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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 10-21     DOI: 10.6046/zrzyyg.2022412
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Global sharing system of Earth simulation data in the Coupled Model Intercomparison Project
LIU Yufu1(), BAI Yuqi1,2()
1. Department of Earth System Science, Ministry of Education Ecological Field Station for East Asian Migratory Birds and Their Habitatses, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
2. Tsinghua Urban Institute, Tsinghua University, Beijing 100084, China
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Abstract  

Remote sensing-based Earth observation and Earth system numerical simulation serve as two significant technical means in revealing the Earth’s environmental changes and predicting its future states. Hence, they assist in enhancing the capacity of human society to mitigate and adapt to global change and in ensuring the sustainable development of the natural environment and human society. The Coupled Model Intercomparison Project (CMIP) is a large-scale international collaboration project in the field of Earth system numerical simulation, aiming to coordinate various countries to complete the simulations of the Earth’s historical environment and the predictions of its future states. The Earth simulation data generated in the CMIP directly support the global climate change assessments of the Intergovernmental Panel on Climate Change (IPCC), United Nations, providing a solid scientific basis for global climate negotiations and governance. The CMIP Phase 6 (CMIP6) has generated up to 30 petabytes (PB) of Earth simulation data. The management and sharing of these data are achieved through the Earth System Grid Federation (ESGF). This study elucidates the CMIP organizational scheme, the ESGF system architecture, and the sharing and interoperability progress of Earth simulation data. It can provide a reference for planning, designing, and operating large-scale networks for sharing remote sensing science data.

Keywords Coupled Model Intercomparison Project (CMIP)      Earth System Grid Federation (ESGF)      climate model data      data sharing     
ZTFLH:  TP79  
Issue Date: 31 December 2025
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Yufu LIU
Yuqi BAI
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Yufu LIU,Yuqi BAI. Global sharing system of Earth simulation data in the Coupled Model Intercomparison Project[J]. Remote Sensing for Natural Resources, 2025, 37(6): 10-21.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022412     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/10
Fig.1  Levels of CMIP6 experiments
Fig.2  Data Stream of CMIP5/6
Fig.3  Multi-node Network of ESGF
Fig.4  Services Included in ESGF Node
Fig.5  ESGF data processing and publishing process
CMIP6全局属性 属性缩写 示例 是否需要
活动ID activity_id “CMIP”,“PMIP” 总是
分支方法 branch_method “standard”,“noparent” 当父元素存在时
子模拟的分支时间 branch_time_in_child 365.0D0,0.0D0 当父元素存在时
父模拟的分支时间 branch_time_in_parent 3 650.0D0 当父元素存在时
注释 comment 从不
联系人 contact 从不
约定版本 Conventions “CF-1.7CMIP-6.2” 总是
创建日期 creation_date 总是
数据规范版本 data_specs_version 01.00.00,01.00.01,…01.00.xx 总是
实验 experiment “pre-industrialcontrol” 总是
实验ID experiment_id “historical”,“abrupt4xCO2” 总是
外部变量 external_variables “areacella”,“areacello” 合适时
强迫指数 forcing_index 1,2,82,323 总是
频率 frequency “mon”,“day”,“6 h” 总是
详细信息网址 further_info_url 总是
网格 grid 总是
网格标签 grid_label “gn”,“gr”,“gr1”,“gr2”,“grz”,“gm” 总是
历史记录 history 从不
初始化指数 initialization_index 1 总是
机构 institution “Meteorological Research Institute” 总是
机构ID institution_id “IPSL” 总是
许可协议 license 总是
MIP阶段 mip_era “CMIP5”,“CMIP6” 总是
标称分辨率 nominal_resolution “50 km”,“100 km”,“250 km”,“1x1degree” 总是
父活动ID parent_activity_id “CMIP”,ScenarioMIP 当父元素存在时
父实验ID parent_experiment_id “piControl” 当父元素存在时
父模拟MIP阶段 parent_mip_era “CMIP5”,“CMIP6” 当父元素存在时
父模拟源ID parent_source_id “CanCM4” 当父元素存在时
父模拟时间单位 parent_time_units “dayssince1850-1-1”,“dayssince1000-1-1(noleap)” 当父元素存在时
父模拟变体标签 parent_variant_label “r1i1p1f1”,“r1i2p223f3”,“noparent” 当父元素存在时
物理指数 physics_index 3 总是
产品类型 product “model-output” 总是
实现指数 realization_index 5 总是
领域 realm “atmos”,“ocean” 总是
参考文献 references 从不
源描述 source 总是
源ID source_id “GFDL-CM2-1” 总是
源类型 source_type “AGCM”,“OGCM”,“AOGCM”,“ISM”, AOGCMISM” 总是
子实验 sub_experiment 总是
子实验ID sub_experiment_id “s1960”,“s1965”,“none” 总是
表格ID table_id “Amon”,“Oday” 总是
标题 title 从不
追踪ID tracking_id 总是
变量ID variable_id “tas”,“pr”,“ua” 总是
变体信息 variant_info “forcing: black carbon aerosol only” 从不,但是推荐
变体标签 variant_label “r1i1p1f1”,“f1i2p223f3” 总是
Tab.1  Global attributes of CMIP6 data
Fig.6  Workflow for citing CMIP6 data via DOI in ESGF
元数据 字段 名称 示例




必需
字段
id cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1.v20110323|pcmdi9.llnl.gov
title project=CMIP5/IPCCFifthAssessmentReport,model=InstituteforNumericalMathematics,experiment=1percentperyearCO2,time_frequency=day,modelingrealm=atmos,ensemble=r1i1p1,version=20110323
type Dataset
project CMIP5
dataset_id cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1.v20110323|pcmdi9.llnl.gov
index_node pcmdi9.llnl.gov
data_node pcmdi11.llnl.gov
可选
版本
和镜
像字
version 20110323
master_id cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1
replica FALSE
latest TRUE
shard localhost: 8982
checksum 7fcd959a4bb57e4079c8e65a7a5d0499
checksum_type SHA256
可选
通用
字段
description inmcm4modeloutputpreparedforCMIP51percentperyearCO2
url http://pcmdi9.llnl.gov/thredds/esgcet/1/cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1.v20110323.xml#cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1
access THREDDS,LAS
timestamp 2012-01-13T01: 34: 15Z
schema cmip5
format NetCDF
可选
下载
字段
size 2874053764
number_of_files 2
number_of_aggregations 2






可选
地球
科学
通用
字段
variable ta
variable_long_name AirTemperature
variable_units K
cf_standard_name air_temperature
可选
时空
搜索
字段
datetime_start 2090-01-01T12: 00: 00Z
datetime_stop 2229-12-31T12: 00: 00Z
north_degrees 89.25
east_degrees 358
south_degrees -89.25
west_degrees 0.0
height_bottom 100000.0
height_top 1000.0
height_units Pa






必需
DRS
字段
model INM-CM4
experiment 1pctCO2
product output1
realm atmos
time_frequency day
tracking_id bbf37d89-fd8a-4aa6-b42b-eb4adfe6504b
可选
元数
据字
ensemble r1i1p1
experiment_family Historical
cmor_table Amon
dataset_id_template cmip5.%(product)s.%(valid_institute)s.%(model)s.%(experiment)s.%(time_frequency)s.%(realm)s.%(cmor_table)s.%(ensemble)s
drs_id cmip5.output1.INM.inmcm4.1pctCO2.day.atmos.day.r1i1p1
forcing Nat
Tab.2  ESGF Metadata
Fig.7  Design of ESGF dashboard
Fig.8  Average data download speed of CMIP6 data nodes in different countries
[1] 周天军, 邹立维, 陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述[J]. 气候变化研究进展, 2019, 15(5):445-456.
[1] Zhou T J, Zou L W, Chen X L. Commentary on the coupled model intercomparison project phase 6 (CMIP6)[J]. Climate Change Research, 2019, 15(5):445-456.
[2] Eyring V, Bony S, Meehl G A, et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development, 2016, 9(5):1937-1958.
doi: 10.5194/gmd-9-1937-2016 url: https://gmd.copernicus.org/articles/9/1937/2016/
[3] 赵宗慈, 罗勇, 黄建斌. CMIP6的设计[J]. 气候变化研究进展, 2016, 12(3):258-260.
[3] Zhao Z C, Luo Y, Huang J B. Design of CMIP6[J]. Climate Change Research, 2016, 12(3):258-260.
[4] 马强, 颜京辉, 魏敏, 等. 北京气候中心CMIP6数据共享平台及应用[J]. 应用气象学报, 2022, 33(5):617-627.
[4] Ma Q, Yan J H, Wei M, et al. Implementation and application of BCC CMIP6 experimental data sharing platform[J]. Journal of Applied Meteorological Science, 2022, 33(5):617-627.
[5] Balaji V, Taylor K E, Juckes M, et al. Requirements for a global data infrastructure in support of CMIP6[J]. Geoscientific Model Development, 2018, 11(9):3659-3680.
doi: 10.5194/gmd-11-3659-2018 url: https://gmd.copernicus.org/articles/11/3659/2018/
[6] Cinquini L, Crichton D, Mattmann C, et al. The Earth System Grid Federation:An open infrastructure for access to distributed geospatial data[J]. Future Generation Computer Systems, 2014,36:400-417.
[7] Williams D N, Taylor K E, Cinquini L, et al. The Earth System Grid Federation:Software Framework Supporting CMIP5 Data Analysis and Dissemination[J]. CLIVAR Exchanges, 2011, 16(2):40-42.
[8] Petrie R, Denvil S, Ames S, et al. Coordinating an operational data distribution network for CMIP6 data[J]. Geoscientific Model Development, 2021, 14(1):629-644.
doi: 10.5194/gmd-14-629-2021
[9] Climate Europe. European Earth System Modelling for Climate Services[EB/OL]. https://www.climateurope.eu/european-earth-system-modelling-for-climate-services/ (Accessed on 24 May 2024).
url: https://www.climateurope.eu/european-earth-system-modelling-for-climate-services/
[10] Earth System Grid Federation, Federation Design. ESGF GitHub Documentation[EB/OL].https://esgf.github.io/federation-design.html.
url: https://esgf.github.io/federation-design.html
[11] Balaji V. CPMIP:Measurements of real computational performance of earth system models in CMIP[J]. Future Generation Computer Systems, 2013, 29 (7),1889-1896.
[12] Juckes M, Taylor K E, Durack P J, et al. The CMIP6 data request (DREQ,version 01.00.31)[J]. Geoscientific Model Development, 2020, 13(1):201-224.
doi: 10.5194/gmd-13-201-2020 url: https://gmd.copernicus.org/articles/13/201/2020/
[13] Stockhause M, Lautenschlager M. CMIP6 data citation of evolving data[J]. Data Science Journal, 2017,16:30.
[14] Pascoe C, Lawrence B N, Guilyardi E, et al. Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6)[J]. Geoscientific Model Development, 2020, 13(5):2149-2167.
doi: 10.5194/gmd-13-2149-2020 url: https://gmd.copernicus.org/articles/13/2149/2020/
[15] Stall S, Yarmey L, Cutcher-Gershenfeld J, et al. Make scientific data FAIR[J]. Nature, 2019, 570(7759):27-29.
doi: 10.1038/d41586-019-01720-7
[16] Stockhause M, Lautenschlager M. Data citation in climate sciences:Improvements in CMIP6 compared to CMIP5[J/OL]. AGU Fall Meeting Abstracts, 2017, 42.http://adsabs.harvard.edu/abs/2017AGUFMIN42C.05S.
url: http://adsabs.harvard.edu/abs/2017AGUFMIN42C.05S
[17] Durack P J, Taylor K E, Eyring V, et al. A CMIP6-based multi-model outlook on future steric dynamic sea level change[J]. Geoscientific Model Development,14:629-667.
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