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Record High Temperatures in the Ocean in 2024

Advances in Atmospheric sciences 2025年第6期42卷 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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Record High Temperatures in the Ocean in 2024

引用

Advances in Atmospheric sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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A Solution Analysis of U.S. Weapon Equipment systems-of-systems Development and Application

A Solution Analysis of U.S. Weapon Equipment Systems-of-Syst...

引用

International Conference on Information science and Control Engineering (ICISCE)

作者： Menglong Lin Shuanghui Yi Mengmeng Zhang Zhen Shu Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China National Key Laboratory for Complex System Simulation Beijing China

In recent years, the development and deployment of the U.S. weapon equipment systems-of-systems (SoS) have yielded rich research results. However, there are still large gaps in the SoS development, joint testing, and design and acquisition of our own equipment SoS. The barriers of these steps have not yet been opened up. This article mainly analyzes the solutions of the U.S. weapon equipment SoS from three aspects, such as capability-based requirement generation and architecture design technology, interconnected-oriented integration test technology, and weapon equipment design and acquisition technology based on complex adaptive systems. We explore key technologies involved in order to form an overall understanding and explore related research approaches.

关键词： Technological innovation Adaptive systems Weapons Agent-based modeling Planning Sustainable development system analysis and design

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China Land Carbon Budget(CLCB1.0):a comprehensive estimate of the land carbon budget in China

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national science Review 2025年第4期 15-18页

作者： Jiangzhou Xia Xiaosheng Xia Xuhui Wang Weimin Ju Zhengyang Lin Zhangcai Qin Yuxing Sang Yanzi Yan Wenping Yuan Xu Yue Haicheng Zhang Hao Zhou Qiuan Zhu Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University School of Atmospheric Sciences Sun Yat-sen University Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking University International Institute for Earth System Sciences Nanjing University Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application School of Atmospheric Sciences Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education) Sun Yat-sen University Department of Soil and Environment Swedish University of Agricultural Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology (NUIST) Carbon-Water Research Station in Karst Regions of Northern Guangdong School of Geography and Planning Sun Yat-sen University College of Meteorology and Oceanography National University of Defense Technology College of Geography and Remote Sensing Hohai University

The terrestrial ecosystem plays an important role in regulating the regional carbon balance, in particular for China, where ecological projects during past decades [1,2] have led to large increases in China's land carbon sinks, partially offsetting fossil fuel emissions. Therefore, accurate evaluation of land carbon fluxes in China is critical for improving our understanding of the magnitude of China's carbon budget, and projecting its future changes.

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Academy Transaction Note “non-smooth characteristics in uncertainty propagation for midcourse correction of Earth-Moon transfer orbit”

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Acta Astronautica 2025年

作者： Zeyue Li Haiyang Li Zhen Yang Jiyuan Lv College of Aerospace Science and Engineering National University of Defense Technology Changsha 410073 Hunan China State Key Laboratory of Space System Operation and Control Changsha 410073 China Astronaut Center of China Beijing 100094 China

This Note investigates the non-smooth characteristics in uncertainty propagation for Earth-Moon trajectory with midcourse corrections. A segmented and domain partition propagation framework considering the initial insertion and execution errors is developed. High-fidelity Monte Carlo simulations show the multiply-connected nature under the maneuver execution threshold.

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Coupled fluid-thermal investigation on non-ablative thermal protection system with spiked body and opposing jet combined configuration

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Chinese Journal of Aeronautics 2019年第6期32卷 1390-1402页

作者： Jie HUANG Weixing YAO Xianyang SHAN State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Fundamental Science for National Defense-Advanced Design Technology of Flight Vehicle Nanjing University of Aeronautics and AstronauticsNanjing 210016China System Design Institute Hubei Aerospace Technology AcademyWuhan 430040China

In this paper, a Non-Ablative Thermal Protection system(NATPS) with the spiked body and the opposing jet combined configuration is proposed to reduce the aerodynamic heating of the hypersonic vehicle, and the coupled fluid-thermal numerical analysis is performed to study the thermal control performance of the NATPS. The results show that the spiked body pushes the bow shock away from the protected structure and thus reduces the shock intensity and the wall heat flux. In addition, the low temperature gas of the opposing jet separates the high temperature gas behind the shock from the nose cone of the spiked body, ensuring the non-ablative property of the spiked body. Therefore, the NATPS reduces the aerodynamic heating by the reconfiguration of the flow field, and the thermal control efficiency of the system is better than the Thermal Protection system(TPS) with the single spiked body and the single opposing jet. The influencing factors of the NATPS are analyzed. Both increasing the length of the spiked body and reducing the total temperature of the opposing jet can improve the thermal control performance of the NATPS and the nonablative property of the spiked body. However, increasing the heat conductivity coefficient of the spiked body can enhance benefit the non-ablative property of the spiked body, but has little influence on the thermal control performance of the NATPS.

关键词： Coupled method Non-ablative Opposing jet Spiked body Thermal protection system

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Generative assimilation and prediction for weather and climate

arXiv

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arXiv 2025年

作者： Yang, Shangshang Nai, Congyi Liu, Xinyan Li, Weidong Chao, Jie Wang, Jingnan Wang, Leyi Li, Xichen Chen, Xi Lu, Bo Xiao, Ziniu Boers, Niklas Yuan, Huiling Pan, Baoxiang Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Key Laboratory of Mesoscale Severe Weather Ministry of Education Nanjing University Nanjing China Earth System Modelling School of Engineering and Design Technical University of Munich Munich Germany Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Chinese Academy of Meteorological Sciences Beijing China College of Meteorology and Oceanography National University of Defense Technology Changsha China School of Mathematical Sciences Peking University Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China China Meteorological Administration Key Laboratory for Climate Prediction Studies National Climate Center Beijing China Xiong’an Institute of Meteorological Artificial Intelligence Xiong’an China

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks [1–4]. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions [5] or climate projections [6]. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales. © 2025, CC BY-NC-SA.

关键词： Prediction models

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A novel IMA resource configuration method based on feature modeling and optimization 3

A novel IMA resource configuration method based on feature m...

引用

3rd IEEE International Conference on Electronic Information technology and Computer Engineering, EITCE 2019

作者： Wang, Lisong Hong, Yang Li, Kui Zhou, Qing Zhang, Guoquan Wang, Ming Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China National Key Laboratory of Science and Technology on Avionics Integration China National Aeronautic Radio Electronics Research Institute Shanghai200233 China

ISBN: (纸本)9781728135847

Integrated Modular avionics (IMA) is the latest generation of avionics systems, with a high level of integration and modularity that make more integrated functions and more sharing of resources. Software product line engineering (SPLE) approaches can reduce the cost associated with developing complex systems like IMA and obtain More IMA configurations which meet user requirements. Feature selection of the feature model resulting from the domain modeling is a key step in obtaining the functionality needed and the characteristics of the user. Due to the high safety requirements of the IMA system, various resident applications need to be provisioned at development time to meet various safety constraints, which makes the feature selection process need to consider the IMA resource configuration constraints. In this paper, by extending the feature model, the feature model can express the information of the software mapping in the IMA resource configuration. We also used the multi-objective genetic algorithm to optimize the functional and non-functional requirements. Moreover, experiments are carried out on different scale models to verify the effectiveness of the method. © 2019 IEEE.

关键词： Genetic algorithms

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Theoretical framework for geoacoustic inversion by adjoint method

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Chinese Physics B 2019年第10期28卷 397-400页

作者： Yang Wang Xiao-Feng Zhao School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an 710072Chin Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China College of Meteorology and Oceanography National University of Defense TechnologyNanjing 211101China

Traditional geoacoustic inversions are generally solved by matched-field processing in combination with metaheuristic global searching algorithms which usually need massive computations. This paper proposes a new physical framework for geoacoustic retrievals. A parabolic approximation of wave equation with non-local boundary condition is used as the forward propagation model. The expressions of the corresponding tangent linear model and the adjoint operator are derived, respectively, by variational method. The analytical expressions for the gradient of the cost function with respect to the control variables can be formulated by the adjoint operator, which in turn can be used for optimization by the gradient-based method.

关键词： geoacoustic inversion adjoint method parabolic equation non-local boundary condition

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