Pub Date : 2025-08-30DOI: 10.1016/j.aosl.2025.100712
Letian Chen, Ze Zhang, Yifei Jiang, Xiaojiang Zhang, Jiagen Li, Weimin Zhang, Huizan Wang
Energy transfers among internal waves in the northern South China Sea are not well characterized, particularly during typhoons, owing to the lack of in situ observations. Based on high-resolution mooring data collected during Typhoon Trami (2024), this study reveals the occurrence of robust vertical energy redistribution among diurnal internal tides (D1 ITs) and near-inertial waves (NIWs). Strikingly, the typhoon not only amplified the NIW energy but also triggered an unexpected surge in the D1 IT energy. The observed average net energy transfer rate of 1 × 10−7 W kg−1 from typhoon-forced NIWs to D1 ITs occurred at water depths of 120–170 m. Further bispectral analysis indicated that the energy transfer is driven by nonlinear wave–wave interaction. These results reveal the existence of a new energy transfer pathway—from atmospheric forcing to D1 ITs—and redefine the redistribution of the internal wave energy during extreme weather events.
{"title":"Observation of Typhoon Trami (2024)-induced energy cascade from near-inertial waves to diurnal internal tides","authors":"Letian Chen, Ze Zhang, Yifei Jiang, Xiaojiang Zhang, Jiagen Li, Weimin Zhang, Huizan Wang","doi":"10.1016/j.aosl.2025.100712","DOIUrl":"10.1016/j.aosl.2025.100712","url":null,"abstract":"<div><div>Energy transfers among internal waves in the northern South China Sea are not well characterized, particularly during typhoons, owing to the lack of in situ observations. Based on high-resolution mooring data collected during Typhoon Trami (2024), this study reveals the occurrence of robust vertical energy redistribution among diurnal internal tides (D1 ITs) and near-inertial waves (NIWs). Strikingly, the typhoon not only amplified the NIW energy but also triggered an unexpected surge in the D1 IT energy. The observed average net energy transfer rate of 1 × 10<sup>−7</sup> W kg<sup>−1</sup> from typhoon-forced NIWs to D1 ITs occurred at water depths of 120–170 m. Further bispectral analysis indicated that the energy transfer is driven by nonlinear wave–wave interaction. These results reveal the existence of a new energy transfer pathway—from atmospheric forcing to D1 ITs—and redefine the redistribution of the internal wave energy during extreme weather events.</div><div>摘要</div><div>台风期间内波间的能量传递特征及机制尚未得到清晰揭示. 本研究利用 2024 年南海北部“潭美”台风期间获取的高分辨率潜标观测数据, 发现全日内潮与近惯性内波之间存在显著的垂直能量串级现象. 台风作用下, 观测发现在近惯性波能量显著增强的同时, 全日内潮能量亦出现激增; 在 120–170 米深度范围的水层, 观测到台风强迫下近惯性波向全日内潮的平均净能量传递率达 1 × 10⁻⁷ W kg<sup>−1</sup>. 双谱分析结果进一步证实, 这一能量传递过程由非线性波-波相互作用主导驱动. 上述结果揭示了“大气强迫至全日内潮”这一全新的内波能量传递路径, 进而重新界定了极端天气事件作用下内波能量的再分配规律.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 2","pages":"Article 100712"},"PeriodicalIF":3.2,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986695","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 : 2025-08-27DOI: 10.1016/j.aosl.2025.100707
Zhan Lian , Kang Xu
The eddies in the southernmost southern Indian Ocean exert major dynamical and biogeochemical influences on the Earth system. Therefore, disentangling the relative contributions of vertical pumping and horizontal transport to water-property anomalies in the eddy cores is of fundamental importance. Here, the authors introduce a temperature–salinity gradient-ratio approach (the “R-method”) that compares vertical and meridional gradients to quantitatively separate the two processes. Application of the R-method to three-dimensional Argo observations reveals that horizontal transport, rather than vertical pumping, predominantly governs the observed temperature and salinity anomalies within eddy cores in the SIO. Independent theoretical estimations based on background meridional gradients, together with composites formed on isopycnal surfaces, further corroborate this conclusion. The results challenge the conventional assumption that vertical pumping invariably controls eddy-core property anomalies and demonstrate the utility of the R-method for diagnosing eddy impacts in climate and biogeochemical studies.
{"title":"Disentangling the relative contributions of vertical pumping and horizontal transport to water-property anomalies within eddy cores in the southern Indian Ocean","authors":"Zhan Lian , Kang Xu","doi":"10.1016/j.aosl.2025.100707","DOIUrl":"10.1016/j.aosl.2025.100707","url":null,"abstract":"<div><div>The eddies in the southernmost southern Indian Ocean exert major dynamical and biogeochemical influences on the Earth system. Therefore, disentangling the relative contributions of vertical pumping and horizontal transport to water-property anomalies in the eddy cores is of fundamental importance. Here, the authors introduce a temperature–salinity gradient-ratio approach (the “<em>R</em>-method”) that compares vertical and meridional gradients to quantitatively separate the two processes. Application of the R-method to three-dimensional Argo observations reveals that horizontal transport, rather than vertical pumping, predominantly governs the observed temperature and salinity anomalies within eddy cores in the SIO. Independent theoretical estimations based on background meridional gradients, together with composites formed on isopycnal surfaces, further corroborate this conclusion. The results challenge the conventional assumption that vertical pumping invariably controls eddy-core property anomalies and demonstrate the utility of the <em>R</em>-method for diagnosing eddy impacts in climate and biogeochemical studies.</div><div>摘要</div><div>南印度洋最南端的涡旋对整个地球系统的动力学和生物地球化学过程具有重大影响. 因此, 厘清垂直抽吸与水平输送对涡旋中心水体性质异常的相对贡献具有重要意义. 本研究提出了一种温-盐梯度比值方法 (“<em>R</em>方法”), 即通过比较海水温盐的垂向梯度比值与经向梯度比值, 可定量区分垂直抽吸与水平输送对温盐异常的相对贡献. 本研究将该方法应用于三维 Argo 观测资料后发现, 在南印度洋最南端区域内, 观测到的涡旋中心温盐异常主要由水平输送而非垂直抽吸所主导. 基于背景经向梯度的理论估算, 以及按等密度面合成的结果, 进一步验证了这一结论. 本研究结果表明, 传统假设“涡旋中心的温盐异常始终由垂直抽吸控制”并不成立. 研究结果还展示了利用<em>R</em>方法诊断涡旋水平和垂向贡献在气候与海洋生态研究中的应用前景.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 2","pages":"Article 100707"},"PeriodicalIF":3.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986694","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 : 2025-08-22DOI: 10.1016/j.aosl.2025.100706
Baoxin Feng , Mengrong Ding , Lingling Xie , Pengfei Lin , Weipeng Zheng , Hailong Liu
This research evaluates the performance of an eddy-resolving forecast system (LFS) in simulating mesoscale eddies over the South China Sea (SCS) through a comparative analysis with satellite observations and the reanalysis dataset from the Global Ocean Physics Reanalysis product (CMEMS). The findings indicate that the spatial characteristics of eddy kinetic energy, number, and amplitude of coherent mesoscale eddies simulated by LFS exhibit a reasonable agreement with satellite observations. The reproduced seasonal variations are also comparable to outputs from the CMEMS reanalysis dataset. Nevertheless, certain systematic biases have also been identified. In the SCS, LFS generates approximately 17 % fewer eddies than observed. Such biases are also evident in the CMEMS reanalysis dataset. Similar to the statistics shown in the CMEMS reanalysis dataset, both cyclonic and anticyclonic eddies are significantly weaker in LFS compared to the observations. Additionally, the composite three-dimensional structures of mesoscale eddies simulated by LFS exhibit a remarkable similarity to those identified in the CMEMS reanalysis datasets. This work lays the foundation for further studies using LFS to investigate the predictability of mesoscale eddies and enhance the accuracy of simulations.
{"title":"An assessment of mesoscale eddies simulated by a global eddy-resolving ocean forecast system in the South China Sea","authors":"Baoxin Feng , Mengrong Ding , Lingling Xie , Pengfei Lin , Weipeng Zheng , Hailong Liu","doi":"10.1016/j.aosl.2025.100706","DOIUrl":"10.1016/j.aosl.2025.100706","url":null,"abstract":"<div><div>This research evaluates the performance of an eddy-resolving forecast system (LFS) in simulating mesoscale eddies over the South China Sea (SCS) through a comparative analysis with satellite observations and the reanalysis dataset from the Global Ocean Physics Reanalysis product (CMEMS). The findings indicate that the spatial characteristics of eddy kinetic energy, number, and amplitude of coherent mesoscale eddies simulated by LFS exhibit a reasonable agreement with satellite observations. The reproduced seasonal variations are also comparable to outputs from the CMEMS reanalysis dataset. Nevertheless, certain systematic biases have also been identified. In the SCS, LFS generates approximately 17 % fewer eddies than observed. Such biases are also evident in the CMEMS reanalysis dataset. Similar to the statistics shown in the CMEMS reanalysis dataset, both cyclonic and anticyclonic eddies are significantly weaker in LFS compared to the observations. Additionally, the composite three-dimensional structures of mesoscale eddies simulated by LFS exhibit a remarkable similarity to those identified in the CMEMS reanalysis datasets. This work lays the foundation for further studies using LFS to investigate the predictability of mesoscale eddies and enhance the accuracy of simulations.</div><div>摘要</div><div>本文基于卫星观测数据与高分辨率再分析数据 (CMEMS), 对由中国科学院大气物理研究所自主研发的全球涡分辨率海洋预报系统 (LFS) 所模拟的南海中尺度涡进行了系统评估. 研究结果表明, LFS模拟的南海涡动能, 涡旋数量, 涡旋振幅等指标的空间分布特征均与卫星观测结果表现出良好的一致性. 此外, 其模拟的季节性变化特征以及涡旋的温盐三维结构特征, 也与 CMEMS再分析数据集结果高度吻合. 然而, 研究也揭示了一些系统性偏差: 在南海区域, LFS模拟的中尺度涡数量比观测结果约少17%, 这一偏差在CMEMS再分析数据集中同样显著.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 2","pages":"Article 100706"},"PeriodicalIF":3.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986693","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 : 2025-07-17DOI: 10.1016/j.aosl.2025.100690
Bowen Zhao , Tao Zhang , Yanfeng Wang , Pengfei Lin , Hailong Liu , Ping Huang , Wei Huang , Pengfei Wang , Yiwen Li
Marine heatwaves (MHWs) in the South China Sea (SCS) significantly impact marine ecosystems and socioeconomic development, yet accurately forecasting MHWs remains a challenge. This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and, in conjunction with the ocean forecasting system LICOM Forecast System (LFS), constructed a hybrid Fusion model using Wasserstein-Distance optimization. The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS. Overall, the Fusion model takes advantage of LFS and ConvLSTM, providing superior forecasts for both the duration and intensity of MHWs in the southern SCS. LFS (ConvLSTM) overestimates (underestimates) the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS. The Fusion model’s superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs. This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.
南海海洋热浪对海洋生态系统和社会经济发展产生重大影响，但对其进行准确预报仍是一个挑战。本研究建立了基于ConvLSTM的南海北部上层海洋温度预报模型，并结合海洋预报系统LICOM Forecast system (LFS)，构建了基于Wasserstein-Distance优化的混合融合模型。2022年夏季，在南海对这三种模型提前10天预测关键MHW指标的能力进行了评估。总的来说，融合模型利用了LFS和ConvLSTM，对南海南部mhw的持续时间和强度都提供了更好的预测。LFS （ConvLSTM）高估（低估）了强热带风暴的持续时间，所有模型在预测南海部分地区强热带风暴的强度方面都存在局限性。Fusion模式在强震预报方面的优势可能是由于其对强震期间混合层深度较浅的上层海洋热结构的反映更为真实。本研究强调了将深度学习技术与动态模型相结合可以提高MHW的预测，并且具有一定的物理可解释性。。本研究基于海洋再分析资料,利用ConvLSTM构建上层海温深度学习预报模型,并与LICOM海洋环境预报系统(LFS)融合,建立订正模型(融合)。评估ConvLSTM LFS和融合对2022年南海夏季MHW持续时间和平均强度的预报技巧表明:1)融合技巧最优;2) LFS, ConvLSTM, ConvLSTM；3)中文翻译：融合技术。中国日报，中国日报，中国日报。
{"title":"Combined LFS and ConvLSTM to forecast marine heatwaves: a case study","authors":"Bowen Zhao , Tao Zhang , Yanfeng Wang , Pengfei Lin , Hailong Liu , Ping Huang , Wei Huang , Pengfei Wang , Yiwen Li","doi":"10.1016/j.aosl.2025.100690","DOIUrl":"10.1016/j.aosl.2025.100690","url":null,"abstract":"<div><div>Marine heatwaves (MHWs) in the South China Sea (SCS) significantly impact marine ecosystems and socioeconomic development, yet accurately forecasting MHWs remains a challenge. This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and, in conjunction with the ocean forecasting system LICOM Forecast System (LFS), constructed a hybrid Fusion model using Wasserstein-Distance optimization. The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS. Overall, the Fusion model takes advantage of LFS and ConvLSTM, providing superior forecasts for both the duration and intensity of MHWs in the southern SCS. LFS (ConvLSTM) overestimates (underestimates) the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS. The Fusion model’s superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs. This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.</div><div>摘要</div><div>海洋热浪 (MHW) 严重威胁中国南海生态与经济, 亟需提高MHW预测能力. 本研究基于海洋再分析资料, 利用ConvLSTM构建上层海温深度学习预报模型, 并与LICOM海洋环境预报系统 (LFS) 融合, 建立订正模型 (Fusion). 评估ConvLSTM, LFS和Fusion对2022年南海夏季MHW持续时间和平均强度的预报技巧表明: 1) Fusion技巧最优; 2) LFS系统性高估, ConvLSTM系统性低估MHW持续时间; 3) 三个模型对部分海域的MHW平均强度预报存在局限. Fusion较高的MHW预报技巧可能与更合理的上层温度垂直结构有关. 本研究表明, 融合深度学习与动力模式可有效改进南海MHW预报, 并具物理可解释性.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 2","pages":"Article 100690"},"PeriodicalIF":3.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986692","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 : 2025-07-17DOI: 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":"2025-07-17","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 : 2025-07-04DOI: 10.1016/j.aosl.2025.100675
Huanmujin Yuan , Hong Wang , Yubin Li , Kevin K.W. Cheung , Zhiqiu Gao
This study presents a comprehensive evaluation of tropical cyclone (TC) forecast performance in the western North Pacific from 2013 to 2022, based on operational forecasts issued by the China Meteorological Administration. The analysis reveals systematic improvements in both track and intensity forecasts over the decade, with distinct error characteristics observed across various forecast parameters. Track forecast errors have steadily decreased, particularly for longer lead times, while error magnitudes have increased with longer forecast lead times. Intensity forecasts show similar progressive enhancements, with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts. The study also identifies several key patterns in forecast performance: typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems; intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems; and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases. These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems, and the remaining challenges in predicting TC changes and landfall behavior, providing valuable benchmarks for future forecast system development.
{"title":"Forecast errors of tropical cyclone track and intensity by the China Meteorological Administration from 2013 to 2022","authors":"Huanmujin Yuan , Hong Wang , Yubin Li , Kevin K.W. Cheung , Zhiqiu Gao","doi":"10.1016/j.aosl.2025.100675","DOIUrl":"10.1016/j.aosl.2025.100675","url":null,"abstract":"<div><div>This study presents a comprehensive evaluation of tropical cyclone (TC) forecast performance in the western North Pacific from 2013 to 2022, based on operational forecasts issued by the China Meteorological Administration. The analysis reveals systematic improvements in both track and intensity forecasts over the decade, with distinct error characteristics observed across various forecast parameters. Track forecast errors have steadily decreased, particularly for longer lead times, while error magnitudes have increased with longer forecast lead times. Intensity forecasts show similar progressive enhancements, with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts. The study also identifies several key patterns in forecast performance: typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems; intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems; and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases. These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems, and the remaining challenges in predicting TC changes and landfall behavior, providing valuable benchmarks for future forecast system development.</div><div>摘要</div><div>本文系统评估了中国气象局在2013至2022年间对西北太平洋热带气旋的预报能力. 结果表明: 过去十年间路径和强度预报均取得显著进步, 其中120小时强度预报误差年均降低0.26 m/s. 研究发现三个关键特征: (1) 台风级以上强热带气旋的路径预报误差小于弱气旋; (2) 强度预报存在系统性偏差, 对弱气旋预报偏强而对强气旋预报偏弱; (3) 近海区域路径预报误差较大. 这些结果反映出近年来对热带气旋预报能力的进步, 也指出了未来预报系统发展的关键方向.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 1","pages":"Article 100675"},"PeriodicalIF":3.2,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610575","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 : 2025-06-30DOI: 10.1016/j.aosl.2025.100672
Shenming Fu , Tingting Huang , Bo Wang , Xiao Li , Nan Zhang , Zhongcan Chen , Jingxue Wang , You Dong , Jianhua Sun
In mid-April 2025, northern and central-eastern China experienced a catastrophic compound disaster marked by Beaufort 8 or greater wind gusts affecting ∼3.5 × 106 km2, exposing ∼610 million residents to extreme conditions, with Typhoon-equivalent Beaufort 12 gusts battering Beijing’s Yanshan Mountains and Beaufort 14–15 winds devastating Inner Mongolia. This unprecedented event surpassed historical extremes at 64 weather stations, impacting 996 monitoring sites with winds exceeding the 99th percentile, including 478 stations recording historic top-three maxima. Concurrently, sandstorms engulfed ∼4.3 × 106 km2, reaching 18°N, while Hulunbuir faced a 1.5-m snowpack—a 30-year April record. Cascading infrastructure failures resulted in 1884 uprooted trees, approximately ¥16.6 million in urban damages (in Beijing), and the collapse of utility-scale photovoltaic systems across northern China and the Huang-Huai region, exacerbating the multi-faceted crisis. A brief analysis indicates the event was primarily driven by a vertically coupled cyclone system featuring a cold vortex at the middle and upper troposphere dynamically aligned with a lower-level cyclone/mesoscale vortex. The intense, deeply coupled cyclone system sustained the wind intensification primarily through its enhanced pressure gradient force and subsidence-induced downward transport of kinetic energy (KE) behind the cyclone’s core. Clarifying the controlling synoptic-scale weather systems and dominant physical mechanisms governing such extreme wind generation is critical for refining predictive models of these high-impact events while advancing the understanding of dynamic interactions within extreme wind regimes.
{"title":"The extreme windstorm of April 2025 in northern and central-eastern China: Historical ranking and synoptic origins","authors":"Shenming Fu , Tingting Huang , Bo Wang , Xiao Li , Nan Zhang , Zhongcan Chen , Jingxue Wang , You Dong , Jianhua Sun","doi":"10.1016/j.aosl.2025.100672","DOIUrl":"10.1016/j.aosl.2025.100672","url":null,"abstract":"<div><div>In mid-April 2025, northern and central-eastern China experienced a catastrophic compound disaster marked by Beaufort 8 or greater wind gusts affecting ∼3.5 × 10<sup>6</sup> km<sup>2</sup>, exposing ∼610 million residents to extreme conditions, with Typhoon-equivalent Beaufort 12 gusts battering Beijing’s Yanshan Mountains and Beaufort 14–15 winds devastating Inner Mongolia. This unprecedented event surpassed historical extremes at 64 weather stations, impacting 996 monitoring sites with winds exceeding the 99th percentile, including 478 stations recording historic top-three maxima. Concurrently, sandstorms engulfed ∼4.3 × 10<sup>6</sup> km<sup>2</sup>, reaching 18°N, while Hulunbuir faced a 1.5-m snowpack—a 30-year April record. Cascading infrastructure failures resulted in 1884 uprooted trees, approximately ¥16.6 million in urban damages (in Beijing), and the collapse of utility-scale photovoltaic systems across northern China and the Huang-Huai region, exacerbating the multi-faceted crisis. A brief analysis indicates the event was primarily driven by a vertically coupled cyclone system featuring a cold vortex at the middle and upper troposphere dynamically aligned with a lower-level cyclone/mesoscale vortex. The intense, deeply coupled cyclone system sustained the wind intensification primarily through its enhanced pressure gradient force and subsidence-induced downward transport of kinetic energy (KE) behind the cyclone’s core. Clarifying the controlling synoptic-scale weather systems and dominant physical mechanisms governing such extreme wind generation is critical for refining predictive models of these high-impact events while advancing the understanding of dynamic interactions within extreme wind regimes.</div><div>摘要</div><div>2025年4月中旬, 中国北部和中东部地区遭遇由8级以上阵风引发的复合型灾害, 影响范围约3.5 × 10⁶平方公里, 波及约6.1亿人口. 北京燕山山脉出现12级 (台风级) 阵风, 内蒙古局部地区风力达14–15级. 此次事件在64个气象站突破历史极值, 996个监测站点风速超过第99百分位 (478个站点创观测史前三极值) . 伴随沙尘暴影响范围达4.3 × 10⁶平方公里, 南扩至18°N; 呼伦贝尔出现1.5米积雪, 为30年来4月最深纪录. 灾害导致1884株树木倒伏, 北京城市设施损失约1660万元, 并造成华北, 黄淮地区光伏系统大面积损毁. 研究表明, 该事件由垂直耦合气旋系统驱动, 中高层冷涡与低层气旋/中尺度涡旋动力耦合, 通过增强气压梯度及下沉动能传输维持强风. 阐明此类极端风的天气系统及物理机制, 对改进预测模型及深化风场动力学认知具有重要意义.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 6","pages":"Article 100672"},"PeriodicalIF":3.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903232","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 : 2025-06-23DOI: 10.1016/j.aosl.2025.100667
Yuxin Jiang, Lin Chen, Haoqian Li, Yesheng Zhu
This study investigated the impacts of key parameters in CAM6’s deep convection and cloud physics schemes on the simulation of summer-mean precipitation over East Asia through conducting perturbed parameter ensemble (PPE) experiments. Utilizing the experimental platform of CAM6, a suite of 128 PPE simulations spanning 1979–2014 were generated through simultaneously perturbing 12 selected parameters. Using EOF analysis, this study firstly extracted the first two leading modes of the precipitation simulation biases. The authors further pinpointed the most critical parameters that have the most influential effects on the precipitation simulation biases, through conducting generalized linear model analysis. The first leading mode of precipitation simulation biases is primarily influenced by parameters from the cloud physics scheme, including the linear effects of dcs and eii, and the nonlinear effect of rhminl*dcs. These parameters influence the simulated total precipitation (PrecT) mainly by altering the large-scale precipitation (PrecL). The second leading mode is predominantly governed by the convection scheme parameter dmpdz, reflecting a competition between the changes in convective precipitation (PrecC) and PrecL in response to variations in dmpdz. An increase in dmpdz induces decreased PrecC and increased PrecL in East Asia, and both of the changes collectively shape the ultimate PrecT response to the adjusted dmpdz. Lastly, it is noteworthy that the nonlinear effect due to the interaction among parameters warrants attention when concurrently adjusting multiple parameters, and the precipitation biases from the PPE simulations resemble those identified through EOF analysis on the AMIP simulations, implying our findings may provide potential reference for other AGCMs.
{"title":"Parametric sensitivity analysis of East Asian summer-mean precipitation simulations by perturbed parameter ensemble experiments in CAM6","authors":"Yuxin Jiang, Lin Chen, Haoqian Li, Yesheng Zhu","doi":"10.1016/j.aosl.2025.100667","DOIUrl":"10.1016/j.aosl.2025.100667","url":null,"abstract":"<div><div>This study investigated the impacts of key parameters in CAM6’s deep convection and cloud physics schemes on the simulation of summer-mean precipitation over East Asia through conducting perturbed parameter ensemble (PPE) experiments. Utilizing the experimental platform of CAM6, a suite of 128 PPE simulations spanning 1979–2014 were generated through simultaneously perturbing 12 selected parameters. Using EOF analysis, this study firstly extracted the first two leading modes of the precipitation simulation biases. The authors further pinpointed the most critical parameters that have the most influential effects on the precipitation simulation biases, through conducting generalized linear model analysis. The first leading mode of precipitation simulation biases is primarily influenced by parameters from the cloud physics scheme, including the linear effects of <em>dcs</em> and <em>eii</em>, and the nonlinear effect of <em>rhminl</em>*<em>dcs</em>. These parameters influence the simulated total precipitation (PrecT) mainly by altering the large-scale precipitation (PrecL). The second leading mode is predominantly governed by the convection scheme parameter <em>dmpdz</em>, reflecting a competition between the changes in convective precipitation (PrecC) and PrecL in response to variations in <em>dmpdz</em>. An increase in <em>dmpdz</em> induces decreased PrecC and increased PrecL in East Asia, and both of the changes collectively shape the ultimate PrecT response to the adjusted <em>dmpdz</em>. Lastly, it is noteworthy that the nonlinear effect due to the interaction among parameters warrants attention when concurrently adjusting multiple parameters, and the precipitation biases from the PPE simulations resemble those identified through EOF analysis on the AMIP simulations, implying our findings may provide potential reference for other AGCMs.</div><div>摘要</div><div>本研究利用CAM6大气模式, 通过开展扰动参数集合 (PPE) 试验, 研究了CAM6中深对流方案和云物理方案的关键参数对东亚 (EA) 夏季平均降水模拟的影响. 通过同时扰动十二个关键参数, 本文开展了包含128个成员的PPE模拟试验. 本文首先利用EOF方法提取了降水模拟偏差的前两个主导模态. 进一步, 通过广义线性模型 (GLM) 分析, 甄别出了对降水模拟偏差影响最为关键的核心参数. 降水模拟偏差的第一主导模态主要受云物理方案参数的影响, 包括参数dcs和eii的线性效应, 以及参数rhminl*dcs的非线性效应. 这些参数主要通过改变大尺度降水 (PrecL) 来影响模拟的总降水 (PrecT) . 第二主导模态则主要由深对流方案参数dmpdz所主导, 它反映了当dmpdz变化时, 对流降水 (PrecC) 和PrecL变化之间的竞争关系. 增加dmpdz会导致东亚地区PrecC减少而PrecL增加, 这两种变化共同塑造了PrecT对dmpdz变化的最终响应. 值得指出的是, 当同时调整多个参数时, 参数间相互作用产生的非线性效应值得关注. 此外, 本文PPE模拟得到的降水偏差与AMIP模式非常相似, 这意味着研究结果可能为其他大气环流模式提供一定的科学参考.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"19 2","pages":"Article 100667"},"PeriodicalIF":3.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986696","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 : 2025-06-18DOI: 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":"2025-06-18","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}
The Three Gorges Region (TGR) of the Yangtze River basin exhibited warm and dry climatic characteristics in 2024. The annual mean temperature in the TGR was 18.6 °C, which was 1.2 °C above normal and marked the highest level since 1961. All four seasons were warmer than normal, with spring and autumn both recording their highest temperatures since 1961. Additionally, the TGR recorded 57.2 high-temperature days in 2024, reaching a historic high since 1961 and exceeding the previous record set in 2022 by 2.4 days. Annual rainfall was 11.2 % below normal, with spring, summer, and autumn all being drier than normal. However, the number of heavy rain days was slightly higher than normal. The annual mean wind speed in the TGR ranked as the second-highest since 1961, only slightly lower than in 2022. The annual mean relative humidity was below normal and the number of fog days across large areas of the TGR decreased compared to 2023. In 2024, the TGR experienced extreme high-temperature events characterized by exceptional intensity and prolonged duration, accompanied by generally severe meteorological drought conditions. During the year, the TGR also experienced frequent and intense cooling events, an early onset of heavy rainfall (including severe convective weather), and exceptionally extreme rainstorm events.
{"title":"State of the climate over the Three Gorges Region of the Yangtze River basin in 2024","authors":"Hongling Zeng, Xianyan Chen, Yundi Jiang, Xukai Zou, Tong Cui, Qiang Zhang, Linhai Sun","doi":"10.1016/j.aosl.2025.100664","DOIUrl":"10.1016/j.aosl.2025.100664","url":null,"abstract":"<div><div>The Three Gorges Region (TGR) of the Yangtze River basin exhibited warm and dry climatic characteristics in 2024. The annual mean temperature in the TGR was 18.6 °C, which was 1.2 °C above normal and marked the highest level since 1961. All four seasons were warmer than normal, with spring and autumn both recording their highest temperatures since 1961. Additionally, the TGR recorded 57.2 high-temperature days in 2024, reaching a historic high since 1961 and exceeding the previous record set in 2022 by 2.4 days. Annual rainfall was 11.2 % below normal, with spring, summer, and autumn all being drier than normal. However, the number of heavy rain days was slightly higher than normal. The annual mean wind speed in the TGR ranked as the second-highest since 1961, only slightly lower than in 2022. The annual mean relative humidity was below normal and the number of fog days across large areas of the TGR decreased compared to 2023. In 2024, the TGR experienced extreme high-temperature events characterized by exceptional intensity and prolonged duration, accompanied by generally severe meteorological drought conditions. During the year, the TGR also experienced frequent and intense cooling events, an early onset of heavy rainfall (including severe convective weather), and exceptionally extreme rainstorm events.</div><div>摘要</div><div>2024年长江三峡地区的气候呈暖干特征, 年平均气温创下新的纪录, 达到18.6 °C, 较常年偏高1.2 °C. 四季气温均偏高, 其中春秋季平均气温均为1961年以来历史同期最高. 高温日数为57.2天, 也为1961年以来最多. 年降水量较常年偏少11.2 %, 春, 夏, 秋三季降水均偏少, 但暴雨日数较常年略偏多. 年平均风速为1961年以来第二大, 仅略低于2022年. 年平均相对湿度偏小, 大部地区雾日数较2023年有所减少. 2024年, 三峡地区经历极端高温事件, 高温强度强, 持续时间长, 气象干旱总体偏重; 强降温频次多, 强度强; 强降水 (强对流) 天气过程偏早, 暴雨极端性强.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 5","pages":"Article 100664"},"PeriodicalIF":2.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696952","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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