, the second SSW was identified as an absorbing event; its impact aligned with typical effects of SSWs on SAT over Eurasia. After its onset, a negative phase of Northern Annular Mode emerged in the stratosphere and propagated downward into the troposphere over time, resulting in warming conditions over China during the following month.
{"title":"Two major sudden warming events in the unprecedentedly active stratosphere during the boreal winter of 2023/2024 and their distinct surface impacts over China","authors":"Jincai Xie , Jinggao Hu , Xuancheng Li , Jing-Jia Luo , Haiming Xu , Yanpei Jia","doi":"10.1016/j.atmosres.2025.108032","DOIUrl":"10.1016/j.atmosres.2025.108032","url":null,"abstract":"<div><div>In the boreal winter of 2023/2024, the 10-hPa stratosphere experienced unprecedented planetary wave activity. This led to two major sudden stratospheric warming (SSW) events occurring on January 16 and March 4. The onset processes of both SSWs demonstrated clear stratosphere-troposphere coupling. On one hand, notable precursor signals were detected in the troposphere before each SSW. Approximately 15 days prior to the first SSW, a markedly intensified 500-hPa high pressure emerged over the high-latitude Atlantic, resulting in the strongest wave 3 in the troposphere since 1979. Additionally, an anomalous high over eastern Europe served as a precursor signal for the second SSW, contributing to a rapid increase in waves 1 and 2 approximately 20 days before its occurrence. On the other hand, these two SSWs exhibited distinct impacts on subsequent surface air temperature (SAT) over China. Specifically, the first SSW was characterized as a reflecting event. Following its onset, strong planetary wave reflections were observed in the troposphere over the Siberian and North Pacific regions, leading to local high-pressure ridge development. The two ridges, along with troughs between them, created an “inverted Ω-shaped” circulation pattern and caused a cold wave across China during January 18–22. In contrast, the second SSW was identified as an absorbing event; its impact aligned with typical effects of SSWs on SAT over Eurasia. After its onset, a negative phase of Northern Annular Mode emerged in the stratosphere and propagated downward into the troposphere over time, resulting in warming conditions over China during the following month.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108032"},"PeriodicalIF":4.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.atmosres.2025.108026
Zhi-Li Chen , Yan Qiu , Wei Song , Xue-Yan Liu
Human-induced nitrogen (N) emissions have enhanced atmospheric N deposition in many polluted regions. However, the spatiotemporal relations between N emissions and deposition remain poorly characterized, which hampers N emission management and effect evaluation in polluted regions. This study investigated dry and wet inorganic N (IN) deposition at representative urban and suburban sites from June 2018 to May 2019 and analyzed historical N emission and deposition data in a N-polluted region of southern China. Dry, wet, and total IN deposition were 14.1 and 6.3, 36.4 and 29.0, and 50.5 and 35.3 kg N ha−1 yr−1 at the urban and suburban sites, respectively. Gaseous N accounted for 79–87 % of dry IN deposition and dry deposition accounted for 18–28 % of total IN deposition. The inter-annual variation of wet IN deposition exhibits an inverted “V” shape: the turning point for ammonium (NH4+) was in 1991 due to the decline in the ratio of ammonia to sulfur dioxide and nitrogen oxide and ammonia emissions; nitrate (NO3−) peaked in 2010, reflecting the benefits of national controls on nitrogen oxide emissions. Consequently, a shift to approximately equal NH4+ and NO3− deposition occurred in 2010–2020. Spatially, annual IN deposition increased with human-induced land use and N emissions, and about 30 % of the area exceeded the critical load for N deposition. Current emission controls are reducing N emissions and deposition but further mitigation measures are needed, especially broader regional strategies.
{"title":"Responses of atmospheric inorganic nitrogen deposition to emissions in a polluted region of southern China","authors":"Zhi-Li Chen , Yan Qiu , Wei Song , Xue-Yan Liu","doi":"10.1016/j.atmosres.2025.108026","DOIUrl":"10.1016/j.atmosres.2025.108026","url":null,"abstract":"<div><div>Human-induced nitrogen (N) emissions have enhanced atmospheric N deposition in many polluted regions. However, the spatiotemporal relations between N emissions and deposition remain poorly characterized, which hampers N emission management and effect evaluation in polluted regions. This study investigated dry and wet inorganic N (IN) deposition at representative urban and suburban sites from June 2018 to May 2019 and analyzed historical N emission and deposition data in a N-polluted region of southern China. Dry, wet, and total IN deposition were 14.1 and 6.3, 36.4 and 29.0, and 50.5 and 35.3 kg N ha<sup>−1</sup> yr<sup>−1</sup> at the urban and suburban sites, respectively. Gaseous N accounted for 79–87 % of dry IN deposition and dry deposition accounted for 18–28 % of total IN deposition. The inter-annual variation of wet IN deposition exhibits an inverted “V” shape: the turning point for ammonium (NH<sub>4</sub><sup>+</sup>) was in 1991 due to the decline in the ratio of ammonia to sulfur dioxide and nitrogen oxide and ammonia emissions; nitrate (NO<sub>3</sub><sup>−</sup>) peaked in 2010, reflecting the benefits of national controls on nitrogen oxide emissions. Consequently, a shift to approximately equal NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup> deposition occurred in 2010–2020. Spatially, annual IN deposition increased with human-induced land use and N emissions, and about 30 % of the area exceeded the critical load for N deposition. Current emission controls are reducing N emissions and deposition but further mitigation measures are needed, especially broader regional strategies.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108026"},"PeriodicalIF":4.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1016/j.atmosres.2025.108010
Aofan Gong, Bu Li, Ruiyang Zhou, Fuqiang Tian, Guangheng Ni
Accurate and fine-grained precipitation nowcasting holds paramount importance for weather-dependent decision-making and is facing escalating expectations and challenges. While researchers have notably advanced precipitation nowcasting using deep learning (DL) models with larger sizes and more complicated structures, there is scarce research exploring the potential improvement from employing radar data with higher spatial resolutions—a hundred-meter scale rather than a kilometer scale. To evaluate the improvement of higher-resolution data, two U-Net architecture-based models, one larger and another smaller, were designed and trained with radar data at different spatial resolutions—1000 m, 500 m, and 100 m. Their effectiveness was examined by comparison to two baseline models. The models trained with diverse resolutions of data underwent comparative evaluation through two specific precipitation cases. The results unveil a positive correlation between the precipitation nowcasting performance and the spatial resolution of radar data. Models trained with higher-resolution data demonstrate superior forecasting accuracy, reduced bias, and enhanced spatial alignment between predictions and observations. Higher-resolution data empowers DL models to capture boundaries and local-scale patterns of convective systems more accurately, thereby improving the performance in precipitation nowcasting. More importantly, the comparison indicates that to further promote the performance of DL-based precipitation nowcasting, improving data resolution is more efficient than expanding model size. The use of high-resolution data diminishes computational and development costs by concurrently reducing the size of DL models, underscoring pragmatic benefits for related services. Given limited resources, employing higher-resolution data is recommended for priority consideration over larger-size models.
{"title":"Higher-resolution data improves deep learning-based precipitation nowcasting","authors":"Aofan Gong, Bu Li, Ruiyang Zhou, Fuqiang Tian, Guangheng Ni","doi":"10.1016/j.atmosres.2025.108010","DOIUrl":"10.1016/j.atmosres.2025.108010","url":null,"abstract":"<div><div>Accurate and fine-grained precipitation nowcasting holds paramount importance for weather-dependent decision-making and is facing escalating expectations and challenges. While researchers have notably advanced precipitation nowcasting using deep learning (DL) models with larger sizes and more complicated structures, there is scarce research exploring the potential improvement from employing radar data with higher spatial resolutions—a hundred-meter scale rather than a kilometer scale. To evaluate the improvement of higher-resolution data, two U-Net architecture-based models, one larger and another smaller, were designed and trained with radar data at different spatial resolutions—1000 m, 500 m, and 100 m. Their effectiveness was examined by comparison to two baseline models. The models trained with diverse resolutions of data underwent comparative evaluation through two specific precipitation cases. The results unveil a positive correlation between the precipitation nowcasting performance and the spatial resolution of radar data. Models trained with higher-resolution data demonstrate superior forecasting accuracy, reduced bias, and enhanced spatial alignment between predictions and observations. Higher-resolution data empowers DL models to capture boundaries and local-scale patterns of convective systems more accurately, thereby improving the performance in precipitation nowcasting. More importantly, the comparison indicates that to further promote the performance of DL-based precipitation nowcasting, improving data resolution is more efficient than expanding model size. The use of high-resolution data diminishes computational and development costs by concurrently reducing the size of DL models, underscoring pragmatic benefits for related services. Given limited resources, employing higher-resolution data is recommended for priority consideration over larger-size models.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108010"},"PeriodicalIF":4.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.atmosres.2025.108023
Shiyu Zhang , Jing Yang , Tao Zhu , Qing Bao
The El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), as key oceanic boundary conditions, play a pivotal role in modulating regional climate variability. However, their influence on subseasonal dynamical prediction has yet to be fully understood. Focusing on Central Southwest Asia (CSWA), a region urgently needing accurate subseasonal prediction and significantly influenced by ENSO and IOD, this study investigates whether and how these interannual climate anomalies affect regional subseasonal rainfall prediction skills during early boreal winter using state-of-the-art Subseasonal-to-Seasonal (S2S) prediction models. First, the study finds that both deterministic and probabilistic prediction skills for domain-averaged rainfall anomalies and dry/wet events at a 2–4-week lead are significantly enhanced under La Niña and active IOD conditions compared to neutral states, while El Niño conditions show limited enhancement. This asymmetry in the ENSO impact is attributed to the inherent uncertainty in El Niño's influence on CSWA rainfall. Second, the analysis reveals that currently operational models exhibit higher skill in predicting ENSO at a 1-month lead, whereas predictions for IOD are comparatively less accurate. Nonetheless, prediction errors for both strong ENSO and IOD events at a 1-month lead are found to be significantly correlated with rainfall anomaly prediction errors over CSWA during the early boreal winter. This study confirms the significant effect of oceanic boundary conditions on regional subseasonal dynamical predictions and emphasizes the need to improve subseasonal prediction skills related to sea surface temperature variability associated with ENSO and IOD in order to reduce rainfall forecast errors and enhance the reliability of S2S predictions.
{"title":"Interannual climate anomalies modulate the subseasonal dynamical prediction skill from the regional perspective over Central Southwest Asia","authors":"Shiyu Zhang , Jing Yang , Tao Zhu , Qing Bao","doi":"10.1016/j.atmosres.2025.108023","DOIUrl":"10.1016/j.atmosres.2025.108023","url":null,"abstract":"<div><div>The El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), as key oceanic boundary conditions, play a pivotal role in modulating regional climate variability. However, their influence on subseasonal dynamical prediction has yet to be fully understood. Focusing on Central Southwest Asia (CSWA), a region urgently needing accurate subseasonal prediction and significantly influenced by ENSO and IOD, this study investigates whether and how these interannual climate anomalies affect regional subseasonal rainfall prediction skills during early boreal winter using state-of-the-art Subseasonal-to-Seasonal (S2S) prediction models. First, the study finds that both deterministic and probabilistic prediction skills for domain-averaged rainfall anomalies and dry/wet events at a 2–4-week lead are significantly enhanced under La Niña and active IOD conditions compared to neutral states, while El Niño conditions show limited enhancement. This asymmetry in the ENSO impact is attributed to the inherent uncertainty in El Niño's influence on CSWA rainfall. Second, the analysis reveals that currently operational models exhibit higher skill in predicting ENSO at a 1-month lead, whereas predictions for IOD are comparatively less accurate. Nonetheless, prediction errors for both strong ENSO and IOD events at a 1-month lead are found to be significantly correlated with rainfall anomaly prediction errors over CSWA during the early boreal winter. This study confirms the significant effect of oceanic boundary conditions on regional subseasonal dynamical predictions and emphasizes the need to improve subseasonal prediction skills related to sea surface temperature variability associated with ENSO and IOD in order to reduce rainfall forecast errors and enhance the reliability of S2S predictions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108023"},"PeriodicalIF":4.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.atmosres.2025.108024
Shaolei Tang , Jing-Jia Luo
The abrupt alternation between floods and droughts can have catastrophic impacts on local communities. In 2019, Southeast China experiences an extreme flood-drought abrupt alternation (FDAA), characterized by record-breaking flood in July and extreme drought in August. This study investigates the atmospheric circulations driving this FDAA and identifies the key mechanisms involved. Both the flood in July and the drought in August are closely associated with cyclonic circulation anomalies, but with distinct locations and dynamics. In July, a cyclonic anomaly over the East China Sea causes moisture convergence and ascending motions, leading to heavy rainfall. In contrast, two cyclonic anomalies in August—one over Northeast China and the other over the Western North Pacific (WNP)—induce northerly winds, moisture divergence, and descending motions, resulting in rainfall deficits. The July cyclonic anomaly is linked to positive soil moisture anomalies over Eastern Europe, which, through cooling surface air temperature, triggers eastward-propagating wave trains that induce cyclonic anomalies over the East China Sea. The August anomalies, on the other hand, are connected to North Atlantic and tropical Indian/Pacific SST anomalies. North Atlantic SST anomalies induce the anomalous Northeast China cyclone through modifying overlying atmosphere and triggering eastward-propagating wave trains. Significant positive IOD and warm SST anomalies over the central Pacific collaboratively trigger cyclonic circulation anomalies over the WNP through modifying the Walker circulation and the Hadley circulation. Our findings highlight the importance of understanding intraseasonal changes in tropical and midlatitude forcings to improve predictions and mitigate the impacts of compound climate disasters.
{"title":"Towards understanding the extreme flood–drought abrupt alternation over Southeast China in late summer 2019","authors":"Shaolei Tang , Jing-Jia Luo","doi":"10.1016/j.atmosres.2025.108024","DOIUrl":"10.1016/j.atmosres.2025.108024","url":null,"abstract":"<div><div>The abrupt alternation between floods and droughts can have catastrophic impacts on local communities. In 2019, Southeast China experiences an extreme flood-drought abrupt alternation (FDAA), characterized by record-breaking flood in July and extreme drought in August. This study investigates the atmospheric circulations driving this FDAA and identifies the key mechanisms involved. Both the flood in July and the drought in August are closely associated with cyclonic circulation anomalies, but with distinct locations and dynamics. In July, a cyclonic anomaly over the East China Sea causes moisture convergence and ascending motions, leading to heavy rainfall. In contrast, two cyclonic anomalies in August—one over Northeast China and the other over the Western North Pacific (WNP)—induce northerly winds, moisture divergence, and descending motions, resulting in rainfall deficits. The July cyclonic anomaly is linked to positive soil moisture anomalies over Eastern Europe, which, through cooling surface air temperature, triggers eastward-propagating wave trains that induce cyclonic anomalies over the East China Sea. The August anomalies, on the other hand, are connected to North Atlantic and tropical Indian/Pacific SST anomalies. North Atlantic SST anomalies induce the anomalous Northeast China cyclone through modifying overlying atmosphere and triggering eastward-propagating wave trains. Significant positive IOD and warm SST anomalies over the central Pacific collaboratively trigger cyclonic circulation anomalies over the WNP through modifying the Walker circulation and the Hadley circulation. Our findings highlight the importance of understanding intraseasonal changes in tropical and midlatitude forcings to improve predictions and mitigate the impacts of compound climate disasters.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108024"},"PeriodicalIF":4.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.atmosres.2025.107973
Jing Liu , Lijuan Chen , Ying Liu , Daquan Zhang , Hai Zhi
The increasing impacts on regional climate of sea ice anomalies have spatio-temporal uncertainties, this study investigates the possible relationships between Arctic sea ice anomalies and intra-seasonal evolution of winter temperature in Northwest China (with a particular emphasis on Xinjiang). The reanalysis dataset during 1990–2022 from the National Centers for Environmental Prediction and the National Center for Atmospheric Research are utilized, with multiple statistical methods including season-reliant empirical orthogonal function (EOF) and regression analysis being employed. The results show that the first and second EOF modes of intra-seasonal temperature evolution in winter over Xinjiang are characterized by in-phase and out-of-phase variations, explaining 32 % and 19 % variance, respectively. The in-phase (out-of-phase) evolution of intra-seasonal temperature is affected by the in-phase (out-of-phase) variations of sea ice in the Kara Sea-East Siberia Sea-Chukchi Sea (KEsC) and the Barents Sea (BS) during the preceding October. The reduction in the KEsC sea ice triggers upward-propagating wave energy anomalies, which is favorable for the wave train shaping as a meridional dipole pattern from the Ural Mountains to Central Asia in early winter through the stratosphere-troposphere coupling, thereby leading to temperature anomalies in Xinjiang during the following December and January. The BS sea ice anomalies favor the formation and maintenance of a North Atlantic Oscillation-like pattern, the wave energy disperses downstream and enhances in February, triggering a Scandinavian teleconnection pattern-like wave train. This leads to circulation anomalies over Xinjiang, further causes temperature anomalies in February. In summary, the KEsC and BS sea ice in October serves as key precursor signals impacting the intra-seasonal evolution of winter temperature in Xinjiang, with the significant impacts of sea ice in these two key regions on intra-seasonal temperature variations occurring at different times.
{"title":"Possible relationships between the intra-seasonal variation of winter temperature in Northwest China and Arctic sea ice","authors":"Jing Liu , Lijuan Chen , Ying Liu , Daquan Zhang , Hai Zhi","doi":"10.1016/j.atmosres.2025.107973","DOIUrl":"10.1016/j.atmosres.2025.107973","url":null,"abstract":"<div><div>The increasing impacts on regional climate of sea ice anomalies have spatio-temporal uncertainties, this study investigates the possible relationships between Arctic sea ice anomalies and intra-seasonal evolution of winter temperature in Northwest China (with a particular emphasis on Xinjiang). The reanalysis dataset during 1990–2022 from the National Centers for Environmental Prediction and the National Center for Atmospheric Research are utilized, with multiple statistical methods including season-reliant empirical orthogonal function (EOF) and regression analysis being employed. The results show that the first and second EOF modes of intra-seasonal temperature evolution in winter over Xinjiang are characterized by in-phase and out-of-phase variations, explaining 32 % and 19 % variance, respectively. The in-phase (out-of-phase) evolution of intra-seasonal temperature is affected by the in-phase (out-of-phase) variations of sea ice in the Kara Sea-East Siberia Sea-Chukchi Sea (KEsC) and the Barents Sea (BS) during the preceding October. The reduction in the KEsC sea ice triggers upward-propagating wave energy anomalies, which is favorable for the wave train shaping as a meridional dipole pattern from the Ural Mountains to Central Asia in early winter through the stratosphere-troposphere coupling, thereby leading to temperature anomalies in Xinjiang during the following December and January. The BS sea ice anomalies favor the formation and maintenance of a North Atlantic Oscillation-like pattern, the wave energy disperses downstream and enhances in February, triggering a Scandinavian teleconnection pattern-like wave train. This leads to circulation anomalies over Xinjiang, further causes temperature anomalies in February. In summary, the KEsC and BS sea ice in October serves as key precursor signals impacting the intra-seasonal evolution of winter temperature in Xinjiang, with the significant impacts of sea ice in these two key regions on intra-seasonal temperature variations occurring at different times.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 107973"},"PeriodicalIF":4.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1016/j.atmosres.2025.108016
Yang Zhao, Lixia Bi, Xiangzhen Kong
In this study, the thunderstorms (TS) cloud top height (CTH) data of the advanced geosynchronous radiation imager of the Fengyun-4 A (FY-4 A, a geostationary satellite) were quality-controlled using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, and the lightning data of the LMI of the FY-4 A were processed by the cluster analysis method. The convective systems in China's land and adjacent sea areas were identified from the CTH data, and the CTH data of TS was corrected. The CTH and lightning activity characteristics of TS and overshooting convections (OC) and their relationship were analyzed. TS and OC occurred more often over land areas than over sea areas. The two had similar spatial distribution characteristics: they were mainly concentrated at low latitudes, and the CTH of both showed a noticeable decreasing trend with increasing latitude. Most TS had CAPE1/2 (updraft speed) values between 15 ms−1 and 50 ms−1 and CTH between 11 km and 17.5 km. Most OC had CAPE1/2 values between 20 ms−1 and 50 ms−1 and CTH between 15 km and 19 km. For both TS and OC, small updrafts generated low flash extent density values and large minimum flash area values, while large updrafts produced high flash extent density and small minimum flash area.
{"title":"Identification and feature analysis of thunderstorm based on AGRI and LMI of Fengyun-4A satellite","authors":"Yang Zhao, Lixia Bi, Xiangzhen Kong","doi":"10.1016/j.atmosres.2025.108016","DOIUrl":"10.1016/j.atmosres.2025.108016","url":null,"abstract":"<div><div>In this study, the thunderstorms (TS) cloud top height (CTH) data of the advanced geosynchronous radiation imager of the Fengyun-4 A (FY-4 A, a geostationary satellite) were quality-controlled using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, and the lightning data of the LMI of the FY-4 A were processed by the cluster analysis method. The convective systems in China's land and adjacent sea areas were identified from the CTH data, and the CTH data of TS was corrected. The CTH and lightning activity characteristics of TS and overshooting convections (OC) and their relationship were analyzed. TS and OC occurred more often over land areas than over sea areas. The two had similar spatial distribution characteristics: they were mainly concentrated at low latitudes, and the CTH of both showed a noticeable decreasing trend with increasing latitude. Most TS had CAPE<sup>1/2</sup> (updraft speed) values between 15 ms<sup>−1</sup> and 50 ms<sup>−1</sup> and CTH between 11 km and 17.5 km. Most OC had CAPE<sup>1/2</sup> values between 20 ms<sup>−1</sup> and 50 ms<sup>−1</sup> and CTH between 15 km and 19 km. For both TS and OC, small updrafts generated low flash extent density values and large minimum flash area values, while large updrafts produced high flash extent density and small minimum flash area.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"318 ","pages":"Article 108016"},"PeriodicalIF":4.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1016/j.atmosres.2025.107988
Christelle Barthe, Pierre Tulet, Sybille de Sevin, Inès Vongpaseut, Sylvain Coquillat
This study analyzes the role of dust particles on the formation of an anomalous charge structure in a thunderstorm observed on 14 October 2016 around Corsica. The cloud-resolving model Meso-NH with explicit aerosol-microphysics coupling is used to simulate realistically the interactions between the storm and the dust plume. This reference simulation shows no liquid water content in the convective region below −10 °C, which is conducive to only positive charging of the graupel according to traditional liquid water content temperature diagrams of the non-inductive charging mechanism. Differential hydrometeor fall speeds lead to a positive charge region below a negative charge region at the cloud scale, the so-called anomalous charge structure.
To disentangle between the radiative and microphysical role of dust particles on cloud structure, additional simulations are performed. First, using a climatological extinction of dust particles over Corsica, the microphysical structure of the storm is similar to the one of the reference simulation, excluding the radiative role of dust particles as a factor controling the charge structure in this case. Secondly, a low, constant and homogeneous ice nuclei particle concentration profile is imposed in the microphysics scheme. It enables the presence of supercooled water at temperatures below −20 °C, allowing the negative charging of the graupel at higher levels of the cloud, and potentially leading to a normal tripole. It highlights the role of desert dust as ice nuclei in the depletion of liquid water content in the mixed phase region of the cloud, which could lead to an anomalous charge structure.
{"title":"Numerical investigation of the role of Saharan dust on the anomalous electrical structure of a thunderstorm over Corsica","authors":"Christelle Barthe, Pierre Tulet, Sybille de Sevin, Inès Vongpaseut, Sylvain Coquillat","doi":"10.1016/j.atmosres.2025.107988","DOIUrl":"10.1016/j.atmosres.2025.107988","url":null,"abstract":"<div><div>This study analyzes the role of dust particles on the formation of an anomalous charge structure in a thunderstorm observed on 14 October 2016 around Corsica. The cloud-resolving model Meso-NH with explicit aerosol-microphysics coupling is used to simulate realistically the interactions between the storm and the dust plume. This reference simulation shows no liquid water content in the convective region below −10 °C, which is conducive to only positive charging of the graupel according to traditional liquid water content temperature diagrams of the non-inductive charging mechanism. Differential hydrometeor fall speeds lead to a positive charge region below a negative charge region at the cloud scale, the so-called anomalous charge structure.</div><div>To disentangle between the radiative and microphysical role of dust particles on cloud structure, additional simulations are performed. First, using a climatological extinction of dust particles over Corsica, the microphysical structure of the storm is similar to the one of the reference simulation, excluding the radiative role of dust particles as a factor controling the charge structure in this case. Secondly, a low, constant and homogeneous ice nuclei particle concentration profile is imposed in the microphysics scheme. It enables the presence of supercooled water at temperatures below −20 °C, allowing the negative charging of the graupel at higher levels of the cloud, and potentially leading to a normal tripole. It highlights the role of desert dust as ice nuclei in the depletion of liquid water content in the mixed phase region of the cloud, which could lead to an anomalous charge structure.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 107988"},"PeriodicalIF":4.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-23DOI: 10.1016/j.atmosres.2025.108009
Seyed Mohsen Mousavi , Naghmeh Mobarghaee Dinan , Korous Khoshbakht , Saeed Ansarifard , Oliver Sonnentag , Amir Naghibi
Climate change and the resulting warming caused by human activities, such as releasing greenhouse gases (GHG), notably carbon dioxide (CO2), are paramount concerns in the contemporary era. Therefore, it is essential to understand spatial and temporal variations of atmospheric carbon dioxide concentration (XCO2) on national and international scales and the role of controlling variables. This research examines the role of controlling variables on XCO2 by utilizing spatiotemporal modeling, correlation analysis, and machine learning methods to determine the sinks and sources of CO2 and their changes in the Middle East (ME). First, XCO2 data from the OCO-2 satellite spanning from 2015 to 2021 were employed for spatiotemporal analysis. The temporal analysis of XCO2 showed peak levels during spring (May) and the lowest levels during summer (September). Additionally, the spatial dispersion of XCO2 revealed significant spatial heterogeneity influenced by land cover. Areas with high vegetation abundance and suitable weather conditions exhibited minimum XCO2 levels, while vice versa occurred. Next, the correlation analysis between the principal controlling variables and XCO2 revealed that, except for vegetation abundance and anthropogenic CO2 emissions, they exhibited predominantly negative and positive correlations throughout the year, respectively. However, the correlation patterns for temperature, precipitation, humidity, and wind speed varied across different months, showing both negative and positive relationships depending on the month. Recognizing that simple correlation alone cannot determine which variables played the most significant role in XCO2 each month, this study employed machine learning and Permutation Feature Importance (PFI) techniques. The results showed that, except for March and September, when precipitation and wind speed respectively had the most significant influence on determining XCO2, air temperature played the dominant role in other months. Additionally, the average monthly results revealed that air temperature, wind speed, precipitation, and vegetation abundance, respectively, played the most significant roles in XCO2. In contrast, CO2 emissions had the most negligible impact. These results highlight the considerable influence of meteorological and environmental variables on regulating XCO2 levels and distributions throughout the ME. By elucidating the seasonality of the carbon cycle and identifying key XCO2 drivers over the ME, this study provides valuable insights to guide regional climate change mitigation policies and further analysis of vulnerable semi-arid ecosystems.
{"title":"Determining the influence of meteorological, environmental, and anthropogenic activity variables on the atmospheric CO2 concentration in the arid and semi-arid regions: A case study in the Middle East","authors":"Seyed Mohsen Mousavi , Naghmeh Mobarghaee Dinan , Korous Khoshbakht , Saeed Ansarifard , Oliver Sonnentag , Amir Naghibi","doi":"10.1016/j.atmosres.2025.108009","DOIUrl":"10.1016/j.atmosres.2025.108009","url":null,"abstract":"<div><div>Climate change and the resulting warming caused by human activities, such as releasing greenhouse gases (GHG), notably carbon dioxide (CO<sub>2</sub>), are paramount concerns in the contemporary era. Therefore, it is essential to understand spatial and temporal variations of atmospheric carbon dioxide concentration (XCO<sub>2</sub>) on national and international scales and the role of controlling variables. This research examines the role of controlling variables on XCO<sub>2</sub> by utilizing spatiotemporal modeling, correlation analysis, and machine learning methods to determine the sinks and sources of CO<sub>2</sub> and their changes in the Middle East (ME). First, XCO<sub>2</sub> data from the OCO-2 satellite spanning from 2015 to 2021 were employed for spatiotemporal analysis. The temporal analysis of XCO<sub>2</sub> showed peak levels during spring (May) and the lowest levels during summer (September). Additionally, the spatial dispersion of XCO<sub>2</sub> revealed significant spatial heterogeneity influenced by land cover. Areas with high vegetation abundance and suitable weather conditions exhibited minimum XCO<sub>2</sub> levels, while vice versa occurred. Next, the correlation analysis between the principal controlling variables and XCO<sub>2</sub> revealed that, except for vegetation abundance and anthropogenic CO<sub>2</sub> emissions, they exhibited predominantly negative and positive correlations throughout the year, respectively. However, the correlation patterns for temperature, precipitation, humidity, and wind speed varied across different months, showing both negative and positive relationships depending on the month. Recognizing that simple correlation alone cannot determine which variables played the most significant role in XCO<sub>2</sub> each month, this study employed machine learning and Permutation Feature Importance (PFI) techniques. The results showed that, except for March and September, when precipitation and wind speed respectively had the most significant influence on determining XCO<sub>2</sub>, air temperature played the dominant role in other months. Additionally, the average monthly results revealed that air temperature, wind speed, precipitation, and vegetation abundance, respectively, played the most significant roles in XCO<sub>2</sub>. In contrast, CO<sub>2</sub> emissions had the most negligible impact. These results highlight the considerable influence of meteorological and environmental variables on regulating XCO<sub>2</sub> levels and distributions throughout the ME. By elucidating the seasonality of the carbon cycle and identifying key XCO<sub>2</sub> drivers over the ME, this study provides valuable insights to guide regional climate change mitigation policies and further analysis of vulnerable semi-arid ecosystems.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108009"},"PeriodicalIF":4.5,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-23DOI: 10.1016/j.atmosres.2025.107989
Yufei Wang , Peng Sun , Rui Yao , Chenhao Ge
The response of vegetation to drought is a crucial aspect of understanding ecosystem adaptation to climate change. Response time (RT) to drought serves as a key metric to quantify vegetation anomalies induced by drought events. However, it remains unclear from the perspective of event. Therefore, we quantified the RT of Leaf Area Index (LAI) and Gross Primary Productivity (GPP) to meteorological drought across various vegetation types and climatic zones in China. Additionally, we analyzed the spatial distribution of vegetation loss during response periods and the factors influencing RT. Our findings indicated that the primary RT of LAI and GPP to Standardized Precipitation Evapotranspiration Index (SPEI) was short-term (<4 months), with LAI's longer RT concentrated in southern China. In contrast, the spatial distribution of GPP's RT largely opposed that of LAI. Grasslands exhibited notably different RT spatial distributions compared to other vegetation types. Additionally, we found LAI experiencing greater losses than GPP during response periods. For RT of both LAI and GPP, near-surface temperature (TEM) emerged as the paramount influencing factor, with RT significantly increasing with rising TEM. Secondary factors included surface solar radiation (SSRD) and Vapor Pressure Deficit (VPD), but LAI and GPP showed distinct differences in their biased dependence on SSRD and VPD respectively. Most regions displayed a significant positive correlation between RT of LAI and GPP with TEM, and a negative correlation with SSRD. The study clarifies the spatiotemporal patterns of vegetation response to drought and provides a scientific basis for water resource allocation and future drought prevention and mitigation to reduce drought's negative impact on terrestrial ecosystems.
{"title":"Rising temperature increases the response time of LAI and GPP to meteorological drought in China","authors":"Yufei Wang , Peng Sun , Rui Yao , Chenhao Ge","doi":"10.1016/j.atmosres.2025.107989","DOIUrl":"10.1016/j.atmosres.2025.107989","url":null,"abstract":"<div><div>The response of vegetation to drought is a crucial aspect of understanding ecosystem adaptation to climate change. Response time (RT) to drought serves as a key metric to quantify vegetation anomalies induced by drought events. However, it remains unclear from the perspective of event. Therefore, we quantified the RT of Leaf Area Index (LAI) and Gross Primary Productivity (GPP) to meteorological drought across various vegetation types and climatic zones in China. Additionally, we analyzed the spatial distribution of vegetation loss during response periods and the factors influencing RT. Our findings indicated that the primary RT of LAI and GPP to Standardized Precipitation Evapotranspiration Index (SPEI) was short-term (<4 months), with LAI's longer RT concentrated in southern China. In contrast, the spatial distribution of GPP's RT largely opposed that of LAI. Grasslands exhibited notably different RT spatial distributions compared to other vegetation types. Additionally, we found LAI experiencing greater losses than GPP during response periods. For RT of both LAI and GPP, near-surface temperature (TEM) emerged as the paramount influencing factor, with RT significantly increasing with rising TEM. Secondary factors included surface solar radiation (SSRD) and Vapor Pressure Deficit (VPD), but LAI and GPP showed distinct differences in their biased dependence on SSRD and VPD respectively. Most regions displayed a significant positive correlation between RT of LAI and GPP with TEM, and a negative correlation with SSRD. The study clarifies the spatiotemporal patterns of vegetation response to drought and provides a scientific basis for water resource allocation and future drought prevention and mitigation to reduce drought's negative impact on terrestrial ecosystems.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 107989"},"PeriodicalIF":4.5,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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