Pub Date : 2026-01-01Epub Date: 2025-05-26DOI: 10.1016/j.aosl.2025.100653
Congqi Cao , Ze Sun , Lanshu Hu , Liujie Pan , Yanning Zhang
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems, offering faster computation and the ability to utilize large historical datasets. However, the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge. In this work, three key solutions are proposed: (1) motivated by the need to improve model performance in data-scarce regional forecasting scenarios, the authors innovatively apply semantic segmentation models, to better capture spatiotemporal features and improve prediction accuracy; (2) recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness, a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations, ensuring more effective learning; and (3) to address the issue of error accumulation in autoregressive prediction, as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction, the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance. The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition. Ablation experiments further validate the effectiveness of each component, highlighting their contributions to enhancing prediction performance.
{"title":"A novel deep learning-based framework for five‐day regional weather forecasting","authors":"Congqi Cao , Ze Sun , Lanshu Hu , Liujie Pan , Yanning Zhang","doi":"10.1016/j.aosl.2025.100653","DOIUrl":"10.1016/j.aosl.2025.100653","url":null,"abstract":"<div><div>Deep learning-based methods have become alternatives to traditional numerical weather prediction systems, offering faster computation and the ability to utilize large historical datasets. However, the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge. In this work, three key solutions are proposed: (1) motivated by the need to improve model performance in data-scarce regional forecasting scenarios, the authors innovatively apply semantic segmentation models, to better capture spatiotemporal features and improve prediction accuracy; (2) recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness, a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations, ensuring more effective learning; and (3) to address the issue of error accumulation in autoregressive prediction, as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction, the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance. The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition. Ablation experiments further validate the effectiveness of each component, highlighting their contributions to enhancing prediction performance.</div><div>摘要</div><div>深度学习逐渐替代传统数值天气预报 (NWP) 系统, 但在数据有限的中期天气预报中仍面临挑战。为此, 本文提出三项创新: 首先, 引入语义分割模型增强时空特征捕捉能力, 提高预测精度; 其次, 设计可学习的高斯噪声机制, 解决过拟合问题并突破传统噪声增强的局限性; 最后, 提出级联预测方法, 平衡预测精度与误差控制, 缓解自回归预测的误差累积问题。该方法在华东区域AI中期气象预报竞赛中表现优异, 实验验证了各模块的有效性, 其中语义分割降低温度预测误差9.3%, 噪声机制提升降水预测F1-score 6.8%, 级联策略减少风速预测均方误差12.5%。此研究为数据受限的区域气象预报提供了新路径。</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 1","pages":"Article 100653"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Northeast China (NEC), a critical agricultural and ecological zone, has experienced intensified hydrological variability under global warming, with cascading impacts on food security and ecosystem resilience. This study utilized observational data and two new generation reanalysis products (i.e., the fifth major global reanalysis produced by ECMWF (ERA5) and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)) to investigate the shift changes in precipitation in NEC around 2000 and associated water vapor transport. The analysis identified a pivotal interdecadal shift in 1998/99, transitioning from moderate increases (17.5 mm/10 yr during 1980–1998) to accelerated but more variable precipitation growth (85.4 mm/10 yr post-1999). While the mean precipitation during the post-shift period decreased, enhanced anticyclonic circulation amplified moisture divergence over continental NEC, redirecting vapor flux toward coastal regions. Crucially, trajectory analysis demonstrated regime-dependent moisture sourcing: midlatitude westerlies dominated during wet extremes (44% of trajectories in 1998), whereas East Asian monsoon flows prevailed in drought years (36 % of trajectories in 2007). The post-1998 period exhibited increased reliance on localized recycling (45 % of mid-tropospheric trajectories), reflecting weakened monsoonal inflow. These findings highlight NEC’s growing vulnerability to competing moisture pathways and atmospheric blocking—a dual mechanism that explains rising extremes despite declining mean precipitation. By reconciling dataset discrepancies (ERA5 vs. JRA-3Q trends) and elucidating circulation-precipitation linkages, this work provides actionable insights for climate-resilient agriculture in NEC’s water-stressed ecosystems.
{"title":"Decadal shift in Northeast China’s precipitation around 2000","authors":"Yawen Liao , Tianbao Zhao , Jingpeng Zhang , Yankun Sun","doi":"10.1016/j.aosl.2025.100650","DOIUrl":"10.1016/j.aosl.2025.100650","url":null,"abstract":"<div><div>Northeast China (NEC), a critical agricultural and ecological zone, has experienced intensified hydrological variability under global warming, with cascading impacts on food security and ecosystem resilience. This study utilized observational data and two new generation reanalysis products (i.e., the fifth major global reanalysis produced by ECMWF (ERA5) and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)) to investigate the shift changes in precipitation in NEC around 2000 and associated water vapor transport. The analysis identified a pivotal interdecadal shift in 1998/99, transitioning from moderate increases (17.5 mm/10 yr during 1980–1998) to accelerated but more variable precipitation growth (85.4 mm/10 yr post-1999). While the mean precipitation during the post-shift period decreased, enhanced anticyclonic circulation amplified moisture divergence over continental NEC, redirecting vapor flux toward coastal regions. Crucially, trajectory analysis demonstrated regime-dependent moisture sourcing: midlatitude westerlies dominated during wet extremes (44% of trajectories in 1998), whereas East Asian monsoon flows prevailed in drought years (36 % of trajectories in 2007). The post-1998 period exhibited increased reliance on localized recycling (45 % of mid-tropospheric trajectories), reflecting weakened monsoonal inflow. These findings highlight NEC’s growing vulnerability to competing moisture pathways and atmospheric blocking—a dual mechanism that explains rising extremes despite declining mean precipitation. By reconciling dataset discrepancies (ERA5 vs. JRA-3Q trends) and elucidating circulation-precipitation linkages, this work provides actionable insights for climate-resilient agriculture in NEC’s water-stressed ecosystems.</div><div>摘要</div><div>东北地区作为中国重要的农业生态区之一, 在区域变暖背景下降水呈现显著波动. 该论文基于CN05.1观测数据与ERA5, JRA-3Q再分析资料, 发现东北地区降水在1998/99年发生关键转折: 降水量增速由前期的17.5 mm/10年 (1980−1998年) 跃升至85.4 mm/10年 (1999−2022年). 反气旋环流增强导致大陆区水汽辐散, 向沿海输送增加.轨迹分析显示水汽来源存在显著年际差异: 1998年丰水期44 %水汽源自西风带, 2007年旱季36 %水汽来自东亚季风.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 1","pages":"Article 100650"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2026-01-01Epub Date: 2025-07-17DOI: 10.1016/j.aosl.2025.100691
Yifan Xie , Ke Fan , Hongqing Yang , Yi Fan , Shengping He
Current shipping, tourism, and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration (SIC). However, due to the complex physical processes involved, predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent. In this study, spatiotemporal prediction models for monthly Arctic SIC at 1- to 3-month leads are developed based on U-Net—an effective convolutional deep-learning approach. Based on explicit Arctic sea-ice–atmosphere interactions, 11 variables associated with Arctic sea-ice variations are selected as predictors, including observed Arctic SIC, atmospheric, oceanic, and heat flux variables at 1- to 3-month leads. The prediction skills for the monthly Arctic SIC of the test set (from January 2018 to December 2022) are evaluated by examining the mean absolute error (MAE) and binary accuracy (BA). Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems (CFSv2 and NorCPM). By analyzing the relative importance of each predictor, the prediction accuracy relies more on the SIC at the 1-month lead, but on the surface net solar radiation flux at 2- to 3-month leads. However, dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes, especially in autumn. Therefore, the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
{"title":"An effective deep-learning prediction of Arctic sea-ice concentration based on the U-Net model","authors":"Yifan Xie , Ke Fan , Hongqing Yang , Yi Fan , Shengping He","doi":"10.1016/j.aosl.2025.100691","DOIUrl":"10.1016/j.aosl.2025.100691","url":null,"abstract":"<div><div>Current shipping, tourism, and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration (SIC). However, due to the complex physical processes involved, predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent. In this study, spatiotemporal prediction models for monthly Arctic SIC at 1- to 3-month leads are developed based on U-Net—an effective convolutional deep-learning approach. Based on explicit Arctic sea-ice–atmosphere interactions, 11 variables associated with Arctic sea-ice variations are selected as predictors, including observed Arctic SIC, atmospheric, oceanic, and heat flux variables at 1- to 3-month leads. The prediction skills for the monthly Arctic SIC of the test set (from January 2018 to December 2022) are evaluated by examining the mean absolute error (MAE) and binary accuracy (BA). Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems (CFSv2 and NorCPM). By analyzing the relative importance of each predictor, the prediction accuracy relies more on the SIC at the 1-month lead, but on the surface net solar radiation flux at 2- to 3-month leads. However, dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes, especially in autumn. Therefore, the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.</div><div>摘要</div><div>准确地预测北极海冰密集度 (SIC) 对北极航运, 旅游和资源开发等十分重要. 由于北极海冰的复杂多变, 预测北极SIC的时空分布比预测海冰范围更具有挑战性. 基于一个有效的卷积类机器学习模型—U-Net, 本文研制了可用于预测未来1至3个月北极SIC的模型. 基于北极海–冰–气物理过程, 本文选取了前期11个与北极海冰变化密切相联的变量作为预测因子, 包括北极SIC, 大气, 海洋和热通量等变量. 较CFSv2和NorCPM而言, 本文研制的U-Net模型具有更高的预测技巧. 此外, 诊断各预测因子的相对重要性显示, 提前1个月的预测模型更依赖于前期的SIC, 但提前2和3个月的预测模型则更依赖于前期的地表净短波辐射通量. 然而, 动力模式对地表净短波辐射和其相关物理过程的预测技能有限, 这可能是U-Net模型预测技巧较动力模式更高的原因之一. 本研究既有利于提升对北极SIC空间分布的预测能力, 也有助于进一步认识动力模式对海冰预测效能有限的原因.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 1","pages":"Article 100691"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2026-01-01Epub Date: 2025-04-04DOI: 10.1016/j.aosl.2025.100618
Xingyu Li , Yuanhong Guan , Ran Dong , Qifeng Lu , Yue Zhang , Jiani Zhen
Based on reanalysis data from 1979 to 2021, this study explores the spatial distribution of the Southern Indian Ocean Dipole (SIOD) and its individual and synergistic effects with the El Niño–Southern Oscillation (ENSO) on summer precipitation in China. The inverse phase spatial distribution of sea surface temperature anomalies (SSTAs) in the southwest and northeast of the southern Indian Ocean is defined as the SIOD. Positive SIOD events (positive SSTAs in the southwest, negative SSTAs in the northeast) are associated with La Niña events (Central Pacific (CP) type), while negative SIOD events (negative SSTAs in the southwest, positive SSTAs in the northeast) are associated with El Niño events (Eastern Pacific (EP) type). Both SIOD and ENSO have certain impacts on summer precipitation in China. Precipitation in the Yangtze River basin decreases, while precipitation in southern China increases during pure positive SIOD (P_PSIOD) events. During pure negative SIOD (P_NSIOD) events, the changes in precipitation are exactly the opposite of those during P_PSIOD events, which may be due to differences in the cross-equatorial flow in the southern Indian Ocean, particularly in low-level Australian cross-equatorial flow. When positive SIOD and CP-type La Niña events occur simultaneously (PSIOD+La_Niña), precipitation increases in the Yangtze–Huaihe River basin, while it decreases in northern China. When negative SIOD and EP-type El Niño events occur simultaneously (NSIOD+El_Niño), precipitation in the Yangtze–Huaihe River basin is significantly lower than during P_NSIOD events. This is caused by differences in water vapor originating from the Pacific Ocean during different events.
{"title":"Relationship between the Southern Indian Ocean Dipole and ENSO and their effect on summer precipitation in China","authors":"Xingyu Li , Yuanhong Guan , Ran Dong , Qifeng Lu , Yue Zhang , Jiani Zhen","doi":"10.1016/j.aosl.2025.100618","DOIUrl":"10.1016/j.aosl.2025.100618","url":null,"abstract":"<div><div>Based on reanalysis data from 1979 to 2021, this study explores the spatial distribution of the Southern Indian Ocean Dipole (SIOD) and its individual and synergistic effects with the El Niño–Southern Oscillation (ENSO) on summer precipitation in China. The inverse phase spatial distribution of sea surface temperature anomalies (SSTAs) in the southwest and northeast of the southern Indian Ocean is defined as the SIOD. Positive SIOD events (positive SSTAs in the southwest, negative SSTAs in the northeast) are associated with La Niña events (Central Pacific (CP) type), while negative SIOD events (negative SSTAs in the southwest, positive SSTAs in the northeast) are associated with El Niño events (Eastern Pacific (EP) type). Both SIOD and ENSO have certain impacts on summer precipitation in China. Precipitation in the Yangtze River basin decreases, while precipitation in southern China increases during pure positive SIOD (P_PSIOD) events. During pure negative SIOD (P_NSIOD) events, the changes in precipitation are exactly the opposite of those during P_PSIOD events, which may be due to differences in the cross-equatorial flow in the southern Indian Ocean, particularly in low-level Australian cross-equatorial flow. When positive SIOD and CP-type La Niña events occur simultaneously (PSIOD+La_Niña), precipitation increases in the Yangtze–Huaihe River basin, while it decreases in northern China. When negative SIOD and EP-type El Niño events occur simultaneously (NSIOD+El_Niño), precipitation in the Yangtze–Huaihe River basin is significantly lower than during P_NSIOD events. This is caused by differences in water vapor originating from the Pacific Ocean during different events.</div><div>摘要</div><div>基于1979年至2021年的再分析数据, 本文探讨了南印度洋偶极子 (SIOD) 的空间分布及其与厄尔尼诺-南方涛动 (ENSO) 对中国夏季降水的独立和协同影响. 南印度洋西南部和东北部海表面温度异常 (SSTAs) 的反相位空间分布被定义为 SIOD. 正SIOD事件 (西南部正SSTAs, 东北部负SSTAs) 多伴随La Niña事件, 且主要为中太平洋 (CP) 型; 而负SIOD事件 (西南部负SSTAs, 东北部正SSTAs) 则多伴随El Niño事件, 且主要为东太平洋 (EP) 型. SIOD和ENSO对中国夏季降水均有一定影响. 纯正SIOD (P_PSIOD) 事件期间, 长江流域降水减少, 而华南降水增加. 纯负SIOD (P_NSIOD) 事件期间, 降水变化与P_PSIOD事件相反, 这可能与越赤道气流 (特别是澳大利亚低空越赤道气流) 有关. 当正SIOD与CP型La Niña事件同时发生时 (PSIOD+La_Niña), 江淮流域降水增加, 而华北降水减少. 当负SIOD与EP型El Niño事件同时发生时 (NSIOD+El_Niño), 江淮流域降水明显低于P_NSIOD事件期间, 这主要归因于不同事件背景下太平洋水汽输送的差异.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 1","pages":"Article 100618"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-11-01Epub Date: 2025-04-28DOI: 10.1016/j.aosl.2025.100636
Hongtao Yang , Guoxing Chen , Qing Bao , Bian He
Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics. However, it is often overlooked in the evaluation and development of climate models. Thus, this study aims to investigate the daily mean (CFR) and diurnal variation (CDV) of cloud fraction across high-, middle-, low-level, and total clouds in the FGOALS-f3-L general circulation model. The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels. Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime. The simulation biases of low clouds, especially their CDV biases, dominate the bias of total CDV. Compensation effects exist among the bias decompositions, where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds. Meanwhile, the bias contributions have notable land–ocean differences and region-dependent characteristics, consistent with the model biases in these variables. Additionally, the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects. It reveals that the impacts of CDV biases can reach half of those from CFR biases, highlighting the importance of accurate CDV representation in climate models.
{"title":"Bias characteristics of cloud diurnal variation in the FGOALS-f3-L model","authors":"Hongtao Yang , Guoxing Chen , Qing Bao , Bian He","doi":"10.1016/j.aosl.2025.100636","DOIUrl":"10.1016/j.aosl.2025.100636","url":null,"abstract":"<div><div>Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics. However, it is often overlooked in the evaluation and development of climate models. Thus, this study aims to investigate the daily mean (CFR) and diurnal variation (CDV) of cloud fraction across high-, middle-, low-level, and total clouds in the FGOALS-f3-L general circulation model. The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels. Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime. The simulation biases of low clouds, especially their CDV biases, dominate the bias of total CDV. Compensation effects exist among the bias decompositions, where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds. Meanwhile, the bias contributions have notable land–ocean differences and region-dependent characteristics, consistent with the model biases in these variables. Additionally, the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects. It reveals that the impacts of CDV biases can reach half of those from CFR biases, highlighting the importance of accurate CDV representation in climate models.</div><div>摘要</div><div>云量日变化可以调节云辐射效应, 影响大气动力过程, 但在气候模式评估中常被忽视. 本研究评估了FGOALS-f3-L模式中高, 中, 低云及总云云量的日均值和日变化特征. 结果表明, 模式普遍低估白天低云云量和夜间中, 高云云量. 低云云量日变化误差主导总云云量日变化误差. 其中, 低云误差造成的负值贡献被中, 高云误差的正值贡献部分抵消. 误差贡献呈现显著的海陆和区域差异, 与相应云量的模式误差一致. 同时, 云量日变化误差对短波云辐射效应误差的影响可达日均云量影响的一半, 突显了在模式中准确表征云量日变化的重要性.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100636"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-11-01Epub Date: 2025-02-27DOI: 10.1016/j.aosl.2025.100610
Xinli Liu , Jingzhi Su , Yihao Peng , Xiaolei Liu
Subseasonal forecasting of extreme events is crucial for early warning systems. However, the forecast skills for extreme events are limited. Taking the extreme cold events in January 2018 as a specific example, and analyzing the 34 extreme cold events in East Asia from 1998 to 2020, the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales. The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time, some individual members demonstrate high forecast skills. For most extreme cold events, there are >10 % of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time. This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales. High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns (500-hPa geopotential height, mean sea level pressure) and key weather systems, including the Ural Blocking and Siberian High, that influence extreme cold events.
{"title":"High-skill members in the subseasonal forecast ensemble of extreme cold events in East Asia","authors":"Xinli Liu , Jingzhi Su , Yihao Peng , Xiaolei Liu","doi":"10.1016/j.aosl.2025.100610","DOIUrl":"10.1016/j.aosl.2025.100610","url":null,"abstract":"<div><div>Subseasonal forecasting of extreme events is crucial for early warning systems. However, the forecast skills for extreme events are limited. Taking the extreme cold events in January 2018 as a specific example, and analyzing the 34 extreme cold events in East Asia from 1998 to 2020, the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales. The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time, some individual members demonstrate high forecast skills. For most extreme cold events, there are >10 % of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time. This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales. High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns (500-hPa geopotential height, mean sea level pressure) and key weather systems, including the Ural Blocking and Siberian High, that influence extreme cold events.</div><div>摘要</div><div>极端事件次季节预报对防灾减灾保障社会经济安全具有重要意义. 本研究针对东亚地区极端低温事件的次季节预报难题, 通过分析1998–2020年34起东亚地区极端低温事件, 并重点关注2018年1月中国东北地区极端低温事件, 系统评估不同版本ECMWF模式集合成员之间的预报性能. 提前3周的模式集合平均预报性能存在局限, 但不同集合成员的预报技巧存在差异. 部分成员具有高预报技巧, 约10 %的高技巧成员能提前14天捕捉气温快速转折的过程. 研究指出集合成员是否具有高预报技巧依赖于对大气环流演变特征的合理预报. 该发现为极端冷事件次季节预报评估和后期订正提供了新视角, 凸显挖掘集合成员预报潜力的重要性, 并为提升次季节时间尺度预警能力提供了理论支撑.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100610"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-11-01Epub Date: 2025-06-18DOI: 10.1016/j.aosl.2025.100666
Tingting Huang , Shenming Fu , Xiao Li , You Dong , Yuanchun Zhang , Jianhua Sun
From 26 October to 2 November 2024, Spain experienced a record-breaking rainfall event, with the most intense episode appearing in Valencia Province. During the event, Turis station recorded a historic 24-hour precipitation of 710.8 mm, exceeding the national annual average. This resulting flood led to widespread disruption and significant societal impacts. Synoptic analyses reveal that the event was dominated by a deep cut-off low extending through the entire troposphere and persisting for approximately 186 h. Background conditions were characterized by upper-level divergence, mid-tropospheric warm advection, and a strong southeasterly low-level jet, which promoted vertical motion and sustained moisture transport. The steep, funnel-shaped terrain along the eastern Iberian coast further triggered and enhanced the local convection. A 10-day backward Lagrangian moisture tracing using the HYSPLIT model identified the Mediterranean Sea as the primary moisture source (78.1 %), followed by northwestern Africa (8.5 %) and central-eastern Europe/the Black Sea (6.2 %). Low-level moisture transport was mainly driven by the cut-off low and a persistent Mediterranean high, while mid- to upper-level trajectories were associated with a preceding low-pressure system over the Mediterranean and the subtropical Atlantic high. These systems acted in sequence to relay moisture toward the Valencia region, and under the influence of the strongly rotating and convergent cut-off low—along with terrain-induced lifting—this moisture was rapidly uplifted, ultimately triggering the extreme rainfall event.
{"title":"Synoptic background conditions and moisture transport for producing the extreme heavy rainfall event in Valencia in 2024","authors":"Tingting Huang , Shenming Fu , Xiao Li , You Dong , Yuanchun Zhang , Jianhua Sun","doi":"10.1016/j.aosl.2025.100666","DOIUrl":"10.1016/j.aosl.2025.100666","url":null,"abstract":"<div><div>From 26 October to 2 November 2024, Spain experienced a record-breaking rainfall event, with the most intense episode appearing in Valencia Province. During the event, Turis station recorded a historic 24-hour precipitation of 710.8 mm, exceeding the national annual average. This resulting flood led to widespread disruption and significant societal impacts. Synoptic analyses reveal that the event was dominated by a deep cut-off low extending through the entire troposphere and persisting for approximately 186 h. Background conditions were characterized by upper-level divergence, mid-tropospheric warm advection, and a strong southeasterly low-level jet, which promoted vertical motion and sustained moisture transport. The steep, funnel-shaped terrain along the eastern Iberian coast further triggered and enhanced the local convection. A 10-day backward Lagrangian moisture tracing using the HYSPLIT model identified the Mediterranean Sea as the primary moisture source (78.1 %), followed by northwestern Africa (8.5 %) and central-eastern Europe/the Black Sea (6.2 %). Low-level moisture transport was mainly driven by the cut-off low and a persistent Mediterranean high, while mid- to upper-level trajectories were associated with a preceding low-pressure system over the Mediterranean and the subtropical Atlantic high. These systems acted in sequence to relay moisture toward the Valencia region, and under the influence of the strongly rotating and convergent cut-off low—along with terrain-induced lifting—this moisture was rapidly uplifted, ultimately triggering the extreme rainfall event.</div><div>摘要</div><div>2024年10月26日至11月2日, 西班牙瓦伦西亚省遭遇罕见极端降雨, Turis站24小时降水量达710.8毫米, 引发严重洪涝灾害. 此次事件由持续186小时的深厚切断低压主导, 在高层辐散, 中层暖平流与低空东南急流共同作用下形成强垂直运动, 东海岸漏斗地形进一步增强对流. HYSPLIT后向追踪显示, 水汽主要来自地中海 (贡献率78.1 %), 其次为非洲西北部 (8.5 %) 和欧洲中东部/黑海 (6.2 %). 水汽由多个天气系统接力输送至瓦伦西亚, 最终在切断低压旋转辐合和地形抬升作用下, 引发此次破纪录降雨事件.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100666"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-11-01Epub Date: 2025-01-28DOI: 10.1016/j.aosl.2025.100596
Caixia Liang , Jiacan Yuan
The likelihood of extreme heat occurrence is continuously increasing with global warming. Under high temperatures, humidity may exacerbate the heat impact on humanity. As atmospheric humidity depends on moisture availability and is constrained by air temperature, it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms. Here, a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point (a measure of humidity) on local temperature evolving with escalating global mean surface temperature. By applying these models to 297 weather stations in seven regions in China, the study analyzes historical trends of humid-heat and dry-hot days, and projects their changes under global warming of 2.0°C and 4.5°C. In response to global warming, rising trends of humid-heat extremes, while weakening trends of dry-hot extremes, are observed at most stations in Northeast China. Additionally, results indicate an increasing trend in dry-hot extremes at numerous stations across central China, but a rise in humid-heat extremes over Northwest China and coastal regions. These trends found in the current climate state are projected to intensify under 2.0°C and 4.5°C warming, possibly influenced by the heterogeneous variations in precipitation, soil moisture, and water vapor fluxes. Requiring much lower computational resources than coupled climate models, these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming, potentially informing the risk management of compound humid-heat extremes on a local scale.
{"title":"The evolving distribution of humidity conditional on temperature and implications for compound heat extremes across China in a warming world","authors":"Caixia Liang , Jiacan Yuan","doi":"10.1016/j.aosl.2025.100596","DOIUrl":"10.1016/j.aosl.2025.100596","url":null,"abstract":"<div><div>The likelihood of extreme heat occurrence is continuously increasing with global warming. Under high temperatures, humidity may exacerbate the heat impact on humanity. As atmospheric humidity depends on moisture availability and is constrained by air temperature, it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms. Here, a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point (a measure of humidity) on local temperature evolving with escalating global mean surface temperature. By applying these models to 297 weather stations in seven regions in China, the study analyzes historical trends of humid-heat and dry-hot days, and projects their changes under global warming of 2.0°C and 4.5°C. In response to global warming, rising trends of humid-heat extremes, while weakening trends of dry-hot extremes, are observed at most stations in Northeast China. Additionally, results indicate an increasing trend in dry-hot extremes at numerous stations across central China, but a rise in humid-heat extremes over Northwest China and coastal regions. These trends found in the current climate state are projected to intensify under 2.0°C and 4.5°C warming, possibly influenced by the heterogeneous variations in precipitation, soil moisture, and water vapor fluxes. Requiring much lower computational resources than coupled climate models, these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming, potentially informing the risk management of compound humid-heat extremes on a local scale.</div><div>摘要</div><div>本研究利用非交叉分位数平滑样条, 对中国七个气候分区的297个气象站分别建立了分位数回归模型, 模拟露点温度基于局地温度的条件概率密度分布对全球变暖的响应, 并预测了这些分布分别在2.0°C和4.5°C温升情景下的变化. 结果表明, (1) 这些分布对全球变暖的响应存在较大的区域异质性: 东北地区, 西北地区与沿海地区大多数站点呈现出极端湿热事件增加的趋势; 而中国中部地区的多个站点呈现出极端干热事件增加的趋势. (2) 这些趋势预计在2.0°C和4.5°C的温升情景下将进一步加剧.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100596"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-11-01Epub Date: 2025-03-08DOI: 10.1016/j.aosl.2025.100611
Yuqing Tian , Ke Fan , Hongqing Yang , Zhiqing Xu
Based on a normalized difference vegetation index (NDVI) dataset for 1982–2021, this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function (EOF) analysis. The first three principal modes (EOF1–3) of the year-to-year increment of summer NDVI (NDVI_DY) exhibit a regionally consistent mode, a western–eastern dipole mode, and a northern–southern dipole mode, respectively. Further analysis shows that sea surface temperature (SST) in the tropical Indian Ocean in February–March and western Siberian soil moisture in April could influence EOF1. EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May. May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3. Using the year-to-year increment method and multiple linear regression analysis, prediction schemes for EOF1–3 are developed based on these predictors. To assess the predictive skill of these schemes, one-year-out cross-validation and independent hindcast methods are employed. The temporal correlation coefficients between observed EOF1–3 and the cross-validation results are 0.62, 0.46, and 0.37, respectively, exceeding the 95 % confidence level. In addition, reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY. Independent hindcasts of NDVI anomalies during 2019–2021 also present consistent distributions with the observed results.
{"title":"Principal modes of summer NDVI in eastern Siberia and its climate prediction schemes","authors":"Yuqing Tian , Ke Fan , Hongqing Yang , Zhiqing Xu","doi":"10.1016/j.aosl.2025.100611","DOIUrl":"10.1016/j.aosl.2025.100611","url":null,"abstract":"<div><div>Based on a normalized difference vegetation index (NDVI) dataset for 1982–2021, this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function (EOF) analysis. The first three principal modes (EOF1–3) of the year-to-year increment of summer NDVI (NDVI_DY) exhibit a regionally consistent mode, a western–eastern dipole mode, and a northern–southern dipole mode, respectively. Further analysis shows that sea surface temperature (SST) in the tropical Indian Ocean in February–March and western Siberian soil moisture in April could influence EOF1. EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May. May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3. Using the year-to-year increment method and multiple linear regression analysis, prediction schemes for EOF1–3 are developed based on these predictors. To assess the predictive skill of these schemes, one-year-out cross-validation and independent hindcast methods are employed. The temporal correlation coefficients between observed EOF1–3 and the cross-validation results are 0.62, 0.46, and 0.37, respectively, exceeding the 95 % confidence level. In addition, reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY. Independent hindcasts of NDVI anomalies during 2019–2021 also present consistent distributions with the observed results.</div><div>摘要</div><div>本文基于1982–2021年的归一化植被指数 (Normalized Difference Vegetation Index, NDVI) 数据集, 利用年际增量法和经验正交函数方法提取了夏季东西伯利亚地区NDVI的年际变化主模态. NDVI年际增量 (NDVI_DY) 的前3个主模态 (EOF1–3) 分别呈全区一致, 东西偶极子和南北偶极子变化特征. 进一步分析影响其主模态变化的影响因子显示, 前期2–3月热带印度洋海温和4月西西伯利亚土壤湿度是全区一致模态的关键影响因子, 东西偶极子模态受前期4月西北太平洋海温和5月西西伯利亚土壤湿度的调控, 前期5月北大西洋海温和前一年10月喀拉海海冰与南北偶极子模态相联. 本文利用上述前期关键影响因子, 基于多元线性回归分析和年际增量方法建立了夏季东西伯利亚地区NDVI_DY主模态的预测模型. 其中, 观测的EOF1–3对应的时间序列与各模态交叉检验结果的时间相关系数分别为0.62, 0.46和0.37, 均超过了95 %的置信水平. 此外, 利用预测的各模态对应时间序列和观测的主模态, 本文进一步建立了夏季东西伯利亚地区NDVI的场重建预测模型. 2019–2021年夏季东西伯利亚地区独立后报的NDVI距平的空间分布也与观测一致.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100611"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerical simulation of the merging of a thunderstorm cluster from the mountain area near Beijing and a thunderstorm over the adjacent plains on 23 August 2021, along with a diagnosis and analysis of the cold pool and vertical motion, reveals the following: (1) The thunderstorm cluster in the mountain area moved slowly westward, weakening during its descent, whereas the thunderstorm cluster in the urban area moved rapidly eastward and intensified. Eventually, the two thunderstorm clusters encountered each other at the foot of the mountain and organized into a linear convective system. (2) Prior to merging, the thunderstorm cluster in the mountain area was blocked by warm advection to the east, causing the system to slow down, the cold pool to weaken, and the convergence and ascent associated with the cold pool outflow to diminish. In contrast, the thunderstorm cluster over the adjacent plains was driven by cold advection to the west, accelerating the system's movement, strengthening the cold pool, and enhancing the convergence and ascent driven by the cold pool outflow. After the thunderstorm clusters merged, the convergence of the northwesterly and southeasterly winds, as well as precipitation, led to the rapid accumulation of cold air, strengthening the cold pool and its upward development, which acted similarly to a terrain feature, further enhancing convergence and ascent. (3) The vertical motion reveals that before merging, the thunderstorm cluster in the mountain area was dominated by negative buoyancy at lower levels, which suppressed the development of ascent, whereas the thunderstorm cluster over the adjacent plains was driven by positive disturbances in the vertical pressure gradient force, which increased ascent. After the merging, the positive disturbances in the vertical pressure gradient force dominated below 2 km, and as the vertical motion increased, the positive buoyancy gradually became the dominant driver, further strengthening the ascent. The analysis suggests that the positive potential temperature disturbance and the southeasterly or southerly winds over the adjacent plains had opposing effects on the two approaching thunderstorm clusters, with the thunderstorm cluster over the adjacent plains taking the lead during the merging process.
{"title":"Mechanistic study of a downhill merging and enhancement of convection in Beijing","authors":"Xinyu Zhao , Lingkun Ran , Shunwu Zhou , Xinyong Shen , Mingxuan Chen , Yanli Chu","doi":"10.1016/j.aosl.2025.100595","DOIUrl":"10.1016/j.aosl.2025.100595","url":null,"abstract":"<div><div>Numerical simulation of the merging of a thunderstorm cluster from the mountain area near Beijing and a thunderstorm over the adjacent plains on 23 August 2021, along with a diagnosis and analysis of the cold pool and vertical motion, reveals the following: (1) The thunderstorm cluster in the mountain area moved slowly westward, weakening during its descent, whereas the thunderstorm cluster in the urban area moved rapidly eastward and intensified. Eventually, the two thunderstorm clusters encountered each other at the foot of the mountain and organized into a linear convective system. (2) Prior to merging, the thunderstorm cluster in the mountain area was blocked by warm advection to the east, causing the system to slow down, the cold pool to weaken, and the convergence and ascent associated with the cold pool outflow to diminish. In contrast, the thunderstorm cluster over the adjacent plains was driven by cold advection to the west, accelerating the system's movement, strengthening the cold pool, and enhancing the convergence and ascent driven by the cold pool outflow. After the thunderstorm clusters merged, the convergence of the northwesterly and southeasterly winds, as well as precipitation, led to the rapid accumulation of cold air, strengthening the cold pool and its upward development, which acted similarly to a terrain feature, further enhancing convergence and ascent. (3) The vertical motion reveals that before merging, the thunderstorm cluster in the mountain area was dominated by negative buoyancy at lower levels, which suppressed the development of ascent, whereas the thunderstorm cluster over the adjacent plains was driven by positive disturbances in the vertical pressure gradient force, which increased ascent. After the merging, the positive disturbances in the vertical pressure gradient force dominated below 2 km, and as the vertical motion increased, the positive buoyancy gradually became the dominant driver, further strengthening the ascent. The analysis suggests that the positive potential temperature disturbance and the southeasterly or southerly winds over the adjacent plains had opposing effects on the two approaching thunderstorm clusters, with the thunderstorm cluster over the adjacent plains taking the lead during the merging process.</div><div>摘要</div><div>以往针对北京平原雷暴群和下山雷暴群合并过程的研究相对较少, 利用WRF模拟数据对2021年8月23日一次此类对流活动过程分析发现： (1) 雷暴群前侧风场和热力条件的差异, 使得山区雷暴群移动和冷池发展受阻, 而平原雷暴群则相反, 最终在山脚处合并, 增强后的冷池起到类似地形作用, 增强辐合和上升运动； (2) 合并前, 山区雷暴群低层负热力浮力抑制上升运动发展, 平原雷暴群低层正扰动垂直气压梯度力加速上升运动, 合并后, 正扰动垂直气压梯度力 (2 km以下) 和正热力浮力 (2 km以上) 共同驱动上升运动发展. 本文主要对冷池和垂直运动分析, 以期为北京地区的临近预报提供一些有用的科学参考.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100595"},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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