设为首页
加入收藏
Implementation and Application of BCC CMIP6 Experimental Data Sharing Platform
-
摘要: 为保障北京气候中心（Beijing Climate Center，BCC）气候模式在第6次耦合模式比较计划（Coupled Model Intercomparison Project Phase 6，CMIP6）中的大量试验数据产品面向国内外实现共享，建立了试验数据共享平台。由于模式试验数据具有数据量大、要素种类繁多、元数据多样等特征，为提供高效的数据管理，平台采用分布式存储架构，数据通过气候模式输出重写（climate model output rewriter，CMOR）软件进行格式规范，并实现基于THREDDS（thematic real-time environmental distributed data services）的数据组织与共享。在平台建设及软件设计部署等层面，充分考虑数据安全。该平台实现BCC 3个模式约190 TB的试验数据稳定、高效共享，为国内外气候变化领域科研工作者提供获取数据的方便快捷途径与方法，成为推动我国气候模式国际应用的有力技术手段。Abstract: The experimental data of ongoing CMIP6 (Coupled Model Intercomparison Project Phase 6) are widely used to study the mechanism of climate change and provide technical support for the assessment report of the Intergovernmental Panel on Climate Change (IPCC). With more types of model experiments and more complex climate model, the amount of CMIP experimental data are also increasing rapidly. Therefore, Beijing Climate Center (BCC) has established Earth System Grid Federation (ESGF) data node to share experimental data of BCC CMIP6.BCC has three latest versions of models to participate in the project through model development in recent years. The hardware of the platform adopts a distributed storage architecture and is deployed in the demilitarized zone (DMZ) of China Meteorological Administration, which provides a strong guarantee for its network access rate and security. The data processing module mainly checks the integrity, processes the original model output and adopts the climate model output rewriter (CMOR) software to standardize the format. Thematic real-time environmental distributed data services data server is used for local storage management and data sharing, publishing metadata to ESGF index node for unified data retrieval. The data storage directory adopts hierarchical management structure with self-describing information to realize hierarchical and classified storage of different elements in different experiments. To ensure the security of data sharing, the platform is optimized based on ESGF security framework in addition to physically adding replica storage, and the needs of easy access are also considered.Totally, 190 TB experimental data of BCC CMIP6 have been released and shared since the establishment of the platform. The platform has provided important technical support for BCC to participate in the CMIP6, and it has also supported scientific research in the fields of climate change simulation and prediction, weather and climate extremes, global warming and human activities.Subsequent work will provide continuous data services to the CMIP and can be extended to other related model comparison programs. It is also important to further improve the capabilities of customized data sharing services.
-
Key words:
- CMIP;
- BCC;
- model experimental data;
- data sharing;
- data security
-
图 5 2022年上半年BCC CMIP6数据访问统计
Fig. 5 BCC CMIP6 data access statistics in the first half of 2022
表 1 BCC参加CMIP6试验的模式版本
Table 1 BCC model versions participated in CMIP6
模式 分量 模式版本 分辨率 BCC-ESM1.0[8] 大气 BCC-AGCM3-Chem T42L26(约为280 km，水平格点数为128×64，垂直分26层，模式顶为2.19 hPa) 陆面 BCC-AVIM2 T42(约为280 km，水平格点数为128×64) 海洋 MOM4-L40v3 gx1v1(纬向分辨率为1°，经向在10°S~10°N加密到(1/3)°，10°S~30°S和10°N~30°N由(1/3)°逐渐过渡到1°，30°S以南、30°N以北区域为1°，水平格点数为360×232，垂直分40层) 海冰 SIS gx1v1(水平分辨率与MOM4-L40v3相同) BCC-CSM2-MR[9] 大气 BCC-AGCM3-MR T106L46(约为110 km，水平格点数为320×160，垂直分46层，模式顶为1.46 hPa) 陆面 BCC-AVIM2 T106(约为110 km，水平格点数为320×160) 海洋 MOM4-L40v3 同BCC-ESM1.0 海冰 SIS 同BCC-ESM1.0 BCC-CSM2-HR[10] 大气 BCC-AGCM3-HR T266L56(约为45 km，水平格点数为800×400，垂直分56层，模式顶为0.1 hPa) 陆面 BCC-AVIM2 T266(约为45 km，水平格点数为800×400) 海洋 MOM5-L50 0.25°×0.25°(水平格点数为1440×688，垂直分50层) 海冰 SIS 0.25°×0.25°(水平格点数为1440×688) 
下载: 导出CSV
表 2 BCC CMIP6数据
Table 2 BCC CMIP6 data
核心试验/子计划 试验名称 模式 样本量 要素数量 数据量/TB 气候诊断、评估和描述试验(DECK)[2] 1pctCO2 BCC-CSM2-MR 1 142 29.00 BCC-ESM1.0 1 179 abrupt-4xCO2 BCC-CSM2-MR 1 142 BCC-ESM1.0 1 169 amip BCC-CSM2-MR 3 97 BCC-ESM1.0 3 134 esm-hist BCC-CSM2-MR 3 155 esm-piControl BCC-CSM2-MR 1 143 气候诊断、评估和描述试验(DECK)[2] piControl BCC-CSM2-MR 1 142 32.00 BCC-ESM1.0 1 168 历史气候模拟试验(Historical)[2] Historical BCC-CSM2-MR 3 154 BCC-ESM1.0 3 189 检测归因模式比较计划(DAMIP)[27] hist-GHG BCC-CSM2-MR 3 149 6.50 hist-aer 3 144 hist-nat 3 145 情景模式比较计划(ScenarioMIP)[28] SSP1-2.6 BCC-CSM2-MR 1 154 26.00 SSP2-4.5 1 153 SSP3-7.0 1 158 SSP5-8.5 1 154 耦合气候碳循环比较计划(C4MIP)[29] 1pctCO2-bgc BCC-CSM2-MR 1 144 7.40 1pctCO2-rad 1 144 esm-ssp585 1 155 全球季风模式比较计划(GMMIP)[30] amip-hist BCC-CSM2-MR 1 74 2.50 hist-resAMO 1 130 云反馈模式比较计划(CFMIP)[31] amip BCC-CSM2-MR 1 113 29.00 amip-4xCO2 1 115 amip-future4K 1 114 amip-m4K 1 114 amip-p4k 1 118 陆面、雪和土壤湿度模式比较计划(LS3MIP)[32] Land-Hist-princeton BCC-CSM2-MR 1 40 0.01 土地利用模式比较计划(LUMIP)[33] deforest-globe BCC-CSM2-MR 1 147 2.00 esm-ssp585-sspl26Lu 1 147 hist-nolu 1 146 land-hist 1 40 land-nolu 1 40 sspl26-ssp370Lu 1 147 ssp370-sspl26Lu 1 147 气溶胶和化学模式比较计划(AerChemMIP)[11] hist-piAer BCC-ESM1.0 3 186 4.10 hist-piNTCF 3 179 histSST 1 121 histSST-piCH4 1 120 histSST-piNTCF 1 120 piClim-BC 1 123 piClim-CH4 1 119 piClim-NOx 1 123 piClim-NTCF 1 120 piClim-O3 1 123 piClim-SO2 1 123 piClim-VOC 1 123 piClim-aer 1 123 piClim-control 1 119 ssp370 3 187 ssp370-lowNTCF 3 182 ssp370SST 1 121 ssp370SST-lowNTCF 1 121 年代际气候预测计划(DCPP)[13] dcppA-hindcast BCC-CSM2-MR 8 74 9.07 高分辨率模式比较计划(HighResMIP)[12] control-1950 BCC-CSM2-HR 1 113 70.00 highresSST-present 1 97 hist-1950 1 130
下载: 导出CSV
表 3 BCC-CSM2-MR模式历史气候模拟试验数据
Table 3 Historical experiment data of BCC-CSM2-MR
模式 分量模式 要素 格式 时间段 时效 分辨率 BCC-CSM2-MR 大气 近地表气温、地表气压、降水等 NetCDF 1850—2014年 月、日、3 h等 320×160，L46，L19等 陆面 土壤总含水量、地表径流等 NetCDF 1850—2014年 月、日、3 h等 320×160 海洋 海表面温度、海水质量、海表面压力等 NetCDF 1850—2014年 月、日 360×232，L40 海冰 海冰厚度、海冰面积、海冰表面温度等 NetCDF 1850—2014年 月、日 360×232
下载: 导出CSV
-
[1] 周天军, 邹立维, 吴波, 等.中国地球气候系统模式研究进展:CMIP计划实施近20年回顾.气象学报, 2014, 72(5):892-907. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201405007.htmZhou T J, Zou L W, Wu B, et al. Development of earth/climate system models in China: A review from the Coupled Model Intercomparison Proiect perspective. Acta Meteor Sinica, 2014, 72(5): 892-907. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201405007.htm [2] Eyring V, Bony S, Meehl G A, et al. Overview of the Coupled Model Intercomparison Project Phase 6(CMIP6)experimental design and organization. Geosci Model Dev, 2016, 9: 1937-1958. doi: 10.5194/gmd-9-1937-2016 [3] 赵宗慈, 罗勇, 黄建斌. CMIP6的设计. 气候变化研究进展, 2016, 12(3): 258-260. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201603013.htmZhao Z C, Luo Y, Huang J B. Design of CMIP6. Climate Change Research, 2016, 12(3): 258-260. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201603013.htm [4] 赵宗慈, 罗勇, 黄建斌. 从检验CMIP5气候模式看CMIP6地球系统模式的发展. 气候变化研究进展, 2018, 14(6): 643-648. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201806012.htmZhao Z C, Luo Y, Huang J B. The detection of the CMIP5 climate model to see the development of CMIP6 Earth system models. Climate Change Research, 2018, 14(6): 634-648. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201806012.htm [5] ESGF Home Page. [2019-02-16]. https://esgf-node.llnl.gov/. [6] 辛晓歌, 吴统文, 张洁. BCC气候系统模式开展的CMIP5试验介绍. 气候变化研究进展, 2012, 8(5): 378-382. doi: 10.3969/j.issn.1673-1719.2012.05.010Xin X G, Wu T W, Zhang J. Introduction of CMIP5 Experiments carried out by BCC Climate System Model. Climate Change Research, 2012, 8(5): 378-382. doi: 10.3969/j.issn.1673-1719.2012.05.010 [7] 辛晓歌, 吴统文, 张洁, 等. BCC模式及其开展的CMIP6试验介绍. 气候变化研究进展, 2019, 15(5): 533-539. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201905014.htmXin X G, Wu T W, Zhang J, et al. Introduction of BCC models and its participation in CMIP6. Climate Change Research, 2019, 15(5): 533-539. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201905014.htm [8] Wu T, Zhang F, Zhang J, et al. Beijing Climate Center Earth System Model version 1 (BCC-ESM1): Model description and evaluation of aerosol simulations. Geosci Model Dev, 2020, 13: 977-1005. doi: 10.5194/gmd-13-977-2020 [9] Wu T W, Lu Y X, Fang Y J, et al. The Beijing Climate Center Climate System Model(BCC-CSM): Main progress from CMIP5 to CMIP6. Geosci Model Dev, 2019, 12: 1573-1600. doi: 10.5194/gmd-12-1573-2019 [10] Wu T, Yu R, Lu Y, et al. BCC-CSM2-HR: A high-resolution version of the Beijing Climate Center Climate System Model. Geosci Model Dev, 2021, 14: 2977-3006. doi: 10.5194/gmd-14-2977-2021 [11] Collins W J, Lamarque J-F, Schulz M, et al. AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6. Geosci Model Dev, 2017, 10: 585-607. doi: 10.5194/gmd-10-585-2017 [12] Haarsma R J, Roberts M J, Vidale P L, et al. High Resolution Model Intercomparison Project(HithResMIP V1.0) for CMIP6. Geosci Model Dev, 2016, 9: 4185-4208. doi: 10.5194/gmd-9-4185-2016 [13] Boer G J, Smith D M, Cassou C, et al. The Decadal Climate Prediction Project(DCPP) contribution to CMIP6. Geosci Model Dev, 2016, 9: 3751-3777. doi: 10.5194/gmd-9-3751-2016 [14] Wei M, Li Q Q, Xin X G, et al. Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition. Sci Bull, 2017, 62(16): 1142-1147. doi: 10.1016/j.scib.2017.08.012 [15] 米前川, 高西宁, 李玥, 等. 深度学习方法在干旱预测中的应用. 应用气象学报, 2022, 33(1): 104-114. doi: 10.11898/1001-7313.20220109Mi Q C, Gao X N, Li Y, et al. Application of deep learning method to drought prediction. J Appl Meteor Sci, 2022, 33(1): 104-114. doi: 10.11898/1001-7313.20220109 [16] 邱岚, 谭彬. 安全域划分研究与应用. 计算机安全, 2012(6): 39-42. https://www.cnki.com.cn/Article/CJFDTOTAL-DZJC201206011.htmQiu L, Tan B. Research and application of security domain. Comput Secur, 2012(6): 39-42. https://www.cnki.com.cn/Article/CJFDTOTAL-DZJC201206011.htm [17] 张正秋, 祝从文, 苏京志, 等. 气候动力诊断和分析系统设计与应用. 应用气象学报, 2021, 32(5): 542-552. doi: 10.11898/1001-7313.20210503Zhang Z Q, Zhu C W, Su J Z, et al. Designing and implementation of Climate Dynamic Diagnosis and Analysis System. J Appl Meteor Sci, 2021, 32(5): 542-552. doi: 10.11898/1001-7313.20210503 [18] 刘娜, 熊安元, 张强, 等. 强对流天气人工智能应用训练基础数据集构建. 应用气象学报, 2021, 32(5): 530-541. doi: 10.11898/1001-7313.20210502Liu N, Xiong A Y, Zhang Q, et al. Development of basic dataset of severe convective weather for artificial intelligence training. J Appl Meteor Sci, 2021, 32(5): 530-541. doi: 10.11898/1001-7313.20210502 [19] 郭启云, 杨荣康, 程凯琪, 等. 基于探空观测的多源掩星折射率质量控制及对比. 应用气象学报, 2020, 31(1): 13-26. doi: 10.11898/1001-7313.20200102Guo Q Y, Yang R K, Cheng K Q, et al. Refractive index quality control and comparative analysis of multi-source occultation based on sounding observation. J Appl Meteor Sci, 2020, 31(1): 13-26. doi: 10.11898/1001-7313.20200102 [20] 董晓云, 余锦华, 梁信忠, 等. CWRF模式极端降水模拟误差订正. 应用气象学报, 2020, 31(4): 504-512. doi: 10.11898/1001-7313.20200412Dong X Y, Yu J H, Liang X Z, et al. Bias correction of summer extreme precipitation simulated by CWRF model. J Appl Meteor Sci, 2020, 31(4): 504-512. doi: 10.11898/1001-7313.20200412 [21] CMOR. [2021-01-08]. https://cmor.llnl.gov/pdf. [22] CMIP6 Table Excerpt. [2022-02-01]. https://cmor.llnl.gov/mydoc_cmip6_table_excerpt. [23] 丁一汇, 李霄, 李巧萍. 气候变暖背景下中国地面风速变化研究进展. 应用气象学报, 2020, 31(1): 1-12. doi: 10.11898/1001-7313.20200101Ding Y H, Li X, Li Q P. Advances of surface wind speed changes over China under global warming. J Appl Meteor Sci, 2020, 31(1): 1-12. doi: 10.11898/1001-7313.20200101 [24] 成菲, 李巧萍, 沈新勇, 等. BCC-CSM1.1m对欧亚积雪覆盖的预测评估. 应用气象学报, 2021, 32(5): 553-566. doi: 10.11898/1001-7313.20210504Cheng F, Li Q P, Shen X Y, et al. Evaluation of Eurasian snow cover fraction prediction based on BCC-CSM1.1m. J Appl Meteor Sci, 2021, 32(5): 553-566. doi: 10.11898/1001-7313.20210504 [25] 魏敏. 气象高性能计算应用服务环境适应性研究. 气象, 2015, 41(1): 92-97. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201501011.htmWei M. Study on adaptability of meteorological high performance computing application services environment. Meteor Mon, 2015, 41(1): 92-97. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201501011.htm [26] 王娟怀, 李清泉, 汪方, 等. 基于DERF2.0的华南前汛期降水订正. 应用气象学报, 2021, 32(1): 115-128. doi: 10.11898/1001-7313.20210110Wang J H, Li Q Q, Wang F, et al. Correction of precipitation forecast predicted by DERF2.0 during the pre-flood season in South China. J Appl Meteor Sci, 2021, 32(1): 115-128. doi: 10.11898/1001-7313.20210110 [27] Gillett N P, Shiogama H, Funke B, et al. The Detection and Attribution Model Intercomparison Project Project(DAMIP V1.0) contribution to CMIP6. Geosci Model Dev, 2016, 9: 3685-3697. [28] O'Neill B C, Tebaldi C, van Vuuren D P, et al. The Scenario Model Intercomparison Project(ScenarioMIP) for CMIP6. Geosci Model Dev, 2016, 9: 3461-3482. [29] Jones C D, Arora V, Friedlingstein P, et al. C4MIP: The Coupled Climate-Carbon Cycle Model Intercomparison Project: Experimental protocol for CMIP6. Geosci Model Dev, 2016, 9: 2853-2880. [30] Zhou T J, Turner A G, Kinter J L, et al. GMMIP(V1.0) contribution to CMIP6: Global Monsoons Model Inter-comparison Project. Geosci Model Dev, 2016, 9: 3589-3604. [31] Webb M J, Andrews T, Bodas-Salcedo A, et al. The Cloud Feedback Model Intercomparison Project(CFMIP) contribution to CMIP6. Geosci Model Dev, 2017, 10: 359-384. [32] van den Hurk B, Kim H, Krinner G, et al. LS3MIP(V1.0) contribution to CMIP6: The Land Surface, Snow and Soil moisture Model Intercomparison Project: Aims, setup and expected outcome. Geosci Model Dev, 2016, 9: 2809-2832. [33] Lawrence D M, Hurtt G C, Arneth A, et al. The Land Use Model Intercomparison Project(LUMIP) contribution to CMIP6: Rationale and experimental design. Geosci Model Dev, 2016, 9: 2973-2998. [34] Xin X, Wu T, Jie W, et al. Impact of higher resolution on precipitation over China in CMIP6 HighResMIP Models. Atmosphere, 2021, 12(6): 762. [35] Zhang J, Furtado K, Turnock S T, et al. The role of anthropogenic aerosols in the anomalous cooling from 1960 to 1990 in the CMIP6 Earth system models. Atmos Chem Phys, 2021, 21: 18609-18627. [36] Ahmed F, Neelin J D. A Process-oriented diagnostic to assess precipitation-thermodynamic relations and application to CMIP6 models. Geophys Res Lett, 2021, 48, e2021GL094108. [37] Publication Hub. [2022]. https://cmip-publications.llnl.gov/view/CMIP6/?type=model. [38] IPCC. Annex II: Models//Climate Change 2021: The 19 Physical Science Basis. Cambridge: Cambridge University Press, 2021. [39] 黄晓远, 李谢辉. 基于CMIP6的西南暴雨洪涝灾害风险未来预估. 应用气象学报, 2022, 33(2): 231-243. doi: 10.11898/1001-7313.20220209Huang X Y, Li X H. Future projection of rainstorm and flood disaster risk in Southwest China based on CMIP6 models. J Appl Meteor Sci, 2022, 33(2): 231-243. doi: 10.11898/1001-7313.20220209 [40] 俞静雯, 李清泉, 丁一汇, 等. 气候变暖背景下青藏高原夏季水汽的长期变化趋势分析. 中国科学(地球科学), 2022, 52(5): 942-954. https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202205010.htmYu J W, Li Q Q, Ding Y H, et al. Long-term trend of water vapor over the Tibetan Plateau in boreal summer under global warming. Sci China(Earth Sci), 2022, 52(5): 942-954. https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202205010.htm [41] 刘绿柳, 魏麟骁, 徐影, 等. 气候变化对黄河流域生态径流影响预估. 水科学进展, 2021, 32(6): 824-833. https://www.cnki.com.cn/Article/CJFDTOTAL-SKXJ202106002.htmLiu L L, Wei L X, Xu Y, et al. Projection of climate change impacts on ecological flow in the Yellow River Basin. Adv Water Sci, 2021, 32(6): 824-833. https://www.cnki.com.cn/Article/CJFDTOTAL-SKXJ202106002.htm -
图(5) / 表(3)
计量
- 摘要浏览量: 2864
- HTML全文浏览量: 424
- PDF下载量: 244
- 被引次数: 0
