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Towards reliable Arctic sea ice prediction using multivariate data assimilation

Science Bulletin 2019年第1期64卷 63-72页

作者： Jiping Liu Zhiqiang Chen Yongyun Hu Yuanyuan Zhang Yifan Ding Xiao Cheng Qinghua Yang Lars Nerger Gunnar Spreen Radley Horton Jun Inoue Chaoyuan Yang Ming Li Mirong Song Department of Atmospheric and Environmental Sciences University at Albany State University of New York College of Ocean and Meteorology Guangdong Ocean University Department of Atmospheric and Oceanic Sciences School of Physics Peking University College of Global Change and Earth System Science Beijing Normal University Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies and School of Atmospheric Sciences Sun Yat-sen University Alfred-Wegener-Institut Helmholtz Zentrum für Polar-und Meeresforschung University of Bremen Institute of Environmental Physics Lamont-Doherty Earth Observatory Columbia University Earth Institute National Institute of Polar Research Polar Research and Forecasting Division National Marine Environmental Forecasting Center State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.

关键词： Arctic sea ice prediction Remote sensing Data assimilation

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A NOTE ON THE ROLE OF THE INITIAL ERROR STRUCTURE IN THE TROPICS ON THE SEASONAL-TO-DECADAL FORECASTING SKILL IN THE EXTRATROPICS

arXiv

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arXiv 2024年

作者： Vannitsem, Stephane Duan, Wansuo Royal Meteorological Institute of Belgium Avenue Circulaire 3 Brussels1180 Belgium Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Beijing China

The predictability of a coupled system composed by a coupled reduced-order extratropical oceanatmosphere model forced by a low-order 3-variable tropical recharge-discharge model, is explored with emphasis on the long term forecasting capabilities. Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system, with a specific attention to the structure of the initial errors in the tropical model. Three main types of experiments are explored with random perturbations along the three Lyapunov vectors of the tropical model, along the two dominant Lyapunov vectors, and along the first Lyapunov vector, only. When perturbations are introduced along all vectors, forecasting biases are developing even if in a perfect model framework. Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector. This perturbation strategy allows furthermore for getting a reduced mean square error at long lead times of a few years, and to get reliable ensemble forecasts on the whole time range. These very counterintuitive findings further underline the importance of appropriately control the initial error structure in the tropics through data assimilation. © 2024, CC BY.

关键词： Tropics

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Quantifying local predictability of the Lorenz system using the nonlinear local Lyapunov exponent

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Atmospheric and Oceanic Science Letters 2017年第5期10卷 372-378页

作者： HUAI Xiao-Wei LI Jian-Ping DING Rui-Qiang FENG Jie LIU De-Qiang State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing China College of Earth Science University of Chinese Academy of Sciences Beijing China College of Global Change and Earth System Science (GCESS) Beijing Normal University Beijing China Joint Center for Global Change Studies Beijing Normal University Beijing China College of Atmospheric Sciences Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China GIobal Systems Division Earth System Research Laboratory/Oceanic and Atmospheric Research/National Oceanic and Atmospheric Administration Boulder CO USA Fujian Meteorological Observatory Fuzhou China

The nonlinear local Lyapunov exponent（NLLE） can be used as a quantification of the local predictability limit of chaotic systems. In this study, the phase-spatial structure of the local predictability limit over the Lorenz-63 system is investigated. It is found that the inner and outer rims of each regime of the attractor have a high probability of a longer than average local predictability limit, while the center part is the opposite. However, the distribution of the local predictability limit is nonuniformly organized, with adjacent points sometimes showing quite distinct error *** source of local predictability is linked to the local dynamics, which is related to the region in the phase space and the duration on the current regime.

关键词： Nonlinear local Lyapunov exponent local predictability limit chaotic system Lorenz system

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Regime Behavior in the Sea Surface Temperature-Cloud Radiative Forcing Relationships over the Pacific Cold Tongue Region

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Atmospheric and Oceanic Science Letters 2010年第5期3卷 271-276页

作者： WU Chun-Qiang ZHOU Tian-Jun De-Zheng SUN State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysieal Fluid Dynamics （LASG） Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Graduate University of Chinese Academy of Sciences Beijing 100049 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China Cooperative Institute for Environmental Studies/University of Colorado ＆ NOAA/Earth System Research Laboratory Boulder Colorado USA

Previous analyses on the estimates of water vapor and cloud-related feedbacks in the tropics usually use observations over the earth Radiation Budget Experiment （ERBE） period （1985-89）. To examine the sample dependence of previous estimates, the authors extend the analysis to two additional periods： 1990-94 and 1995-99. The results confirm our hypothesis, i.e., the values of the feedbacks depend on the period of data coverage. The differences in the feedbacks from cloud radiative forcings （CRFs） estimated from the three periods are particularly significant. Two possible causes for these differences are proposed. First, a regime behavior in the CRFs-Sea Surface Temperature Anomaly （SSTA） rela- tionship over the cold tongue region is revealed： when SSTA is below -0.5℃, the CRF anomalies are insensitive to the SSTA; when the SSTA is between -0.5℃ and 2.0℃, the CRF anomalies are positively correlated with the SSTA; however, when the SSTA exceeds 2.0℃, the CRF anomalies decrease with the SSTA. This regime behavior is due to the regime behavior of cirrostratus and deep convective clouds. Second, the CRFs-SSTA relationship is regulated by remote forcings. Warming of the far eastern equatorial Pacific would reduce the water vapor convergence over the central Pacific by weakening the trade wind over the southeastern Pacific, thereby reducing the feeding of moisture to the convective flow. The results suggest that CRFs-SSTA relationships during ENSO events are nonlinear and strongly depend on the magnitude and the spatial distribution of the SSTA.

关键词： cloud radiative feedback cloud-SST regime ENSO nonlinearity

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Varying contributions of fast and slow responses cause asymmetric tropical rainfall change between CO_(2) ramp-up and ramp-down

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Science Bulletin 2022年第16期67卷 1702-1711,M0004页

作者： Shijie Zhou Ping Huang Shang-Ping Xie Gang Huang Lin Wang Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100190China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Scripps Institution of Oceanography University of California San DiegoLa Jolla CA 92093USA Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China

Tropical rainfall is important for regional climate around the *** a warming climate forced by rising CO_(2),the tropical rainfall will increase over the equatorial Pacific where sea surface warming is locally ***,we analyze an idealized CO_(2) removal experiment from the Carbon Dioxide Removal Model Intercomparison Project(CDRMIP)and show that the tropical rainfall change features a stronger pattern during CO_(2) ramp-down than ramp-up,even under the same global mean temperature increase,such as the 2℃ goal of the Paris *** tropical rainfall during CO_(2) ramp-down increases over the equatorial Pacific with a southward extension,and decreases over the Pacific intertropical convergence zone and South Pacific convergence *** asymmetric rainfall changes between CO_(2) ramp-down and ramp-up result from time-varying contributions of the fast and slow oceanic responses to CO_(2) forcing,defined as the responses to abrupt CO_(2) forcing in the first 10 years and thereafter,respectively,in the abrupt-4xCO_(2) *** fast response follows the CO_(2) evolution,but the slow response does not peak until 60 years after the CO_(2) *** slow response features a stronger El Niño-like pattern,as the ocean dynamical thermostat effect is suppressed under stronger subsurface *** delayed and stronger slow response leads to stronger tropical rainfall changes during CO_(2) *** results indicate that returning the global mean temperature increase to below a certain goal,such as 2℃,by removing CO_(2),may fail to restore tropical convection distribution,with potentially devastating effects on climate worldwide.

关键词： Tropical rainfall Fast and slow responses Sea surface warming pattern CDRMIP

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Seamless Prediction in China: A Review

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Advances in Atmospheric Sciences 2023年第8期40卷 1501-1520页

作者： Hong-Li REN Qing BAO Chenguang ZHOU Jie WU Li GAO Lin WANG Jieru MA Yao TANG Yangke LIU Yujun WANG Zuosen ZHAO State Key Laboratory of Severe Weather and Institute of Tibetan Plateau MeteorologyChinese Academy of Meteorological SciencesBeijing 100081China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Department of Atmospheric Science School of Environmental StudiesChina University of GeosciencesWuhan 430074China China Meteorological Administration Key Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Ensemble Forecasting Division CMA Earth System Modeling and Prediction Center(CEMC)Beijing 100081China School of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225China

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and *** prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the ***,seamless prediction has received widespread attention from the international scientific community *** Chinese scientists have also carried out relevant research,this paper reviews the research in China on developments and applications of seamless prediction methods and prediction systems in recent *** them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate *** terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in China in recent years are ***,some of the issues in seamless prediction that need further study are discussed.

关键词： seamless prediction weather-climate integrated prediction prediction system

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Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric Sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Earth System Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact Research Potsdam 14412Germany Department of Mathematics and Global Systems Institute University of ExeterExeter EX44QEUK CMA Earth System Modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community earth system Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google earth Engine

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Dominant atmospheric circulation patterns associated with the rapid intensification of summer flash droughts in Eastern China

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Science of the Total Environment 2024年 957卷 177416页

作者： Ma, Feng Yuan, Xing Li, Hua Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing210044 China School of Hydrology and Water Resources Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China

Flash droughts (FDs), which are characterized by rapid intensification, occurred frequently over Eastern China, posing great challenges for drought forecasting and preparation on subseasonal timescale. However, the drivers of the rapid development of FDs are not well understood. By comparing with slow droughts (SDs), this study investigates the dominant physical processes responsible for FDs in four different regions over Eastern China through diagnosing moisture budgets and further linking them to large-scale atmospheric circulation patterns. Results reveal that the rapid intensification of FDs in the four regions are dominantly driven by stronger and more persistent subsidence than those for SDs, which suppress precipitation and heat the atmosphere, leading to rapid soil drying. Further analyses indicate that stronger and more stable atmospheric circulation systems are conductive to the rapid intensification of FDs. Specifically, in Northeast China, the persistent subsidence is strongly related to the maintenance of high over area south to Lake Baikal, which is associated with enhanced subtropical jet stream. In North China Plain, the upper-level cyclonic anomalies associated with the negative phase of the circumglobal teleconnection, together with more southward East Asia summer monsoon (EASM) rain belt, are responsible for the stronger sinking motions. In southern China, droughts can be attributed to simultaneous expansions of the South Asia high (SAH) and western Pacific subtropical high (WPSH). While the persistently joint control of the SAH and WPSH and larger area under the control of the two systems are crucial for FDs in Yangtze River basin and South China, respectively. The SDs are often related to the less organized circulation patterns and inconsistent shift of the EASM rain belt during the development phase. Our findings suggest potential source of predictability for summer FDs in Eastern China. © 2024 Elsevier B.V.

关键词： Drought

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Future earth Strategies in Northeast Asia: From Scientific Research to the Construction of Community with a Shared Future for Mankind

Future Earth Strategies in Northeast Asia: From Scientific R...

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作者： Yuan, Naiming Ha, Kyung-Ja Watanabe, Masahiro Zhou, Tianjun Dong, Wenjie School of Atmospheric Sciences Sun Yat-sen University Zhuhai China Center for Climate Physics Institute for Basic Science Busan Korea Republic of BK21 School of Earth and Environmental Systems Pusan National University Busan Korea Republic of Division for Climate System Research Atmosphere and Ocean Research Institute The University of Tokyo Chiba Japan State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

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