近地表冻融循环是陆地表层热力变化的基本特征，是研究寒区冻土分布和气候变化的重要参数。近几十年来，全球变暖不可避免地导致了冻融过程和格局的变化，威胁着生态环境安全和区域可持续发展。为支撑气候变化背景下冻融循环响应和反馈特征的研究，我们基于最新的ERA5-Land逐时温度数据，制备了2000–2023年中国及周边地区地表冻融数据集。该数据集包含近地表日冻融状态、年冻融强度等10个冻融循环变化指标，空间分辨率为0.1°和0.01°，区域范围介于3°N–54°N，60°E–136°E，覆盖中国、蒙古、巴基斯坦、阿富汗、塔吉克斯坦、吉尔吉斯斯坦等29个国家。本数据集具有现势性强、要素多、范围广等特点，可为气候变化背景下近地表冻融循环过程演变规律、冰冻圈、水文、生态、环境等研究提供数据支撑。
", "ds_source": "欧洲中期天气预报中心(ECMWF)ERA5-Land数据集和美国国家环境信息中心全球综合地表数据库(ISD)。
", "ds_process_way": "首先，将原始ERA5-Land温度单位转换为摄氏度，观测时间转换为地方时；其次，检查每个格点数据序列的变化特征，对数据质量进行控制；最后，从每天24个小时的数据序列中，提取当天的最高温度(T_max)、最低温度(T_min)和日平均温度(T_avg)，进而对冻融循环特征进行计算统计。冻融变量包括逐日空气(a)和地表(b)冻融状态（FTS）、逐年冻结天数（FSD），冻结指数(FSI)、冻融天数（FTD）、冻融指数（FTI）、融化天数（TSD）、融化指数（TSI）、经典融化指数(TI)、冻结指数(FI)和2000—2023地表热力分类10个冻融循环特征，提供两种空间分辨率：0.1°（约9 km）和通过最近邻插值算法得到的0.01°（900m）。
", "ds_quality": "空气冻融状态和地表冻融状态总体判别精度均为87%，年空气冻结日数(FSD)、年空气融化日数(TSD)和年空气冻融循环日数(FTD)的均方根误差(RMSE)分别为17.8、29.9和20.7天。年空气冻结指数(FSI)、年空气融化指数(TSI)和年空气冻融循环指数(FTI)分别为395.1、455.3和104.5天 ℃。 年地表冻结日数(FSD)、年地表融化日数(TSD)和年地表冻融循环日数(FTD)的RMSE分别为12、20和28.6天。年地表冻结指数(FSI)、年地表融化指数(TSI)和年地表冻融循环指数(FTI)分别为155.8、299.1和160.7天 ℃。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "中国及周边地区", "ds_acq_lon_east": 60.0, "ds_acq_lat_south": 3.0, "ds_acq_lon_west": 136.0, "ds_acq_lat_north": 54.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 22612924440, "ds_files_count": 35925, "ds_format": "GeoTIFF", "ds_space_res": "0.1°,0.01°", "ds_time_res": "天,年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "dad395a9-148e-489f-bc72-8cb62a7014ed.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据集保存为GeoTIF文件格式，可用ArcGIS、QGIS、ENVI、ERDAS等常用的地学和图像软件直接读取，可应用于中国区域的冰冻圈和水文、生态系统相关研究和气候变化分析。", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170", "170.45", "170.4510" ], "quality_level": 3, "publish_time": "2024-10-29 10:26:33", "last_updated": "2026-05-27 18:01:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6621.2024", "i18n": { "en": { "title": "Surface freeze-thaw dataset of China and surrounding areas from 2000 to 2023", "ds_format": "GeoTIFF", "ds_source": "The European Centre for Medium Range Weather Forecasts (ECMWF) ERA5 Land dataset and the United States National Environmental Information Center Global Integrated Surface Database (ISD).
", "ds_quality": "The overall discrimination accuracy of air freeze-thaw status and surface freeze-thaw status is 87%. The root mean square errors (RMSE) of annual air freezing days (FSD), annual air melting days (TSD), and annual air freeze-thaw cycle days (FTD) are 17.8, 29.9, and 20.7 days, respectively. The annual air freezing index (FSI), annual air melting index (TSI), and annual air freeze-thaw cycle index (FTI) are 395.1, 455.3, and 104.5 days ℃, respectively. The RMSE of annual surface freezing days (FSD), annual surface melting days (TSD), and annual surface freeze-thaw cycle days (FTD) are 12, 20, and 28.6 days, respectively. The annual surface freezing index (FSI), annual surface melting index (TSI), and annual surface freeze-thaw cycle index (FTI) are 155.8, 299.1, and 160.7 days ℃, respectively.
", "ds_ref_way": "", "ds_abstract": "The near surface freeze-thaw cycle is a fundamental characteristic of thermal changes in the land surface and an important parameter for studying the distribution of frozen soil and climate change in cold regions. In recent decades, global warming has inevitably led to changes in freeze-thaw processes and patterns, posing a threat to ecological security and regional sustainable development. To support the study of freeze-thaw cycle response and feedback characteristics under the background of climate change, we prepared a surface freeze-thaw dataset for China and surrounding areas from 2000 to 2023 based on the latest ERA5 Land hourly temperature data. This dataset contains 10 indicators of freeze-thaw cycle changes, including daily freeze-thaw status and annual freeze-thaw intensity on the near surface, with spatial resolutions of 0.1 ° and 0.01 °. The regional range is between 3 ° N-54 ° N and 60 ° E-136 ° E, covering 29 countries including China, Mongolia, Pakistan, Afghanistan, Tajikistan, and Kyrgyzstan. This dataset has the characteristics of strong current situation, multiple elements, and wide scope, which can provide data support for the evolution of near surface freeze-thaw cycles, cryosphere, hydrology, ecology, environment and other research under the background of climate change.
", "ds_time_res": "", "ds_acq_place": "China and surrounding regions", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Firstly, convert the original ERA5 Land temperature unit to Celsius and the observation time to local time; Secondly, check the changing characteristics of each grid point data sequence and control the data quality; Finally, from the data sequence of 24 hours per day, extract the highest temperature (Tmax), lowest temperature (Tmin), and daily average temperature (Tavg) of the day, and then calculate and statistically analyze the freeze-thaw cycle characteristics. The freeze-thaw variables include daily air (a) and surface (b) freeze-thaw status (FTS), annual freezing days (FSD), freezing index (FSI), freeze-thaw days (FTD), freeze-thaw index (FTI), melting days (TSD), melting index (TSI), classical melting index (TI), freezing index (FI), and 10 freeze-thaw cycle characteristics of 2000-2023 surface thermal classification, providing two spatial resolutions: 0.1 ° (about 9 km) and 0.01 ° (900m) obtained through nearest neighbor interpolation algorithm.
", "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": [ "近地表冻融" ], "ds_subject_tags": [ "地球科学", "地理学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "巴基斯坦", "蒙古", "阿富汗", "塔吉克斯坦", "吉尔吉斯斯坦", "中巴经济走廊", "一带一路", "黄河流域", "中亚", "青藏高原" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 ], "ds_contributors": [ { "true_name": "赵国辉", "email": "zhgh@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": "张耀南", "email": "yaonan@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": "余慧明", "email": "yuhm@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": "肖瑶", "email": "xiaoyao@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": "赵国辉", "email": "zhgh@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": "张耀南", "email": "yaonan@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": "余慧明", "email": "yuhm@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": "肖瑶", "email": "xiaoyao@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": "赵国辉", "email": "zhgh@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": "冻土" }