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seasonal prediction of Extreme High-Temperature Days in Southwestern China Based on the Physical Precursors

Advances in Atmospheric Sciences 2023年第7期40卷 1212-1224页

作者： Zhiyi ZHOU Juan LI Haishan CHEN Zhiwei ZHU Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100086China University of Chinese Academy of Sciences Beijing 100049China

Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.

关键词： extreme high-temperature days southwestern China seasonal prediction predictability

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seasonal prediction of the Record-Breaking Northward Shift of the Western Pacific Subtropical High in July 2021

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Advances in Atmospheric Sciences 2023年第3期40卷 410-427页

作者： Shuai HU Tianjun ZHOU Bo WU Xiaolong CHEN State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of the Chinese Academy of Sciences Beijing 100049China

The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.

关键词： western pacific subtropical high seasonal prediction seasonal predictability La Niña Pacific-Japan pattern

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seasonal prediction of extreme high-temperature days over the Yangtze River basin

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Science China Earth Sciences 2024年第7期67卷 2137-2147页

作者： Shifeng PAN Zhicong YIN Mingkeng DUAN Tingting HAN Yi FAN Yangyang HUANG Huijun WANG Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and Technology Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Extreme high temperatures occur frequently over the densely populated Yangtze River basin(YRB) in China during summer, significantly impacting the local economic development and ecological system. However, accurate prediction of extreme high-temperature days in this region remains a challenge. Unfortunately, the Climate Forecast System Version 2(CFSv2) exhibits poor performance in this regard. Thus, based on the interannual increment approach, we develop a hybrid seasonal prediction model over the YRB(HMYRB) to improve the prediction of extreme high-temperature days in summer.The HMYRBrelies on the following four predictors: the observed preceding April–May snowmelt in north western Europe; the snow depth in March over the central Siberian Plateau; the CFSv2-forecasted concurrent summer sea surface temperatures around the Maritime Continent; and the 200-hPa geopotential height over the Tibetan Plateau. The HMYRBindicates good capabilities in predicting the interannual variability and trend of extreme high-temperature days, with a markable correlation coefficient of 0.58 and a percentage of the same sign(PSS) of 76% during 1983–2015 in the one-year-out cross-validation. Additionally, the HMYRB maintains high PSS skill(86%) and robustness in the independent prediction period(2016–2022). Furthermore, the HMYRB shows a good performance for years with high occurrence of extreme high-temperature days, with a hit ratio of 40%. These predictors used in HMYRBare beneficial in terms of the prediction skill for the average daily maximum temperature in summer over the YRB, albeit with biases existing in the magnitude. Our study provides promising insights into the prediction of 2022-like hot extremes over the YRB in China.

关键词： Hot extremes Yangtze River basin CFSv2 seasonal prediction One-year-out cross-validation

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seasonal prediction of Monthly Precipitation in China Using Large-Scale Climate Indices

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Advances in Atmospheric Sciences 2010年第1期27卷 47-59页

作者： Maeng-Ki KIM Yeon-Hee KIM Department of Atmospheric Science Kongju National University

In this study, seasonal predictions were applied to precipitation in China on a monthly basis based on a multivariate linear regression with an adaptive choice of predictors drawn from regularly updated climate indices with a two to twelve month lead time. A leave-one-out cross validation was applied to obtain hindcast skill at a 1% significance level. The skill of forecast models at a monthly scale and their significance levels were evaluated using Anomaly Correlation Coefficients （ACC） and Coefficients Of Determination （COD）. The monthly ACC skill ranged between 0.43 and 0.50 in Central China, 0.41-0.57 in East China, and 0.41 0.60 in South China. The dynamic link between large-scale climate indices with lead time and the precipitation in China is also discussed based on Singular Value Decomposition Analysis （SVDA） and Correlation Analysis （CA）.

关键词： seasonal prediction precipitation in China climate predictors climate index

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Deep Learning for seasonal Precipitation prediction over China

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Journal of Meteorological Research 2022年第2期36卷 271-281页

作者： Weixin JIN Yong LUO Tongwen WU Xiaomeng HUANG Wei XUE Chaoqing YU Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084 Beijing Climate Center China Meteorological AdministrationBeijing 100081 AI for Earth Lab the Joint Laboratory of Cross-Strait Tsinghua Research InstituteXiamen 361006

Despite significant progress having been made in recent years,the forecast skill for seasonal precipitation over China remains limited.In this study,a deep-learning-based statistical prediction model for seasonal precipitation over China was developed.The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data.First,it was pre-trained with the hindcasts of several general circulation models(GCMs),and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation,with the anomaly pattern correlation coefficients(PCCs)greater than 0.80.Then,transfer learning was applied by using ECMWF Reanalysis v5(ERA5)data and gridded precipitation observational data over China,to further correct the systemic errors in the model.As a result,using general circulation fields from reanalysis as the input,this hybrid model performed reasonably well in simulating the seasonal precipitation over China,with the PCC reaching 0.71.In addition,the results using the circulation fields predicted by GCMs as the input were also assessed.In general,the proposed model improves the PCC over China by 0.10-0.13,as compared to the raw GCM outputs,for lead times of 1-4 months.This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.

关键词： seasonal precipitation seasonal prediction statistical downscaling deep learning

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seasonal prediction of the Global Precipitation Annual Modes with the Grid-Point Atmospheric Model of IAP LASG

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Acta meteorologica Sinica 2009年第4期23卷 428-437页

作者：吴志伟李建平 LASG Institute of Atmospheric Physics Chinese Academy of Sciences Graduate School of the Chinese Academy of Sciences

A right annual cycle is of critical importance for a model to improve its seasonal prediction skill. This work assesses the performance of the Grid-point Atmospheric Model of IAP LASG （GAMIL） in retrospective prediction of the global precipitation annual modes for the 1980 2004 period. The annual modes are gauged by a three-parameter metrics： the long-term annual mean and two major modes of annual cycle （AC）, namely, a solstitial mode and an equinoctial asymmetric mode. The results demonstrate that the GAMIL one-month lead prediction is basically able to capture the major patterns of the long-term annual mean as well as the first AC mode （the solstitial monsoon mode）. The GAMIL has deficiencies in reproducing the second AC mode （the equinoctial asymmetric mode）. The magnitude of the GAMIL prediction tends to be greater than the observed precipitation, especially in the sea areas including the Arabian Sea, the Bay of Bengal （BOB）, and the western North Pacific （WNP）. These biases may be due to underestimation of the convective activity predicted in the tropics, especially over the western Pacific warm pool （WPWP） and its neighboring areas. It is suggested that a more accurate parameterization of convection in the tropics, especially in the Maritime Continent, the WPWP and its neighboring areas, may be critical for reproducing the more realistic annual modes, since the enhancement of convective activity over the WPWP and its vicinity can induce suppressed convection over the WNP, the BOB, and the South Indian Ocean where the GAMIL produces falsely vigorous convections. More efforts are needed to improve the simulation not only in monsoon seasons but also in transitional seasons when the second AC mode takes place. Selection of the one-tier or coupled atmosphere-ocean system may also reduce the systematic error of the GAMIL prediction. These results offer some references for improvement of the GAMIL seasonal prediction skill.

关键词： seasonal prediction global precipitation annual cycle climate model

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Probabilistic seasonal prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes

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Acta meteorologica Sinica 2011年第3期25卷 283-292页

作者：李芳 Institute of Atmospheric Physics Chinese Academy of Sciences

The skill of probability density function （PDF） prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones： the equally-weighted ensemble （EE）, EE for calibrated model-simulations （Cali-EE）, the ensemble scheme based on multiple linear regression analysis （MLR）, and the Bayesian ensemble scheme （Bayes）. The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.

关键词： multi-model ensemble uncertainty probability density function seasonal prediction rainfall

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seasonal prediction Experiments of the Summer Droughts and Floods during the Early 1990′s in East Asia with Numerical Models

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Advances in Atmospheric Sciences 1998年第4期15卷 3-16页

作者：黄荣辉李旭袁重光陆日宇中科院大气物理所北京 Pusan National University 韩国

It has been shown by the observed data that during the early 1990′s, the severe disastrous climate occurred in East Asia. In the summer of 1991, severe flood occurred in the Yangtze River and the Huaihe River basin of China and in South Korea, and it also appeared in South Korea in the summer of 1993. However, in the summer of 1994, a dry and hot summer was caused in the Huaihe River basin of China and in R. O. K.. In order to investigate the seasonal predictability of the summer droughts and floods during the early 1990′s in East Asia, the seasonal prediction experiments of the summer droughts and floods in the summers of 1991-1994 in East Asia have been made by using the Institute of Atmopsheric Physics-Two-Level General Circulation Model (IAP-L2 AGCM), the IAP-Atmosphere/Ocean Coupled Model (IAP-CGCM) and the IAP-L2 AGCM including a filtering scheme, respectively. Compared with the observational facts, it is shown that the IAP-L2 AGCM or IAP-CGCM has some predictability for the summer droughts and floods during the early 1990′s in East Asia, especially for the severe droughts and floods in China and R. O. K.. In this study, a filtering scheme is used to improve the seasonal prediction experiments of the summer droughts and floods during the early 1990′s in East Asia. The predicted results show that the filtering scheme to remain the planetary-scale disturbances is an effective method for the improvement of the seasonal prediction of the summer droughts and floods in East Asia.

关键词： seasonal prediction Drought and flood General circulation model (GCM)

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2011 Spring seasonal prediction of SDS in Northeast Asia Based on DGAEM

2011 Spring Seasonal Prediction of SDS in Northeast Asia Bas...

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第28届中国气象学会年会

作者： YANG Yuanqin,WANG Jizhi,Niu Tao,Hou Qing Liu Yiyan,Chen Miao(Atmospheric Composition Observing & Service Center,Chinese Academy of Meteorological Sciences,100081 Beijing)

Here we use a Discriminant Genetic Algorithm Extended Model（DGAEM） to diagnose and predict seasonal sand and dust storm（SDS） activities occurring in Northeast Asia.The study employed the regular meteorological data,including surface data,upper air data,and NCEP reanalysis data,collected from 1980-2006.The regional,seasonal,and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given.Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution.Genetic probability was used as a substitute for posterior probability of multi-level discriminants,to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms.This has unveiled the spatial distribution of the maximum adaptability,allowing the forecast field to be defined by the population with the largest probability,and made discriminant genetic extension possible.In addition,the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia.The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2010 and made the prediction in springtime 2011 by this model.The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities,which are a meaningful reference for seasonal SDS predictions in the future.

关键词： Discriminant Sand and Dust Storms seasonal prediction DGAEM Model

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Recent Advances in Dynamical Extra-seasonal to Annual Climate prediction at IAP/CAS

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Advances in Atmospheric Sciences 2004年第3期21卷 456-466页

作者：林朝晖王会军周广庆陈红郎咸梅赵彦曾庆存 International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing 100080 International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing 100080ecent advances in dynamical climate prediction at the Institute of Atmospheric Physics Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly the second generation of the IAP dynamical climate prediction system (IAP DCP-Ⅱ) has been described and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998 and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction along with studies on the influences from surface boundary conditions (e.g. land surface characteristics sea surface temperature). Certain conclusions have been reached such as the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally several practical techniques (e.g. ensemble technique correction method etc.) which lead to the increase of the prediction skill for summer rainfall anomalies over China have also been illustrated. The paper concludes with a list of critical requ

Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-Ⅱ) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

关键词： seasonal prediction ensemble technique ENSO prediction soil moisture correction method

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