THU CIESM model output prepared for CMIP6 CMIP historical
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下载链接:
http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.CMIP.THU.CIESM.historical
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资源简介:
Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.CMIP.THU.CIESM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
The Community Integrated Earth System Model climate model, released in 2017, includes the following components:
aerosol: MAM4, atmos: CIESM-AM (FV/FD; 288 x 192 longitude/latitude; 30 levels; top level 2.255 hPa), atmosChem: trop_mam4, land: CIESM-LM (modified CLM4.5), ocean: CIESM-OM (FD, SCCGrid Displaced Pole; 720 x 560 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.
The model was run by the Department of Earth System Science, Tsinghua University, Beijing 100084, China (THU) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.
Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).
CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).
The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
耦合模式比较计划第六阶段(Coupled Model Intercomparison Project Phase 6,CMIP6)数据集:本数据集涵盖所有以`CMIP6.CMIP.THU.CIESM.historical`标识发布的数据集,其数据命名遵循完整数据参考语法(Data Reference Syntax)模板:`mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version`。
清华大学地球系统科学系于2017年发布的社区集成地球系统模式(Community Integrated Earth System Model,CIESM)包含以下组分:气溶胶模块采用MAM4方案,大气模块为CIESM-AM(有限差分/有限体积(FV/FD)格式;288×192经纬度格点;30层垂直结构;顶层气压为2.255 hPa),大气化学模块为trop_mam4方案,陆面模块为CIESM-LM(基于CLM4.5改进),海洋模块为CIESM-OM(有限差分格式,采用SCCGrid位移极坐标;720×560经纬度格点;46层垂直结构;顶层网格单元水深范围0~6 m),海冰模块为CICE4方案。
该模式由中国北京100084的清华大学地球系统科学系(THU)以原生标称分辨率运行:气溶胶、大气、大气化学及陆面模块的空间分辨率为100 km,海洋与海冰模块的空间分辨率为50 km。
本数据集作为国际协调的耦合模式比较计划第六阶段(CMIP6;详见《Geoscientific Model Development》特刊:http://www.geosci-model-dev.net/special_issue590.html)的组成部分生成。该模拟数据为解答基础科学问题的气候研究提供核心支撑,同时可作为政府间气候变化专门委员会第六次评估报告(Intergovernmental Panel on Climate Change Sixth Assessment Report,IPCC-AR6)撰写者的参考资源。
CMIP6由世界气候研究计划(World Climate Research Programme,WCRP)下属的耦合模式工作组(Working Group on Coupled Modelling,WGCM)协调推进。第六阶段继承了此前由气候模式诊断与比较计划(Program for Climate Model Diagnosis and Intercomparison,PCMDI)主导的前序阶段成果,依托地球系统网格联盟(Earth System Grid Federation,ESGF)、环境数据分析中心(Centre for Environmental Data Analysis,CEDA)及众多相关配套活动完成实施。原始数据存储于分布式数据节点集合中并进行部分副本备份,IPCC所依赖的绝大多数数据将由德国气候计算中心(German Climate Computing Center,DKRZ)托管的IPCC数据分发中心(IPCC Data Distribution Centre,IPCC DDC)进行长期归档保存。
该项目涵盖全球约120个全球气候模式与约45个科研机构及组织的模拟试验。——项目官网:https://pcmdi.llnl.gov/CMIP6。
创建时间:
2020-03-30
搜集汇总
数据集介绍

背景与挑战
背景概述
该数据集是清华大学地球系统科学系为CMIP6历史实验提供的CIESM气候模型输出数据,包含大气、海洋、陆面等多个模块,其中大气模块分辨率为100公里,海洋和海冰模块为50公里。数据用于支持全球气候研究,特别是IPCC第六次评估报告,遵循CMIP6标准并采用CC BY 4.0许可。
以上内容由遇见数据集搜集并总结生成



