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CMIP5模式对大尺度年平均地面气温异常的多年代际趋势的模拟评估和未来预估

Engineering 2017年第1期 289-305页

作者：邢楠李建平王兰宁 State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology

基于观测和第五次耦合模式比较计划（CMIP5）模式的模拟结果,本文对全球、半球、半球陆地及海洋尺度的年平均地面气温异常在过去一百多年及两个代表性浓度路径（RCPs）情景下的多年代际变化及趋势进行了评估分析。根据模式对全球平均地... 详细信息

基于观测和第五次耦合模式比较计划（CMIP5）模式的模拟结果,本文对全球、半球、半球陆地及海洋尺度的年平均地面气温异常在过去一百多年及两个代表性浓度路径（RCPs）情景下的多年代际变化及趋势进行了评估分析。根据模式对全球平均地面气温异常的时间变率、长期趋势、多年代际变化及趋势的模拟能力,筛选出15个模式进行分析。观测结果表明,北半球陆地、北半球海洋和南半球海洋平均地面气温异常与全球平均地面气温异常具有相似的多年代际变化特征:在1900—1944年及1971—2000年呈现增暖趋势,并在1945—1970年和2001—2013年呈现增暖停滞甚至变冷趋势。模式能够基本再现以上观测特征。然而,与以上变化不同的是,南半球陆地的平均地面气温在1945—1970年呈现增暖趋势,并且模式不能很好模拟该特征。对于近期的增暖停滞阶段（2001—2013年）,BCC-CSM1-1-m模式、CMCC-CM模式、GFDL-ESM2M模式及NorESM1-ME模式在RCP4.5和RCP8.5情景下预估的全球及半球尺度的地面气温异常趋势值最接近观测值,表明它们具有较好的预估能力。由于这四个模式在地面气温异常的多年代际趋势上具有较好的模拟及预估性能,故选择它们来预估2006—2099年的地面气温异常在全球及半球尺度上的变化特征。结果显示在RCP4.5（RCP8.5）情景下,所选四个模式集合平均的全球、北半球及南半球年平均地面气温异常趋势值分别为0.17（0.29）、0.22（0.36）及0.11（0.23）℃·decade;,其趋势值明显小于未经过模式筛选的CMIP5模式集合的结果。

关键词：地面气温异常多年代际趋势第五次耦合模式比较计划(CMIP5) 预估

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The Chinese Carbon-Neutral Goal:Challenges and Prospects

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Advances in Atmospheric Sciences 2022年第8期39卷 1229-1238,I0027-I0030页

作者： Ning ZENG Kejun JIANG Pengfei HAN Zeke HAUSFATHER Junji CAO Daniel KIRK-DAVIDOFF Shaukat ALI Sheng ZHOU Department of Atmospheric and Oceanic Science and Earth System Science Interdisciplinary CenterUniversity of MarylandCollege Park 20742MarylandUSA State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Energy Research Institute National Development and Reform CommissionBeijing 100045China Carbon Neutrality Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Breakthrough Institute Oakland 94612CaliforniaUSA Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Global Change Impact Study Centre Ministry of Climate ChangeIslamabad 45250Pakistan Institute of Energy Environment and EconomyTsinghua UniversityBeijing 100084China

On 22 September 2020,within the backdrop of the COVID-19 global pandemic,China announced its climate goal for peak carbon emissions before 2030 and to reach carbon neutrality before *** carbon-neutral goal is generally considered to cover all anthropogenic greenhouse *** planning effort is now in full swing in China,but the pathway to decarbonization is *** needed transition towards non-fossil fuel energy and its impact on China and the world may be more profound than its reform and development over the past 40 years,but the challenges are *** of four representative scenarios shows significant differences in achieving the carbon-neutral goal,particularly the contribution of non-fossil fuel energy *** high target values for nuclear,wind,and bioenergy have approached their corresponding resource limitations,with solar energy being the exception,suggesting solar’s critical *** also found that the near-term policies that allow for a gradual transition,followed by more drastic changes after 2030,can eventually reach the carbon-neutral goal and lead to less of a reduction in cumulative emissions,thus inconsistent with the IPCC 1.5°C *** challenges and prospects are discussed in the historical context of China’s socio-economic reform,globalization,international collaboration,and development.

关键词： carbon neutral carbon dioxide reductions energy system transformation distributed energy system model projections

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Boosting weather forecast via generative superensemble

arXiv

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arXiv 2024年

作者： Nai, Congyi Chen, Xi Yang, Shangshang Liang, Yuan Xiao, Ziniu Pan, Baoxiang Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Engineering and Design Technical University of Munich Munich Germany Potsdam Institute for Climate Impact Research Potsdam Germany TianJi Weather Science and Technology Company Beijing China

Accurate weather forecasting is essential for socioeconomic activities. While data-driven forecasting demonstrates superior predictive capabilities over traditional numerical Weather Prediction (NWP) with reduced computational demands, its deterministic nature and limited advantages over physics-based ensemble predictions restrict operational applications. We introduce the generative ensemble prediction system (GenEPS) framework to address these limitations by randomizing and mitigating both random errors and systematic biases. GenEPS provides a plug-and-play ensemble forecasting capability for deterministic models to eliminate random errors, while incorporating cross-model integration for cross-model ensembles to address systematic biases. The framework culminates in a superensemble approach utilizing all available data-driven models to further minimize systematic biases. GenEPS achieves an Anomaly Correlation Coefficient (ACC) of 0.679 for 500hPa geopotential (Z500), exceeding the ECMWF Ensemble Prediction system’s (ENS) ACC of 0.646. Integration of the ECMWF ensemble mean further improves the ACC to 0.683. The framework also enhances extreme event representation and produces energy spectra more consistent with ERA5 reanalysis. GenEPS establishes a new paradigm in ensemble forecasting by enabling the integration of multiple data-driven models into a high-performing super-ensemble system. © 2024, CC BY-NC-SA.

关键词： Prediction models

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气候变化情景下中国因高温造成的劳动生产力损失预估

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Science Bulletin 2023年第22期68卷 2827-2837,M0006页

作者：程亮亮谷魁英赵亮王会滨纪思翰刘钊黄建斌陈艺丹高学杰徐影王灿罗勇蔡闻佳宫鹏梁万年黄存瑞 School of Public Health Sun Yat-sen UniversityGuangzhou 510080China Vanke School of Public Health Tsinghua UniversityBeijing 100084China State Key Laboratory of Numerical Modelling for Atmosphere Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China School of Linkong Economics and Management Beijing Institute of Economics and ManagementBeijing 100102China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100084China Climate Change Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Climate Centre China Meteorological AdministrationBeijing 100081China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Department of Earth Sciences and Department of Geography University of Hong KongHong Kong 999077China Institute of Healthy Chin1 Tsinghua UniversityBeijing 100084China

气候变化背景下,高温会影响职业人群的工作效率并造成劳动生产力损失.本研究基于区域气候模式并采用本土化的暴露-反应函数,预估了未来气候变化情景下中国因高温造成的劳动时间损失.研究发现,与基线期的213亿小时高温相关劳动时间损失相... 详细信息

气候变化背景下,高温会影响职业人群的工作效率并造成劳动生产力损失.本研究基于区域气候模式并采用本土化的暴露-反应函数,预估了未来气候变化情景下中国因高温造成的劳动时间损失.研究发现,与基线期的213亿小时高温相关劳动时间损失相比,到21世纪末在RCP2.6、RCP4.5和RCP8.5情景下,损失将分别增加-17.8%、10.8%及121.1%.损失主要集中在华南、华中和华东地区,其中广东与河南将占到全国总损失的四分之一.因为这些区域的温度将更高、人口更密集、户外工作者在总人群中的比例较大.与RCP2.6、RCP4.5和RCP8.5情景相比,若能实现1.5℃温控目标,将分别能够避免11.8%、33.7%和53.9%的劳动时间损失,相当于避免了全中国21世纪中叶的0.1%、0.6%及1.4%的年均GDP损失.本研究显示未来气候变化会给中国造成严重的热相关劳动生产力损失,并强调亟需采取因地制宜的气候减缓及适应策略以保护职业人群健康.本研究对其他与中国相似的发展中国家同样具有重要意义.

关键词： Climate change Heat stress Labor Work hours lost Occupational health

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南大洋增暖及其气候影响

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Science Bulletin 2023年第9期68卷 946-960,M0004页

作者：蔡文炬高立宝罗义勇李熙晨郑小童张学斌程旭华贾凡 Ariaan Purich Agus Santoso 杜岩 David MHolland 石佳睿项宝强谢尚平 Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Physical Oceanography Ocean University of ChinaQingdao 266100China Laboratory for Ocean and Climate Dynamics Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Center for Southern Hemisphere Oceans Research Commonwealth Scientific and Industrial Research Organisation(CSIRO)Oceans and AtmosphereHobart 7004Australia First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China College of Oceanography Hohai UniversityNanjing 210098China Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of SciencesQingdao 266071China Climate Change Research Centre University of New South WalesSydney 2052Australia Australian Research Council Centre of Excellence for Climate Extremes University of New South WalesSydney 2052Australia State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 511458China Courant Institute of Mathematical Sciences New York UniversityNew York NY 10012USA Woods Hole Oceanographic Institution Woods Hole MA 02543USA NOAA/Geophysical Fluid Dynamics Laboratory Princeton NJ 08540USA University Corporation for Atmospheric Research Boulder CO 80307USA Scripps Institution of Oceanography University of California San DiegoLa Jolla CA 92093USA

过去几十年南大洋在南极平流层臭氧消耗和大气二氧化碳浓度增加的共同作用下大幅增暖,显著影响着南极冰架和冰盖的融化、海平面上升、南北半球的降水分配以及热带海洋和大气环流等,引发了深远的气候效应.全球变暖背景下南大洋西风表现... 详细信息

过去几十年南大洋在南极平流层臭氧消耗和大气二氧化碳浓度增加的共同作用下大幅增暖,显著影响着南极冰架和冰盖的融化、海平面上升、南北半球的降水分配以及热带海洋和大气环流等,引发了深远的气候效应.全球变暖背景下南大洋西风表现为极向加强,同时进入南大洋的热辐射和淡水通量持续增加,由此上翻的海水可吸收更多的热量和碳,经由更强的平均流向北输送,更多地在45°S附近海域下沉并存储.当前对南大洋增暖背后诸多复杂的物理过程仍缺乏足够的理解,比如来自冰架、冰盖及海洋涡旋的作用、热带-极地相互作用、南大洋本身对全球变暖的响应等,尤其是有限的观测数据和较低分辨率的模式无法准确呈现快速变化的物理现象和机理.因此,南大洋的未来增暖存在很大的不确定性且可能长期持续,但近期的进展为深入认知南大洋增暖及其气候影响奠定了坚实的基础.

关键词：平流层臭氧南大洋物理现象物理过程全球变暖热带海洋南北半球低分辨率

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DRUM: Diffusion-based runoff model for probabilistic flood forecasting

arXiv

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arXiv 2024年

作者： Ou, Zhigang Nai, Congyi Pan, Baoxiang Pan, Ming Shen, Chaopeng Jiang, Peishi Liu, Xingcai Tang, Qiuhong Li, Wenqing Zheng, Yi School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China Scripps Institution of Oceanography University of California La Jolla San DiegoCA United States Civil and Environmental Engineering The Pennsylvania State University University ParkPA United States Atmospheric Climate and Earth Sciences Division Pacific Northwest National Laboratory RichlandWA United States Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China State Key Laboratory of Stimulation and Regulation of Water Cycles in River Basins China Institute of Water Resources and Hydropower Research Beijing China

Reliable flood forecasting remains a critical challenge due to persistent underestimation of peak flows and inadequate uncertainty quantification in current approaches. We present DRUM (Diffusion-based Runoff Model), a generative AI solution for probabilistic runoff prediction. DRUM builds up an iterative refinement process that generates ensemble runoff estimates from noise, guided by past meteorological conditions, present meteorological forecasts, and static catchment attributes. This framework allows learning complex hydrological behaviors without imposing explicit distributional assumptions, particularly benefiting extreme event prediction and uncertainty quantification. Using data from 531 representative basins across the contiguous United States, DRUM outperforms state-of-the-art deep learning methods in runoff forecasting regarding both deterministic and probabilistic skills, with particular advantages in extreme flow (1%) predictions. DRUM demonstrates superior flood early warning skill across all magnitudes and lead times (1–7 days), achieving F1 scores near 0.4 for extreme events under perfect forecasts and maintaining robust performance with operational forecasts, especially for longer lead times and high-magnitude floods. When applied to climate projections through the 21st century, DRUM reveals increasing flood vulnerability in 47.8–57.1% of basins across emission scenarios, with particularly elevated risks along the West Coast and Southeast regions. These advances demonstrate significant potential for improving both operational flood forecasting and long-term risk assessment in a changing climate. © 2024, CC BY-NC-SA.

关键词： Floods

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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

lMFF: Efficient and Scalable layered Materials Force Field o...

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Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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Erratum to: Vol. 35 No. 1 Publisher’s Erratum

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Chinese Journal of Oceanology and Limnology 2017年第2期35卷 466-467页

作者： Shuangyan He Mingxia He Jürgen Fischer Dongliang Yuan Peng Xu Tengfei Xu Xianping Yang Leonid Sokoletsky Xiaodao Wei Fang Shen Juhong Zou Maohua Guo Songxue Cui Wu Zhou Dalu Gao Guangzhen Jin Xianqing Lü Fuwen Qiu Wendong Fang Aijun Pan Jing Cha Shanwu Zhang Jiang Huang Tao Wang Yongzhou Cheng Xiaoyan Chen Zhaopu Liu Xiaohua Long Zhishuai Hou Haishen Wen Jifang Li Feng He Qun Liu Jinhuan Wang Biao Guan Qinglong Wang Md. Shahjahan Md. Farajul Kabir Kizar Ahmed Sumon Lipi Rani Bhowmik Harunur Rashid Shu Li Kefu Yu Jianxin Zhao Yuexing Feng Tianran Chen Shun Zhou Yichao Ren Christopher M. Pearce Shuanglin Dong Xiangli Tian Qinfeng Gao Fang Wang Liming Liu Rongbin Du Xiaoling Zhang Shichun Sun Song Feng Jianing Lin Song Sun Fang Zhang Zhipeng Zhang Xuexi Tang Haitian Tang Jingjing Song Jian Zhou Hongjun Liu Qixiang Wang Kuimei Qian Xia Liu Yuwei Chen Chengjun Sun Fenghua Jiang Wei Gao Xiaoyun Li Yanzhen Yu Xiaofei Yin Yong Wang Haibing Ding Zhongmin Sun Yongqiang Wang Pengcheng Yan Hui Guo Jianting Yao Jiro Tanaka Hiroshi Kawai Na Song Muyan Chen Tianxiang Gao Takashi Yanagimoto Xia Lu Sheng Luan Jie Kong Longyang Hu Yong Mao Shengping Zhong Yan Liu Weihong Zhao Caiyan Li Hui Miao Ocean Remote Sensing Institute Ocean University of China Qingdao China Institute for Space Sciences Freie Universität Berlin Berlin Germany Ocean College Zhejiang University Hangzhou China Key Laboratory of Ocean Circulation and Waves (KLOCAW) Institute of Oceanology Chinese Academy of Sciences Qingdao China Qingdao Collaborative Innovation Center of Marine Science and Technology Qingdao China University of Chinese Academy of Sciences Beijing China First Institute of Oceanography State Oceanic Administration Qingdao China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai China National Satellite Ocean Application Service Beijing China Key Laboratory of Space Ocean Remote Sensing and Application SOA Beijing China Key Laboratory of Physical Oceanography (Ocean University of China) Ministry of Education Qingdao China Key Lab of Marine Science and Numerical Modeling First Institute of Oceanography SOA Qingdao China Third Institute of Oceanography State Oceanic Administration Xiamen China State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China Jiangsu Provincial Key Laboratory of Marine Biology College of Resources and Environmental Sciences Nanjing Agricultural University Nanjing China Fisheries College Ocean University of China Qingdao China Key Laboratory of Mariculture Ministry of Education Ocean University of China Qingdao China Department of Fisheries Management Bangladesh Agricultural University Mymensingh Bangladesh Sado Marine Biological Station Faculty of Science Niigata University Sado Niigata Japan Department of Aquatic Ecology and Water Quality Management Wageningen University Wageningen University and Research Centre Amsterdam The Netherlands Science and Math Program Asian University for Women Chittagong Bangladesh Key laboratory of Marginal Sea Geology South China Sea Institute of Oceanology Chinese Academy of Sciences

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Paddy rice methane emissions across Monsoon Asia

引用

Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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