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Can Industrial Structure Upgrading Restrain Industrial Land Expansion?Evidence from china

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chinese geographical Science 2024年第3期34卷 504-518页

作者： chen Wei li Qiao ZHANG Sun zhou Xue College of Economics and Management Northwest A&F UniversityYangling Shaanxi 712100China institute of geographic sciences&{3. resources research Chinese Academy of SciencesBeijing 100101China

china has made great achievements in industrial development and is transforming into a powerful manufacturing ***,the industrial land scale is also ***,whether industrial structure upgrading achieves the purpose of restraining industrial land expansion remains *** calculating the industrial land structure index(ILSI)and industrial land expansion scale(ILES),this study analyzed their temporal and spatial distribution characteristics at both regional and city levels from 2007to 2020 in *** show that industrial land expansion presents a different trend in the four regions,the ILES in the eastern region is the largest,and the speed of industrial land expansion has declined since 2013.but it has gradually increased since *** ILSI of the eastern and central regions is higher than that of the western and northeastern ***,a spatial Durbin model(SDM)has been established to estimate the spatial effect of industrial structure upgrading on industrial land expansion from 2007 ***,industrial structure upgrading has not slowed industrial land *** eastern and western regions require a greater amount of industrial land while upgrading the industrial *** improvement of the infrastructure level and international trade level has promoted industrial land expansion.

关键词： industrial development industrial structure upgrading industrial land expansion regional differences china

Multi-omic Analyses Shed light on The Genetic Control of High-altitude Adaptation in Sheep

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Genomics, Proteomics & Bioinformatics 2024年第2期22卷 43-56页

作者： Chao li Bingchun chen Suo Langda Peng Pu Xiaojia Zhu Shiwei {3. Peter Kalds Ke Zhang Meenu Bhati Alexander Leonard Shuhong Huang Ran li Awang Cuoji Xiran Wang haolin Zhu Yujiang Wu Renqin Cuomu Ba Gui Ming li Yutao Wang Yan li Wenwen Fang Ting Jia Tianchun Pu Xiangyu Pan Yudong cai Chong He liming Wang Yu Jiang Jian-lin Han Yulin chen Ping {3. Hubert Pausch Xiaolong Wang International Joint Agriculture research Center for Animal Bio-Breeding Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal GeneticsBreeding and Reproduction of Shaanxi ProvinceCollege of Animal Science and TechnologyNorthwest A&F UniversityYangling 712100China Animal Genomics ETH ZürichZürich 8092Switzerland institute of Animal Science Tibet Academy of Agricultural and Animal Husbandry SciencesLhasa 850009China School of Biological and Pharmaceutical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China College of life sciences Shaanxi Normal UniversityXi’an 710119China Zoology and Evolutionary Biology Department of BiologyUniversity of Konstanz78457 KonstanzGermany College of life and geographic sciences Kashi UniversityKashi 844000China Beijing Key Laboratory of Captive Wildlife Technologies Beijing ZooBeijing 100044China Department of Medical research Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs/Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceCollege of Information EngineeringNorthwest A&F UniversityYangling 712100China institute of Animal Husbandry and Veterinary Medicine Xinjiang Academy of Agricultural and Reclamation SciencesShihezi 832000China State Key Laboratory of Sheep Genetic Improvement and Healthy Production Xinjiang Academy of Agricultural and Reclamation SciencesShihezi 832000China CAAS-ILRI Joint Laboratory on livestock and Forage Genetic resources Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing 100193China livestock Genetics Program International Livestock Research InstituteNairobi 00100Kenya

Sheep were domesticated in the Fertile Crescent and then spread globally,where they have been encountering various environmental *** Tibetan sheep has adapted to high altitudes on the Qinghai-Tibet Plateau over the past 3.00 *** explore genomic variants associated with high-altitude adaptation in Tibetan sheep,we analyzed Illumina short-reads of 994 whole genomes representing∼60 sheep breeds/populations at varied altitudes,PacBio High fidelity(HiFi)reads of 13.breeds,and 96 transcriptomes from 12 sheep *** testing between the inhabited altitudes and 3.,298,967 variants was conducted to investigate the genetic architecture of altitude *** accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associatedβ-globin locus and to validate the presence of two haplotypes A and B among 13.sheep *** haplotype A carried two homologous gene clusters:(1)HBE1,HBE2,HBB-like,and HBBC,and(2)HBE1-like,HBE2-like,HBB-like,and HBB;while the haplotype B lacked the first *** high-altitude sheep showed highly frequent or nearly fixed haplotype A,while the low-altitude sheep dominated by haplotype *** further demonstrated that sheep with haplotype A had an increased hemoglobin–O2 affinity compared with those carrying haplotype *** highly associated genomic region contained the EGLN1 gene which showed varied expression between high-altitude and low-altitude *** results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.

关键词： Environmental adaptation High altitude Hypoxia Selection signature Sheep

Genome assembly of autotetraploid Actinidia arguta highlights adaptive evolution and enables dissection of important economic traits

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Plant Communications 2024年第6期5卷 173-192页

作者： Xue-Mei lu Xiao-Fen Yu Guo-Qiang li Ming-hao Qu Huan Wang Chuang liu Yu-Ping Man Xiao-Han Jiang Mu-Zi li Jian Wang Qi-Qi chen Rui Lei cheng-cheng Zhao Yun-Qiu zhou Zheng-Wang Jiang Zuo-zhou li Shang Zheng Chang Dong Bai-lin Wang Yan-Xiang Sun Hui-Qin Zhang Jie-Wei li Quan-Hui Mo Ying Zhang Xin Lou Hai-Xu Peng Ya-Ting Yi He-Xin Wang Xiu-Jun Zhang Yi-Bo Wang Dan Wang li li Qiong Zhang Wen-Xia Wang Yongbo liu Lei Gao Jin-Hu Wu Yan-Chang Wang CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture Wuhan Botanical GardenChinese Academy of SciencesWuhanHubeiChina Key Laboratory of Western china’s Environmental Systems(Ministry of Education) College of Earth and Environmental SciencesLanzhou UniversityLanzhouGansuChina College of life sciences University of Chinese Academy of SciencesBeijingChina Wuhan Frasergen Bioinformatics Co. LtdWuhanHubeiChina institute of Soil and Fertilizer Anhui Academy of Agricultural SciencesHefeiChina College of Agricultural sciences Xichang UniversityXichangSichuanChina Department of Horticulture Heilongjiang Academy of Agricultural SciencesHarbinHeilongjiangChina College of life sciences Langfang Normal UniversityLangfangHebeiChina institute of Horticulture Zhejiang Academy of Agricultural SciencesHangzhouZhejiangChina Guangxi institute of Botany Chinese Academy of SciencesGuilinGuangxiChina Xi’an Botanical Garden of Shaanxi Province Institute of Botany of Shaanxi ProvinceXi’anShaanxiChina institute of Modern Agricultural research Dalian UniversityDalianLiaoningChina Bioinformatics Center College of Plant Science and TechnologyBeijing University of AgricultureBeijingChina College of Agriculture Eastern Liaoning UniversityDandongLiaoningChina Dalian institute of Chemical Physics Chinese Academy of SciencesDalianLiaoningChina State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment Chinese Research Academy of Environmental SciencesBeijingChina Hubei Hongshan Laboratory WuhanHubeiChina The New Zealand institute for Plant and Food research limited AucklandNew Zealand

Actinidia arguta,the most widely distributed Actinidia species and the second cultivated species in the genus,can be distinguished from the currently cultivated Actinidia chinensis on the basis of its small and smooth fruit,rapid softening,and excellent cold *** evolution of tetraploid Actinidia species and the genetic basis of their important agronomic traits are still ***,we generated a chromosome-scale genome assembly of an autotetraploid male *** *** genome assembly was 2.77 Gb in length with a contig N50 of 9.97 Mb and was anchored onto 116 *** and clustering of 101 geographically representative accessions showed that they could be divided into two geographic groups,Southern and Northern,which first diverged 12.9 million years *** underwent two prominent expansions and one demographic bottleneck from the midPleistocene climate transition to the late *** genomics studies using paleoclimate data enabled us to discern the evolution of the species’adaptation to different historical *** genes(AaCEL1,AaPME1,and AaDOF1)related to flesh softening were identified by multi-omics analysis,and their ability to accelerate flesh softening was verified through transient expression assays.A set of genes that characteristically regulate sexual dimorphism located on the sex chromosome(Chr3.or autosomal chromosomes showed biased expression during stamen or carpel *** chromosome-level assembly of the autotetraploid *** genome and the genes related to important agronomic traits will facilitate future functional genomics research and improvement of ***.

关键词： kiwiberry dioecism adaptive evolution paleoclimate GEA rapid softening

Study on snowmelt runoff simulation in the Kaidu River basin

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Science china Earth sciences 2007年第S1期 26-35页

作者： ZHANG YiChi1, li Baolin1, BAO AnMing2, zhou {3.gHu1, {3. Xi2 & ZHANG XueRen3.1 institute of geographic sciences and natural resources research, chinese academy of sciences, Beijing 100101,{13. 2 Xinjiang institute of Ecology and Geography, chinese academy of sciences,ürümqi 83.011,{13. 3.Xinjiang Tarim River Basin Management Bureau, Korla 841000,{13.

Alpine snowmelt is an important generation mode for runoff in the source region of the Tarim River basin, which covers four subbasins characterized by large area, sparse gauge stations, mixed runoff supplied by snowmelt and rainfall, and remarkably spatially heterogeneous precipitation. Taking the Kaidu River basin as a research area, this study analyzes the influence of these characteristics on the variables and parameters of the Snow Runoff Model and discusses the corresponding determination strategy to improve the accuracy of snowmelt simulation and forecast. The results show that: (i) The temperature controls the overall tendency of simulated runoff and is dominant to simulation accuracy, as the measured daily mean temperature cannot represent the average level of the same elevation in the basin and that directly inputting it to model leads to inaccurate simulations. Based on the analysis of remote sensing snow maps and simulation results, it is reasonable to approximate the mean temperature with 0.5 time daily maximum temperature. (ii) For the conflict between the limited gauge sta-tion and remarkably spatial heterogeneity of rainfall, it is not realistic to compute rainfall for each elevation zone. After the measured rainfall is multiplied by a proper coefficient and adjusted with runoff coefficient for rainfall, the measured rainfall data can satisfy the model demands. (iii) Adjusting time lag according to the variation of snowmelt and rainfall position can improve the simulation precision of the flood peak process. (iv) Along with temperature, the rainfall increases but cannot be completely monitored by limited gauge stations, which results in precision deterioration.

关键词： snowmelt runoff model (SRM) Kaidu River basin runoff simulation runoff coefficient for rainfall time lag

Study of proton resonances in ^(18)Ne via resonant elastic scattering of ^(17)F+p and its astrophysical implication

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Science china(Physics,Mechanics & Astronomy) 2011年第S1期54卷 32-36页

作者： HE JianJun1, HU Jun1,2, XU ShiWei1, chen ZhiQiang1, ZHANG XueYing1, WANG JianSong1, YU XiangQing1, ZHANG liYong1,2, li Long1,2, YANG YanYun1,2, MA Peng1,2, ZHANG XueHeng1, HU ZhengGuo1, GUO {3.1, XU Xing1,2, YUAN XiaoHua1, lu Wan1,2, YU YuHong1, ZANG YongDong1,2, TANG ShuWen1,2, YE RuiPing1,2, chen JinDa1,2, JIN Shilun1,2, DU chengMing1,2, WANG ShiTao1,2, MA JunBing1,2, liU LongXiang1,2, BAI Zhen1,2, LEI XiangGuo1, SUN ZhiYu1, ZHANG YuHu1, zhou XiaoHong1, XU HuShan1, SU Jun3. li ErTao3. WANG HongWei4, TIAN WenDong4 & li XiangQing5 1 institute of Modern Physics (IMP),{13., Lanzhou 73.000, china 2 Graduate University of{13., Beijing 100049, china 3.15} institute of Atomic Energy (CIAE), Beijing 102413. china 4 Shanghai institute of Applied Physics (SINAP),{13., Shanghai 201800, china 5 School of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, china institute of Modern Physics (IMP) Chinese Academy of Sciences Lanzhou China Graduate University of chinese academy of sciences Beijing China china institute of Atomic Energy (CIAE) Beijing China Shanghai institute of Applied Physics (SINAP) Chinese Academy of Sciences Shanghai China School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China

The proton resonant properties in 18Ne, which determine the reaction rate of the key stellar 14O(α,p)17F reaction, have been studied by using a technique of proton resonant elastic scattering of 17F+p. A 4.22 MeV/nucleon 17F radioactive ion (RI) beam was produced via a projectile-fragmentation reaction, and separated by a Radioactive Ion Beam line in Lanzhou (RIBLL). By bombarding a thick (CH2)n target, the energy spectra of the recoiled protons were measured by two ΔE-E silicon telescopes at the center-of-mass scattering angles of θc.m.≈175°±5°, θc.m.≈152°±8°, respectively. Several proton resonances in 18Ne were ob served clearly. A further R-matrix analysis of the experimental data is under way to determine the resonant parameters. The present work reports the preliminary results briefly.

关键词： resonant reaction reaction rate explosive burning accreting binary systems

Distribution and Forming Model of Fluvial Terrace in the Huangshui Catchment and its Tectonic Indication

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Acta Geologica Sinica(English Edition) 2010年第2期84卷 415-423页

作者： WANG Xianyan lu Huayu Jef VANDENBERGHE chen zhongyan {3. Langpin geographic and Oceanographic sciences Nanjing University Nanjing 210093 China institute of Earth sciences VU University Amsterdam De Bpoelelaan 1085 1081 HVAmsterdam The Netherlands Department of Geography Qinghai Normal University Xining 830008 China

The Huang Shui River, a main tributary of the Yellow River, crosses a series of tectonically subsided and uplifted areas that show different patterns of terrace formation. The distribution of fluvial terrace of the Huang Shui River is studied through topographic and sedimentologic terrace mapping. Three terraces in the Haiyan Basin, four terraces in the Huangyuan Basin, 19 terraces in the Xi＇ning Basin （the four high terraces may belong to another river）, nine terraces in the Ping＇an Basin, five terraces in the Ledu Basin and 12 terraces in the Minhe Basin are recognized. Sedimentology research shows that the geomorphologic and sedimentological pattern of the Huang Shui River, which is located at the margin of Tibet, are different from that of the rivers at other regions. The formation process of the terrace is more complicated at the Huang Shui catchment： both accumulation terrace and erosion terrace were formed in each basin and accumulation terraces were developed in some basins when erosion terraces were formed in other basins, indicating fluvial aggradation may occur in some basins simultaneously with river incision in other basins. A conceptual model of the formation process of these two kinds of fluvial terraces at Huang Shui catchment is brought forward in this paper. First, the equilibrium state of the river is broken because of climatic change and/or tectonic movement, and the river incises in all basins in the whole catchment until reaching a new equilibrium state. Then, the downstream basin subsides quickly and the equilibrium state is broken again, and the river incises at upstream basins while the river accumulates at the subsidence basin quickly until approaching a new equilibrium state again. Finally, the river incises in the whole catchment because of climatic change and/or tectonic movement and the accumulation terrace is formed at the subsidence basin while the erosion terrace is formed at other basins. The existence of the accumulation terrace im

关键词： Huangshui catchment fluvial terrace tectonic subsidence geomorphologic evolution late Cenozoic

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min chen chenying liu Jin Zhang Long Yu Fei Wang Yong Xie Jun li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang li Wang Lei luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan li Chaolei Zheng Yang zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi lizhe Wang Aerospace Information research institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA institute of Remote Sensing and geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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地球观测知识枢纽:内涵、关键技术与展望

遥感学报 2024年第6期28卷 1375-1394页

作者：赵利民苗晨邢进李国庆侯宇葵刘闯李家国陈兴峰刘军杨健周翔顾行发中国科学院空天信息创新研究院北京100094 遥感卫星应用国家工程研究中心北京100094 国家航天局航天遥感论证中心北京100094 国家遥感中心北京100036 国防科工局重大专项工程中心北京100101 国家对地观测科学数据中心北京100094 中国空间技术研究院钱学森空间技术实验室北京100094 中国科学院地理科学与资源研究所北京100101

智能化地共享和复用地球观测应用实践所形成的知识,有序促进应用要素的高通量流动,是激活和释放地球观测系统效能的关键。地球观测知识枢纽EOKH(Earth Observation Knowledge Hub)将分散的应用要素有机耦合,形成专题知识集合的协同设计... 详细信息

智能化地共享和复用地球观测应用实践所形成的知识,有序促进应用要素的高通量流动,是激活和释放地球观测系统效能的关键。地球观测知识枢纽EOKH(Earth Observation Knowledge Hub)将分散的应用要素有机耦合,形成专题知识集合的协同设计与吞吐能力,是地球观测成果治理与智能服务的研究前沿。针对地球观测组织GEO(Group on Earth Observations)发展GEO知识枢纽GKH(GEO Knowledge Hub)得到的启示,结合地球观测知识本体研究最新进展,系统解析了EOKH的内涵与特征,梳理了发展EOKH的技术需求与挑战,分析存在的关键问题及其应对策略。在此基础上,提出了EOKH系统架构原型,结合地球观测知识包转移复用和知识推理两个EOKH典型应用场景进行了原理实现与概念验证。最后,就如何促进地球观测知识“人—机—物”要素的协同共生,提出了中国发展EOKH的相关认识与思考。探索“人在回路”的高通量知识协同生产与转移技术,以及“知识驱动”的成果复用技术,是提升地球观测隐性知识可解释性、促进EOKH服务质量与发展力的必要举措。

关键词：遥感知识枢纽地球观测本体知识包知识复用人—机—物人在回路

Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

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Big Earth Data 2024年第2期8卷 189-230页

作者： Alexander Mahura Alexander Baklanov Risto Makkonen Michael Boy Tuukka Petäjä Hanna KLappalainen Roman Nuterman Veli-Matti Kerminen Stephen R.Arnold Markus Jochum Anatoly Shvidenko Igor Esau Mikhail Sofiev Andreas Stohl Tuula Aalto Jianhui Bai Chuchu chen Yafang cheng Oxana Drofa Mei Huang Leena Järvi Harri Kokkola Rostislav Kouznetsov Tingting li Piero Malguzzi Sarah Monks Mads Bruun Poulsen Steffen M.Noe Yuliia Palamarchuk Benjamin Foreback Petri Clusiu Till Andreas Soya Rasmussen Jun She Jens Havskov Sørensen Dominick Spracklen Hang Su Juha Tonttila Siwen Wang Jiandong Wang Tobias Wolf-Grosse Yongqiang Yu Qing Zhang Wei Zhang Wen Zhang Xunhua Zheng Siqi li Yong li Putian zhou Markku Kulmala Faculty of Science Institute for Atmospheric and Earth System Research(INAR)/PhysicsUniversity of Helsinki(UHEL)HelsinkiFinland Science and Innovation Department World Meteorological Organization(WMO)GenevaSwitzerland Niels Bohr institute University of CopenhagenCopenhagenDenmark Climate System research Finnish Meteorological Institute(CSR-FMI)HelsinkiFinland School of Engineering sciences Lappeenranta University of TechnologyLappeenrantaFinland institute for Climate and Atmospheric Science School of Earth and EnvironmentUniversity of LeedsLeedsUK Biodiversity and natural resources Program International Institute for Applied Systems Analysis(IIASA)LaxenburgAustria Department of Physics and Technology UiT-The Arctic University of NorwayTromsoNorway Nansen Environmental and Remote Sensing Center(NERSC) BergenNorway Atmospheric Composition Finnish Meteorological Institute(AC-FMI)HelsinkiFinland Norwegian institute for Air research(NIlu) KjellerNorway LAGEO Institute of Atmospheric PhysicsChinese Academy of Sciences(IAP-CAS)BeijingChina Max Planck institute for Chemistry(MPI-Chem) MainzGermany institute of Atmospheric sciences and Climate National Research Council of Italy(CNR-15SAC)BolognaItaly institute of geographic sciences and {3. resources research(IGSNRR) Chinese Academy of Sciences(CAS)BeijingChina Faculty of Science Helsinki Institute of Sustainability Science(HELSUS)University of Helsinki(UHEL)HelsinkiFinland Atmospheric research Centre of Eastern Finland Finnish Meteorological Institute(ARCEF-FMI)KuopioFinland institute of Atmospheric Physics Chinese Academy of Sciences(IAP-CAS)BeijingChina Department of Plant Physiology Estonian University of Life Sciences(EULS)Institute of Agricultural and Environmental Sciences(IAES)TartuEstonia Danish Meteorological institute(DMI) CopenhagenDenmark State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry LAPC) Institute of Atmospheric Physics(IAP)Chinese Academy of Sciences(CAS)BeijingChina Uni

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX research *** PEEX MP has more than 3. models and is directed towards seamless envir-onmental *** main focus area is the Arctic-boreal regions and *** models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and *** paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical *** employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are *** virtual research platforms(PEEXView,Virtual research Environment,Web-based Atlas)for handling PEEX modelling and observational results are *** over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,*** population,envir-onment and climate in the PEEX *** work was also one of the last projects of *** Zilitinkevich,who passed away on 15 February *** the finalization took time,the paper was actually submitted in 2023.and we could not argue that the final paper text was agreed with him.

关键词： Multi-scale and-processes modelling concept seamless coupling high-performance computing data infrastructure virtual research platforms