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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Yonghui Li Wei Han Wansuo Duan Zeting Li Hao Li State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Severe Weather Meteorological Science and Technology (LASW) Beijing China CMA Earth System Modeling and Prediction Centre (CEMC) China Meteorological Administration Beijing China Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology Nanjing China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China Shanghai Academy of Artificial Intelligence for Science (SAIS) Shanghai China

The increasing volume of satellite data, particularly hyperspectral infrared data, combined with the real-time monitoring requirements of numerical weather prediction (NWP) systems, has heightened the demand for computational efficiency and accuracy in radiative transfer models (RTM). Machine learning (ML) offers a promising approach, and numerous studies on ML-based RTM have emerged recently. However, existing ML-based RTMs for hyperspectral infrared were not end-to-end. Moreover, since the label data do not represent truth, models trained with loss functions like mean squared error (MSE) or mean absolute error (MAE) fail to account for its uncertainty. This limitation can lead to suboptimal model parameters, as training may assign higher weights to labels with larger errors. This study construct an end-to-end ML-based RTM focused on clear sky conditions over the ocean for the FengYun-4B satellite (FY-4B) Geostationary Interferometric Infrared Sounder (GIIRS), using maximum likelihood estimation (MLE) and MSE for training, respectively. MLE accounts for the uncertainty of labels. The results indicate both models achieve high accuracy, with mean errors within 0.1 K (K) and standard deviation (STD) of errors within 0.04 K compared to the labels. The model trained with MLE exhibits a mean error closer to 0 and a STD similar to the error STD of labels, suggesting better parameter configurations to reflect the actual error distribution of the labels. Additionally, the temperature and water vapor Jacobian computed by both models are comparable to those obtained from RTTOV, highlighting their potential for application as observational operator in satellite data assimilation for hyperspectral infrared sounder. An end-to-end machine learning-based radiative transfer model for clear sky has been developed for FY-4B GIIRS The model, trained using a likelihood-based loss function that weights each channel differently, captures the uncertainty of labels The Jacobian matrix calc

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Propagation and influence on tropical precipitation of intraseasonal variation over mid-latitude East Asia in boreal winter

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Atmospheric and Oceanic Science Letters 2019年第3期12卷 155-161页

作者： JIAO Yang WU Renguang Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

The authors investigate the characteristics of propagation and the influence on tropical precipitation of 9–29-day intraseasonal variation over midlatitude East Asia during boreal winter, and find that the intraseasonal wind signal can propagate both eastward and southward. In the case of eastward propagation, the intraseasonal wind signal is mainly confined to the midlatitudes, featuring eastward migration of anomalous cyclones and anticyclones. In the case of southward propagation, intraseasonal meridional wind perturbations may extend from the mid to the low latitudes, and even the equatorial region. The accompanying wind convergence/divergence induces anomalous precipitation in the near-equatorial regions, forming a north–south dipole precipitation anomaly pattern between the southern South China Sea and the eastern China– Japan region. An anomalous meridional overturning circulation plays an important role in linking tropical and midlatitude intraseasonal wind and precipitation variations.

关键词： Intraseasonal wind variation midlatitude East Asia eastward and southward propagation tropical precipitation

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Decreasing emissions and increasing sink capacity to support China in achieving carbon neutrality before 2060

arXiv

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arXiv 2021年

作者： Han, Pengfei Zeng, Ning Zhang, Wen Cai, Qixiang Yang, Ruqi Yao, Bo Lin, Xiaohui Wang, Guocheng Liu, Di Yu, Yongqiang Carbon Neutrality Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Department of Atmospheric and Oceanic Science Earth System Science Interdisciplinary Center University of Maryland College ParkMD United States State Key Laboratory of Atmospheric Boundary Layer Physics Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Meteorological Observation Centre China Meteorological Administration Beijing China State Key Laboratory of Numerical Modeling for Atmospheric Sciences Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

In September 2020, President Xi Jinping announced that China strives to achieve carbon neutrality before 2060. This ambitious and bold commitment was well received by the global community. However, the technology and pathway are not so clear. Here, we conducted an extensive review covering more than 200 published papers and summarized the key technologies to achieve carbon neutrality. We projected sectoral CO2 emissions for 2020-2050 based on our previous studies and published scenarios. We applied a medium sink scenario for terrestrial sinks due to the potential resource competition and included an ocean sink, which has generally not been included in previous estimates. We analyzed and revisited China’s historical terrestrial carbon sink capacity from 1980-2020 based on multiple models and a literature review. To achieve neutrality, it is necessary to increase sink capacity and decrease emissions from many sources. On the one hand, critical measures to reduce emissions include decreasing the use of fossil fuels;substantially increasing the proportion of the renewable energy and nuclear energy. On the other hand, the capacity of future carbon sinks is projected to decrease due to the natural evolution of terrestrial ecosystems, and anthropogenic management practices are needed to increase sink capacity, including increasing the forest sinks through national ecological restoration projects and large-scale land greening campaigns;increasing wood harvesting and storage;and developing CCUS. This paper provides basic source and sink data, and established and promising new technologies for decreasing emissions and increasing sinks for use by the scientific community and policy makers. © 2021, CC BY.

关键词： Fossil fuels

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CODC-S: A quality-controlled global ocean salinity profiles dataset

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Scientific data 2025年第1期12卷 917页

作者： Zhetao Tan Yujing Zhu Lijing Cheng Viktor Gouretski Yuying Pan Huifeng Yuan Zhankun Wang Guancheng Li Xinyi Song Bin Zhang Senliang Bao Yuanlong Li Jiang Zhu State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China. Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace Ecole Normale Supérieure - Université PSL Paris 75231 France. University of Chinese Academy of Sciences Beijing 100049 China. State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China. chenglij@***. University of Chinese Academy of Sciences Beijing 100049 China. chenglij@***. Computer Network Information Center Chinese Academy of Sciences Beijing 100039 China. NOAA National Centers for Environmental Information Silver Spring MD 20910 USA. Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment Guangzhou 510611 China. Institute of Oceanology Chinese Academy of Sciences Qingdao 266071 China. Oceanographic Data Center Chinese Academy of Sciences Qingdao 266071 China. College of Meteorology and Oceanography National University of Defense Technology Changsha 410073 China.

Changes in the global ocean salinity reflect the evolution of the global hydrological cycle. These secular changes are assessed using seawater salinity profiles obtained during the past ~80 years. Here, we introduce a new global ocean salinity profiles database named CODC-S (the Chinese Academy of Science (CAS) Oceanography Data Center - Salinity component), which encompasses over 11 million in-situ salinity profiles from 1940 to 2023 obtained by means of several instrument types. These salinity profiles are quality-controlled (QC-ed) using a new automated salinity quality control system named CODC-QC-S (the CODC Quality Control system - Salinity component), consisting of 11 distinct quality checks. By applying time-varying, flow-dependent, and topographical-dependent 0.5% and 99.5% quantile thresholds, the CODC-QC-S defines local climatology salinity ranges without the assumption of Gaussian distribution. The CODC-S database, together with the newly proposed QC algorithm, has undergone extensive evaluations, including comparisons with the benchmark data and climatology, as well as analyses of global and basin-scale long-term salinity changes before and after QC. These validations demonstrate that the quality of salinity data in the CODC-S is time-, depth-, region- and instrument type-dependent. The eight-decade quality-homogeneous salinity profiles from the CODC-S database can support diverse oceanographic and climatic research such as monitoring water cycle changes and freshwater/overturning transports.

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Decadal climate variability during the pre-industrial Common Era: characteristics and mechanisms

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Science Bulletin 2025年

作者： Ning, Liang Liu, Zhengyu Mann, Michael E. Liu, Jian Yan, Mi Sun, Weiyi Chen, Kefan Qin, Yanmin Wen, Qin State Key Laboratory of Climate System Prediction and Risk Management Nanjing210023 China Key Laboratory for Virtual Geographic Environment Ministry of Education Nanjing210023 China State Key Laboratory Cultivation Base of Geographical Environment Evolution and Regional Response of Jiangsu Province Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China School of Geography Nanjing Normal University Nanjing210023 China Department of Geography The Ohio State University ColumbusOH43210 United States Department of Earth and Environmental Science Annenberg School for Communication University of Pennsylvania PhiladelphiaPA19104 United States Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical Science Nanjing Normal University Nanjing210023 China

In this article, we present a comprehensive review of decadal to multi-decadal climate variability during the Common Era (CE), focusing on their characteristics and mechanisms. We begin by summarizing recent advances in proxy reconstructions that reveal the paleo-evidence of decadal to multi-decadal climate variability during the CE. Decadal to multi-decadal variability has been observed in extensive sets of proxy records in the CE. Despite improvements in proxy records in the type, temporal resolution, and temporal coverage, there remains a lack of clear consistency in the preferred time scales and phases of the variability among different records. The agreements of decadal characteristics between proxy records and model simulations are higher during the periods with strong external forcings, but lower during periods of weak external forcing. We subsequently describe the recent modeling studies on the influences of external forcings and internal variability on decadal to multi-decadal climate variability with associated physical mechanisms, and some emerging research topics. Despite the improved understanding of climate variability and regional climate changes, especially over the eastern Asia summer monsoon region, several inconsistencies still exist, such as the amplitudes of responses to external forcings and relative contributions from external forcings and internal variability. The review ends with perspectives for future directions to reconcile discrepancies of decadal climate variability, such as applications of paleoclimate data assimilation and isotope-enabled transient climate modeling, and implications for projecting future decadal to multi-decadal climate changes and for improving the accuracy of decadal predictions. © 2025 Science China Press

关键词： Global warming

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Interdecadal change in the lagged relationship between the Victoria mode and ENSO

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Atmospheric and Oceanic Science Letters 2019年第4期12卷 294-301页

作者： XIE Wei FAN Guangzhou DING Ruiqiang LI Jianping LI Baosheng QIN Jianhuang ZHOU Xuejing School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information TechnologyChengduChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Global Change and Earth System Sciences Beijing Normal UniversityBeijingChina Institute of Oceanography Shanghai Jiao Tong UniversityShanghaiChina

This paper investigates the interdecadal variability in the lagged relationship between the spring Victoria mode(VM)and the following-winter El Ni?o–Southern Oscillation(ENSO).It is found that the relationship is strong during high correlation(HC)periods,e.g.1957–1964 and 1981–2004,but weak during low correlation(LC)periods,e.g.1907–1924,1926–1956,1965–1980,and2005–*** surface air–sea coupling and the evolution of subsurface ocean temperature anomaliesh along the equatorial Paci?c associated with the VM are found to be strong during HC periods and weak during LC periods,which results in a stronger impact of the VM on the following-winter ENSO during HC *** interdecadal change in the relationship between the VM and ENSO is mainly due to the interdecadal change in the intensity of the VM,which is found to be in?uenced by the North Paci?c ***?ndings may improve the prediction skill for the onset of ENSO events.

关键词： Victoria mode ENSO interdecadal change North Pacific Oscillation

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Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective

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Computers, Environment and Urban systems 2025年 121卷

作者： Xiaorui Yan Tao Pei Ci Song Zidong Fang Xiaohan Liu Tianyu Liu Linfeng Jiang Ying Gao Guangdong Li Jie Huang Yaqin Sun State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China College of Earth and Environmental Sciences Lanzhou University Lanzhou China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China

Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.

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Predictability of Ensemble Forecasting Estimated Using the Kullback–Leibler Divergence in the Lorenz Model

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Advances in Atmospheric Sciences 2019年第8期36卷 837-846页

作者： Ruiqiang DING Baojia LIU Bin GU Jianping LI Xuan LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth Sciences University of Chinese Academy of SciencesBeijing 100049China Institute of Space Weather School of Math and StatisticsNanjing University of Information Science and TechnologyNanjing 210044China College of Physics and Optoelectronic Engineering Nanjing University of Information Science and TechnologyNanjing 210044China College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875China

A new method to quantify the predictability limit of ensemble forecasting is presented using the Kullback–Leibler(KL)divergence(also called the relative entropy), which provides a measure of the difference between the probability distributions of ensemble forecasts and local reference(true) states. The KL divergence is applicable to a non-normal distribution of ensemble forecasts, which is a substantial improvement over the previous method using the ensemble spread. An example from the three-variable Lorenz model illustrates the effectiveness of the KL divergence, which can effectively quantify the predictability limit of ensemble forecasting. On this basis, the KL divergence is used to investigate the dependence of the predictability limit of ensemble forecasting on the initial states and the magnitude of initial errors. The local predictability limit of ensemble forecasting varies considerably with the initial states, as well as with the magnitude of initial errors. Further research is needed to examine the real-world applications of the KL divergence in measuring the predictability of ensemble weather forecasts.

关键词： predictability ensemble forecasting Kullback–Leibler divergence

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Towards reliable Arctic sea ice prediction using multivariate data assimilation

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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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