基于WRF模式，结合改进的土壤水热、积雪参数化方案，和Spectral Nudging方案，开展了长江黄河源区未来（2015-2100年）不同气候情景（SSP126、SSP245和SSP585）下9km气候动力降尺度模拟。所用驱动数据源于中国科学院大气物理研究所开发的全球偏差校正CMIP6数据集（BC-CMIP6; Xu et al., 2021；https://www.nature.com/articles/s41597-021-01079-3#Sec6）， 提供动力降尺度模拟的边界和初始条件。该数据集基于18个CMIP6模式数据，采用ERA5再分析数据进行均值与方差偏差校正，同时引入多模式集合（MME）的非线性趋势。校正后的数据具有ERA5基准期的气候统计特征，又有效保留未来气候变化趋势，空间分辨率为1.25°×1.25°，时间步长为6小时。基于该数据数据驱动的WRF模式输出了高分辨率（9km）的水文强迫数据，经过时间插值、日统计和空间插值处理，最终生成0.1度分辨率的网格化数据产品。", "ds_source": "
基于WRF模式，结合改进的土壤水热、积雪参数化方案，和Spectral Nudging方案，开展了长江黄河源区未来（2015-2100年）不同气候情景下9km气候动力降尺度模拟。", "ds_process_way": "
本数据集为WRF模式动力降尺度（9km）过后输出的水文强迫数据，经过时间插值、日统计和空间插值处理，最终生成0.1度分辨率的网格化数据产品。", "ds_quality": "
", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2015-01-01 00:00:00", "ds_acq_place": "长江,黄河源区", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 30249721474, "ds_files_count": 1807, "ds_format": "netcdf", "ds_space_res": "0.1°", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "dd476baf-d0c5-4722-ba78-5792a8595745.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-08-04 11:23:21", "last_updated": "2025-08-04 15:45:00", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6938.2025", "i18n": { "en": { "title": "High Resolution Climate Dynamics Downscaling Dataset for Future SSP126, SSP245, and SSP585 Scenarios in the Source Regions of the Yangtze and Yellow Rivers", "ds_format": "", "ds_source": "
Based on the WRF model, combined with an improved soil water heat and snow parameterization scheme and Spectral Nudging scheme, a downscaling simulation of 9 km climate dynamics under different climate scenarios in the Yangtze River and Yellow River source areas in the future (2015-2100) was carried out.", "ds_quality": "
", "ds_ref_way": "", "ds_abstract": "
Based on the WRF model, combined with improved soil water heat and snow parameterization schemes and Spectral Nudging schemes, a downscaling simulation of climate dynamics over a distance of 9km was conducted in the Yangtze River and Yellow River source areas under different climate scenarios (SSP126, SSP245, and SSP585) from 2015 to 2100. The driving data used is from the global bias correction CMIP6 dataset developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (BC-CMIP6; Xu et al., 2021; https://www.nature.com/articles/s41597-021-01079-3#Sec6 ）Provide boundary and initial conditions for dynamic downscaling simulation. This dataset is based on 18 CMIP6 pattern data, and ERA5 reanalysis data is used for mean and variance bias correction, while introducing non-linear trends from the Multi Pattern Set (MME). The corrected data exhibits climate statistical characteristics of the ERA5 baseline period while effectively preserving future climate change trends, with a spatial resolution of 1.25 °× 1.25 ° and a time step of 6 hours. Based on this data-driven WRF model, high-resolution (9km) hydrological forcing data was output. After time interpolation, daily statistics, and spatial interpolation processing, a grid data product with a resolution of 0.1 degrees was finally generated.
", "ds_time_res": "日", "ds_acq_place": "Yangtze River and Yellow River source areas", "ds_space_res": "0.1°", "ds_projection": "", "ds_process_way": "This dataset is the hydrological forcing data output from the WRF model after dynamic downscaling (9km). After time interpolation, daily statistics, and spatial interpolation processing, a grid data product with a resolution of 0.1 degrees is finally generated.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "长江", "黄河源区", "SSP126", "SSP245", "SSP585" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "长江" ], "ds_time_tags": [ 2015, 2100 ], "ds_contributors": [ { "true_name": "赵林", "email": "zhaolin_110@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "孟宪红", "email": "mxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵林", "email": "zhaolin_110@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "赵林", "email": "zhaolin_110@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "category": "气象" }