This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics o...This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.展开更多
复杂地形风电机组建设时易形成高边坡地形,可能严重影响机组的发电量和疲劳寿命。以重庆市某复杂山地风电场为例,基于CDRFG(Consistent Discretizing Random Flow Generation)方法生成大气边界层湍流入口,采用大涡模拟技术重现高边坡复...复杂地形风电机组建设时易形成高边坡地形,可能严重影响机组的发电量和疲劳寿命。以重庆市某复杂山地风电场为例,基于CDRFG(Consistent Discretizing Random Flow Generation)方法生成大气边界层湍流入口,采用大涡模拟技术重现高边坡复杂地形的湍流风场分布,并根据激光测风雷达和测风塔实测数据验证大涡模拟结果的准确性;对比分析了风机平台开挖导致的3种不同高边坡地形下,风机机位湍流风场的差异性,提出了风机平台开挖影响评估指标,深入分析了高边坡地形对风电机组发电效益和安全运行的影响。研究为复杂地形风机平台建设提供了科学保障。展开更多
数值天气预报降尺度技术是获取百米级地面风场预报的重要手段。在复杂海湾地形,尽管动力降尺度能较好地表征百米级地面风场,但其对计算资源要求很高且尚未达到业务时效性要求。而统计降尺度的优点是计算效率高,但在实际应用中缺乏统计...数值天气预报降尺度技术是获取百米级地面风场预报的重要手段。在复杂海湾地形,尽管动力降尺度能较好地表征百米级地面风场,但其对计算资源要求很高且尚未达到业务时效性要求。而统计降尺度的优点是计算效率高,但在实际应用中缺乏统计建模所需的百米级实况场。因此,单一的降尺度方法难以满足复杂海湾地面风场的精细化预报需求。本研究选取福建湄洲湾冬季冷空气大风的8次过程为研究对象,基于水平分辨率为0.03°的CMA-GD模式模拟结果进行降尺度,结合动力降尺度和统计降尺度两种方法的优点,探讨复杂海湾百米级地面风场的降尺度技术。首先,利用WRF模式嵌套水平分辨率达111 m的大涡模拟(LES)进行动力降尺度,建立2019~2023年冬季冷空气大风过程的百米级WRF-LES地面风场模拟数据集。其次,基于2019~2022年CMA-GD气象要素模拟数据集和WRF-LES地面风场模拟数据集,采用随机森林算法构建两者之间的分区统计降尺度模型。利用湄洲湾区域气象自动站地面风观测进行对比评估,结果表明,与CMA-GD模拟相比,百米级WRF-LES数据集更能捕捉到近地面风速脉动的时空跳跃性和刻画湍流的脉动特征,与观测风速的均方根误差(RMSE)也更低,而2023年CMA-GD地面风模拟结果经分区随机森林统计模型降尺度至百米级后,空间分布特征与WRF-LES模拟吻合,风速随时间变化趋势也基本一致,且RMSE明显小于CMA-GD地面风模拟的双线性插值降尺度结果,大部分地区的RMSE控制在0~2.5 m s^(-1)范围内。综上所述,联合大涡模拟和机器学习的动力—统计降尺度模型能够有效地将复杂海湾地形的公里级地面风速降尺度至百米级,为复杂海湾地形精细化天气预报提供技术支持。展开更多
沙尘是中国北方典型的灾害天气。构建长时间尺度东亚地区高分辨率沙尘气溶胶质量浓度再分析数据集,是深化理解中国沙尘天气发生机理和提升多尺度预报水平的数据基础。受到风蚀起沙过程参数化方案、长距离输送误差等限制,当前沙尘模拟结...沙尘是中国北方典型的灾害天气。构建长时间尺度东亚地区高分辨率沙尘气溶胶质量浓度再分析数据集,是深化理解中国沙尘天气发生机理和提升多尺度预报水平的数据基础。受到风蚀起沙过程参数化方案、长距离输送误差等限制,当前沙尘模拟结果存在较大不确定性。鉴于此,本研究在前期开发的沙尘同化系统基础上,集成地面PM_(10)质量浓度、卫星气溶胶光学厚度(aerosol optical depth,AOD)观测非沙尘组分偏差校正技术,以及适用于沙尘气溶胶强度、位置误差协同校正的有效时刻偏移卡尔曼滤波同化算法(valid time shift ensemble Kalman filter,VTS-EnKF),建立了10 a(2014—2023年)东亚地区春季(3—5月)逐3 h的沙尘气溶胶三维质量浓度再分析数据集,分辨率为0.25°×0.25°。在此基础上,分析了所建立的再分析数据集相较于MERRA-2(modern-era retrospective analysis for research and applications version 2)沙尘再分析产品的优势,同时讨论了过去10 a东亚地区春季沙尘天气的月、年际变化趋势。展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
Since Blanford(1884)first linked Himalayan snowfall to subsequent Indian summer monsoon(ISM)rainfall,the potential for long-range forecasting has been recognized.Key advances followed:discovery of the Southern Oscilla...Since Blanford(1884)first linked Himalayan snowfall to subsequent Indian summer monsoon(ISM)rainfall,the potential for long-range forecasting has been recognized.Key advances followed:discovery of the Southern Oscillation(Walker,1925;Walker and Bliss,1932);identification of the Pacific-North American pattern(Wallace and Gutzler,1981);and Bjerknes’(1969)seminal study of ENSO as a predictable climate driver.Foundational theory—including equatorial wave dynamics(Matsuno,1966;Webster,1972;Gill,1980).展开更多
Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately...Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures.In this study,numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia.Using the Weather Research and Forecasting Model coupled with Chemistry(WRF-Chem)and European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)data,the most severe SDS events in the spring of 2023 in East Asia were numerically simulated.The simulated results were compared and validated using meteorological observations and multisource remote sensing data.The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events.The primary dust sources in spring 2023 were identified as the western desert of Mongolia,the Gobi Desert,and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China.Peak dust load and maximum wind speed occurred almost simultaneously,indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events.Increased surface vegetation covers partially mitigated wind-driven dust emissions.In April,strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways.Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China.In contrast,their impact on the northwestern regions of China remains relatively limited.These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant Nos.U2442206,42205067,and 41922035)the National Key R&D Program of China(Grant No.2024YFC3013100)the Key Research Program of Frontier Sciences of CAS(Grant No.QYZDB-SSW-DQC017).
文摘This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.
文摘复杂地形风电机组建设时易形成高边坡地形,可能严重影响机组的发电量和疲劳寿命。以重庆市某复杂山地风电场为例,基于CDRFG(Consistent Discretizing Random Flow Generation)方法生成大气边界层湍流入口,采用大涡模拟技术重现高边坡复杂地形的湍流风场分布,并根据激光测风雷达和测风塔实测数据验证大涡模拟结果的准确性;对比分析了风机平台开挖导致的3种不同高边坡地形下,风机机位湍流风场的差异性,提出了风机平台开挖影响评估指标,深入分析了高边坡地形对风电机组发电效益和安全运行的影响。研究为复杂地形风机平台建设提供了科学保障。
文摘数值天气预报降尺度技术是获取百米级地面风场预报的重要手段。在复杂海湾地形,尽管动力降尺度能较好地表征百米级地面风场,但其对计算资源要求很高且尚未达到业务时效性要求。而统计降尺度的优点是计算效率高,但在实际应用中缺乏统计建模所需的百米级实况场。因此,单一的降尺度方法难以满足复杂海湾地面风场的精细化预报需求。本研究选取福建湄洲湾冬季冷空气大风的8次过程为研究对象,基于水平分辨率为0.03°的CMA-GD模式模拟结果进行降尺度,结合动力降尺度和统计降尺度两种方法的优点,探讨复杂海湾百米级地面风场的降尺度技术。首先,利用WRF模式嵌套水平分辨率达111 m的大涡模拟(LES)进行动力降尺度,建立2019~2023年冬季冷空气大风过程的百米级WRF-LES地面风场模拟数据集。其次,基于2019~2022年CMA-GD气象要素模拟数据集和WRF-LES地面风场模拟数据集,采用随机森林算法构建两者之间的分区统计降尺度模型。利用湄洲湾区域气象自动站地面风观测进行对比评估,结果表明,与CMA-GD模拟相比,百米级WRF-LES数据集更能捕捉到近地面风速脉动的时空跳跃性和刻画湍流的脉动特征,与观测风速的均方根误差(RMSE)也更低,而2023年CMA-GD地面风模拟结果经分区随机森林统计模型降尺度至百米级后,空间分布特征与WRF-LES模拟吻合,风速随时间变化趋势也基本一致,且RMSE明显小于CMA-GD地面风模拟的双线性插值降尺度结果,大部分地区的RMSE控制在0~2.5 m s^(-1)范围内。综上所述,联合大涡模拟和机器学习的动力—统计降尺度模型能够有效地将复杂海湾地形的公里级地面风速降尺度至百米级,为复杂海湾地形精细化天气预报提供技术支持。
文摘沙尘是中国北方典型的灾害天气。构建长时间尺度东亚地区高分辨率沙尘气溶胶质量浓度再分析数据集,是深化理解中国沙尘天气发生机理和提升多尺度预报水平的数据基础。受到风蚀起沙过程参数化方案、长距离输送误差等限制,当前沙尘模拟结果存在较大不确定性。鉴于此,本研究在前期开发的沙尘同化系统基础上,集成地面PM_(10)质量浓度、卫星气溶胶光学厚度(aerosol optical depth,AOD)观测非沙尘组分偏差校正技术,以及适用于沙尘气溶胶强度、位置误差协同校正的有效时刻偏移卡尔曼滤波同化算法(valid time shift ensemble Kalman filter,VTS-EnKF),建立了10 a(2014—2023年)东亚地区春季(3—5月)逐3 h的沙尘气溶胶三维质量浓度再分析数据集,分辨率为0.25°×0.25°。在此基础上,分析了所建立的再分析数据集相较于MERRA-2(modern-era retrospective analysis for research and applications version 2)沙尘再分析产品的优势,同时讨论了过去10 a东亚地区春季沙尘天气的月、年际变化趋势。
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
文摘Since Blanford(1884)first linked Himalayan snowfall to subsequent Indian summer monsoon(ISM)rainfall,the potential for long-range forecasting has been recognized.Key advances followed:discovery of the Southern Oscillation(Walker,1925;Walker and Bliss,1932);identification of the Pacific-North American pattern(Wallace and Gutzler,1981);and Bjerknes’(1969)seminal study of ENSO as a predictable climate driver.Foundational theory—including equatorial wave dynamics(Matsuno,1966;Webster,1972;Gill,1980).
基金supported by the Science&Technology Fundamental Resources Investigation Program(2023FY100700)the Key Project of Innovation LREIS(KPI006)+1 种基金the Key R&D and Achievement Transformation Program of Inner Mongolia Autonomous Region(2023KJHZ0027)the Construction Project of China Knowledge Centre for Engineering Sciences and Technology(CKCEST-2023-1-5).
文摘Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures.In this study,numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia.Using the Weather Research and Forecasting Model coupled with Chemistry(WRF-Chem)and European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)data,the most severe SDS events in the spring of 2023 in East Asia were numerically simulated.The simulated results were compared and validated using meteorological observations and multisource remote sensing data.The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events.The primary dust sources in spring 2023 were identified as the western desert of Mongolia,the Gobi Desert,and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China.Peak dust load and maximum wind speed occurred almost simultaneously,indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events.Increased surface vegetation covers partially mitigated wind-driven dust emissions.In April,strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways.Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China.In contrast,their impact on the northwestern regions of China remains relatively limited.These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
