{
    "created": "2023-02-22 11:09:14",
    "updated": "2026-06-23 04:06:01",
    "id": "4759b5b7-27b1-4a45-b3cb-ceb532a666a1",
    "version": 0,
    "ds_topic": null,
    "title_cn": "祁连山国家公园未来气候情景数据（2030年、2050年、2070年）",
    "title_en": "Future climate scenario data of Qilian Mountain National Park (2030, 2050, 2070)",
    "ds_abstract": "<p>&emsp;&emsp;该数据包含了四种共享社会经济路径情景（SSP126/245/370/585）下NCC气候模式预测未来三个时期（2030/2050/2070）的降水数据和TaiESM1模式预测未来三个时期（2030/2050/2070）的气温数据。",
    "ds_source": "<p>&emsp;&emsp;以CMIP6发布的未来情景（SSP1-2.6，SSP2-4.5，SSP3-7.0，SSP5-8.5）数据为基础数据源。",
    "ds_process_way": "<p>&emsp;&emsp;通过对CMIP6发表的数据，采用统计降尺度方法得到未来不同情景下年均气温和年降水的预估信息。在气温方面，考虑地形的影响，构建了分层校正函数；在降水方面，利用机器学习算法构建环境、地形变量与降水之间的关系模型。",
    "ds_quality": "<p>&emsp;&emsp;1.原始数据来源于CMIP6公开发布的基础数据，数据精度获得充分验证和控制；2.数据处理方法完全按照相应的处理技术规范进行，并借助地面监测数据、再分析数据进行了交叉验证和质量控制。",
    "ds_acq_start_time": "2030-01-01 00:00:00",
    "ds_acq_end_time": "2070-12-31 00:00:00",
    "ds_acq_place": "祁连山国家公园",
    "ds_acq_lon_east": 103.3586111111111,
    "ds_acq_lat_south": 36.75694444444444,
    "ds_acq_lon_west": 95.2,
    "ds_acq_lat_north": 39.757777777777775,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "apply-access",
    "ds_total_size": 22730323619,
    "ds_files_count": 2,
    "ds_format": "NC4",
    "ds_space_res": "1km",
    "ds_time_res": "20年",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "4759b5b7-27b1-4a45-b3cb-ceb532a666a1.png",
    "ds_thumb_from": 0,
    "ds_ref_way": "杜军，祁连山国家公园未来气候情景数据（2030年、2050年、2070年），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.nieer.db2744.2023",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "52109486-75ef-4764-a933-6380c6f42432",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "10.12072/ncdc.nieer.db2744.2023",
    "subject_codes": [
        "170.45"
    ],
    "quality_level": 0,
    "publish_time": "2023-02-28 16:00:12",
    "last_updated": "2023-03-01 10:50:57",
    "protected": false,
    "protected_to": "2025-01-01 00:00:00",
    "lang": "zh",
    "cstr": "11738.11.ncdc.nieer.db2744.2023",
    "i18n": {
        "en": {
            "title": "Future climate scenario data of Qilian Mountain National Park (2030, 2050, 2070)",
            "ds_format": "",
            "ds_source": "<p>&emsp; Take the data of future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) released by CMIP6 as the basic data source.",
            "ds_quality": "<p>&emsp;1. The original data comes from the basic data published by CMIP6, and the data accuracy is fully verified and controlled; 2. The data processing method is fully in accordance with the corresponding processing technical specifications, and cross validation and quality control are carried out with the help of ground monitoring data and reanalysis data.",
            "ds_ref_way": "",
            "ds_abstract": "<p>  This data includes the precipitation data predicted by NCC climate model for the next three periods (2030/2050/2070) and the temperature data predicted by TaiESM1 model for the next three periods (2030/2050/2070) under the four shared socio-economic path scenarios (SSP126/245/370/585).</p>",
            "ds_time_res": "20年",
            "ds_acq_place": "Qilian Mountain National Park",
            "ds_space_res": "1km",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp; Based on the data published by CMIP6, the statistical downscaling method is used to obtain the estimated information of annual average temperature and annual precipitation under different scenarios in the future. In terms of temperature, considering the influence of terrain, a layered correction function is constructed; In precipitation, machine learning algorithm is used to build the relationship model between environment, terrain variables and precipitation.",
            "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": [
        "气温",
        "降水",
        "共享社会经济路径",
        "情景预测",
        "祁连山"
    ],
    "ds_subject_tags": [
        "地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "祁连山国家公园"
    ],
    "ds_time_tags": [
        2030,
        2031,
        2032,
        2033,
        2034,
        2035,
        2036,
        2037,
        2038,
        2039,
        2040,
        2041,
        2042,
        2043,
        2044,
        2045,
        2046,
        2047,
        2048,
        2049,
        2050,
        2051,
        2052,
        2053,
        2054,
        2055,
        2056,
        2057,
        2058,
        2059,
        2060,
        2061,
        2062,
        2063,
        2064,
        2065,
        2066,
        2067,
        2068,
        2069,
        2070
    ],
    "ds_contributors": [
        {
            "true_name": "杜军",
            "email": "dujun2012@lzb.ac.cn",
            "work_for": "中国科学院西北生态环境资源研究院",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "杜军",
            "email": "dujun2012@lzb.ac.cn",
            "work_for": "中国科学院西北生态环境资源研究院",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "杜军",
            "email": "dujun2012@lzb.ac.cn",
            "work_for": "中国科学院西北生态环境资源研究院",
            "country": "中国"
        }
    ],
    "category": "气象"
}