基于北半球1220个长时间序列的最大冻结深度站点数据作为训练数据，融合最大冻结深度的站点数据和气温、降水、冻结指数、融化指数、积雪深度、太阳辐射、叶面积指数、土壤质地等预测因子，采用XGBoost算法构建北半球土壤冻结深度模型。在模型训练过程中，采用十折交叉验证，运行300次，输出最优的模型。模拟并预测了北半球过去和未来不同情景下22个模式（SSP126、SSP245、SSP370、SSP585）土壤最大冻结深度。22个模式的模型集合平均RSME为33.15 cm，MAE为22.96 cm，R2为0.81。数据格式为netcdf，空间分辨率约0.5°，时间分辨率为逐年。
", "ds_source": "（1）CMIP6模式数据本研究下载了由世界气候研究计划（World Climate Research Program, WCRP）组织的第六次国际耦合模型比较计划（Coupled Model Intercomparison Project Phase 6， CMIP6）中的五个变量（气温/降水/积雪深度/太阳辐射/叶面积指数），22个气候模式数据用于季节冻土最大冻结深度的模拟（https://esgf-node.llnl.gov/projects/cmip6/）。这些模式输出采用了不同的空间分辨率，时间范围均为1850年到2100年。本文使用五个CMIP6试验的输出：包括1个历史模拟试验，从1850年到2014年；5个未来气候预估试验，从2015年到2100年。历史气候模拟试验是在实际观测的基础上，由外强迫驱动下对1850~2014年的历史气候演变。对于未来试验的预估，CMIP6模式使用了共享社会经济路径（Shared Socioeconomic Pathways，SSPs）和典型浓度路径（Representative Concentration Pathways，RCPs）来描述在没有气候变化或气候政策影响的情况下社会可能的未来发展，更加强调未来辐射强迫情景与共享社会经济情景的一致性。所选取的未来数据包括SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5四种不同情景，SSP1、SSP2、SSP3和SSP5分别代表了可持续发展、中度发展、局部发展和常规发展4种路径。使用双线性插值方法将CMIP6模式数据的分辨率统一为0.5°×0.5°。
\n（2）土壤质地数据：一个全面的、网格化的、利用地球系统模型的全球土壤数据集（GSDE）开发于中山大学陆气互动研究小组（Land-Atmosphere Interaction Research Group at Sun Y at-sen University，http://globalchange.bnu.edu.cn/research/soilw#download），GSDE提供的土壤数据包括土壤粒径分布、有机碳和养分等。GSDE是基于世界土壤地图和各种区域及国家的土壤数据库，包括土壤属性数据和土壤地图。分辨率为20弧秒。本文使用了其中的4个土壤属性，分别是含砂量、含泥量、黏土含量、含砾量。
", "ds_process_way": "融合最大冻结深度的站点数据和气温、降水、冻结指数、融化指数、积雪深度、太阳辐射、叶面积指数、土壤质地等预测因子，利用最大冻结深度的站点数据作为训练数据，采用XGBoost算法构建北半球土壤冻结深度模型。在模型训练过程中，采用十折交叉验证，运行300次，输出最优的模型。
", "ds_quality": "基于22个CMIP6模式数据分别构建的北半球土壤最大冻结深度模型误差，22个模式的模型集合平均RSME为33.15 cm，MAE为22.96 cm，R2为0.81。
", "ds_acq_start_time": "1850-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "北半球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 0.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 266045506, "ds_files_count": 6, "ds_format": "Netcdf", "ds_space_res": "0.5°", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "5a620863-5111-415c-b6c8-a6bd28639098.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-02-18 15:27:17", "last_updated": "2026-05-18 15:34:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6394.2024", "i18n": { "en": { "title": "Northern Hemisphere Seasonal Freeze Depth Dataset (1850-2100)", "ds_format": "Netcdf", "ds_source": "(1) This study downloaded five variables (temperature/precipitation/snow depth/solar radiation/leaf area index) from the 6th International Coupled Model Intercomparison Project Phase 6 (CMIP6) organized by the World Climate Research Program (WCRP), and 22 climate model data were used to simulate the maximum freezing depth of seasonally frozen soil（ https://esgf-node.llnl.gov/projects/cmip6/ ）. These mode outputs use different spatial resolutions and time ranges from 1850 to 2100. This article uses the outputs of five CMIP6 tests: including one historical simulation test from 1850 to 2014; Five future climate prediction experiments from 2015 to 2100. The historical climate simulation experiment is based on actual observations and is driven by external forces to study the historical climate evolution from 1850 to 2014. For the estimation of future experiments, the CMIP6 model uses Shared Socio economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to describe the possible future development of society without the impact of climate change or climate policies, emphasizing the consistency between future radiative forcing scenarios and shared socio-economic scenarios. The selected future data includes four different scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. SSP1, SSP2, SSP3, and SSP5 represent four paths of sustainable development, moderate development, local development, and conventional development, respectively. Use bilinear interpolation method to unify the resolution of CMIP6 mode data to 0.5 °× 0.5 °.\r\n
\r\n(2) Soil Texture Data: A comprehensive, gridded global soil dataset (GSDE) utilizing Earth system models was developed by the Land Atmosphere Interaction Research Group at Sun Y at sen University, http://globalchange.bnu.edu.cn/research/soilw#download ）The soil data provided by GSDE includes soil particle size distribution, organic carbon, and nutrient content. GSDE is based on world soil maps and various regional and national soil databases, including soil attribute data and soil maps. The resolution is 20 arc seconds. This article uses four soil attributes, namely sand content, mud content, clay content, and gravel content.
", "ds_quality": "The maximum freezing depth model error of soil in the Northern Hemisphere was constructed based on 22 CMIP6 model data, with an average RSME of 33.15 cm, MAE of 22.96 cm, and R2 of 0.81 for the model set of 22 models.
", "ds_ref_way": "", "ds_abstract": "Based on 1220 long-term time series data of maximum freezing depth stations in the Northern Hemisphere as training data, the XGBoost algorithm is used to construct a soil freezing depth model for the Northern Hemisphere by integrating station data of maximum freezing depth with predictive factors such as temperature, precipitation, freezing index, melting index, snow depth, solar radiation, leaf area index, and soil texture. During the model training process, ten fold cross validation was used to run 300 times and output the optimal model. We simulated and predicted the maximum freezing depth of soil in 22 patterns (SSP126, SSP245, SSP370, SSP585) under different past and future scenarios in the Northern Hemisphere. The average RSME of the model set with 22 modes is 33.15 cm, MAE is 22.96 cm, and R2 is 0.81. The data format is netcdf, with a spatial resolution of approximately 0.5 ° and a temporal resolution of year by year.
", "ds_time_res": "", "ds_acq_place": "Northern Hemisphere", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Integrating station data with maximum freezing depth and predictive factors such as temperature, precipitation, freezing index, melting index, snow depth, solar radiation, leaf area index, soil texture, etc., using station data with maximum freezing depth as training data, XGBoost algorithm is used to construct a soil freezing depth model for the Northern Hemisphere. During the model training process, ten fold cross validation was used to run 300 times and output the optimal model.
", "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": [ "冻土", "北半球", "季节冻深" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北半球" ], "ds_time_tags": [ 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 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, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100 ], "ds_contributors": [ { "true_name": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "陈聪", "email": "chenc20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "陈聪", "email": "chenc20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "陈聪", "email": "chenc20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "冻土" }