多年冻土区的年平均地温是多年冻土变化研究的另外一个重要指标。在以往的研究中，基于野外实测数据以及遥感反演算法已经对年平均地温在历史时期下的变化有了很多探讨，但其空间分辨率较低。以CMIP6数据为基础，通过降尺度的方法获得1km分辨率的多年冻土区地表气温；然后该数据作为多年平均地温未来变化的高精度高分辨输入变量，从而进一步获得1km分辨率的北半球年平均地温，通过使用多种机器学习方法，获取多种方法多模式平均，年平均地温数据集的精度和分辨率都得到提升。集合平均模式输出结果的的RMSE为1.01℃，MAE为0.69℃，R为0.89。
", "ds_source": "北半球多年冻土区地温年变化深度处的年平均地温（MAGT）观测数据建立在全球多年冻土网络（GTN-P）数据库（gtnpdatabase.org）的基础上，同时，我们还扩展了GTN-P数据，并从相关文献中添加更多的MAGT观测数据，包括一些MAGT>0℃观测数据，这些观测数据是帮助我们预测未来情景中地面热状态的关键因素其它相关数据请参考Jin et al., 2024.
", "ds_process_way": "使用的机器学习模型包括逻辑回归（LR）、随机森林（RF）和LightGBM（LGB）。考虑到使用单一方法可能会导致模拟的过度拟合，我们在模拟MAGT时使用了上述三种方法的集和平均结果。使用TDD、FDD、叶面积指数、降水量、积雪、太阳辐射、土壤含水量、土壤有机质、砾石的体积含量作为输入变量，与站点观测值相对应，用90%的数据作为测试集进行模型建立，10%的数据作为验证集对模型的精度进行验证评价，为了减少单次运行的不确定性，本研究对使用的三种机器学习模型运行了100次，用其集合平均结果，最终结合各时期的环境变量模拟出不同时期的多年冻土区年平均地温的分布变化。
", "ds_quality": "集合平均模式输出结果的的RMSE为1.01℃，MAE为0.69℃，R为0.89。
", "ds_acq_start_time": "1850-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "", "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": "login-access", "ds_total_size": 83277754433, "ds_files_count": 2, "ds_format": "netcdf格式，单位℃", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "f41f6bd2-c137-4ccb-bf20-0e064187e99a.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "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": [ "170.45" ], "quality_level": 3, "publish_time": "2024-03-01 11:57:28", "last_updated": "2025-04-29 16:10:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB4213.2024", "i18n": { "en": { "title": "Long time series kilometer level annual average ground temperature dataset in the Northern Hemisphere (1850-2100)", "ds_format": "netcdf", "ds_source": "The annual average ground temperature (MAGT) observation data at the depth of annual temperature variation in the permafrost regions of the Northern Hemisphere are based on the Global Permafrost Network (GTN-P) database (gtnpdatabase. org). Additionally, we have expanded the GTN-P data and added more MAGT observation data from relevant literature, including some MAGT>; 0 ℃ observation data, these observation data are key factors that help us predict the ground thermal state in future scenarios. For other related data, please refer to Jin et al., 2024
", "ds_quality": "The RMSE of the output result of the ensemble average mode is 1.01 ℃, MAE is 0.69 ℃, and R is 0.89.
", "ds_ref_way": "", "ds_abstract": "The annual average ground temperature in permafrost regions is another important indicator for studying permafrost changes. In previous studies, there have been many explorations on the changes in annual average ground temperature over historical periods based on field measured data and remote sensing inversion algorithms, but their spatial resolution is relatively low. Based on CMIP6 data, obtain surface temperature in permafrost regions with a resolution of 1km through downscaling methods; Then, this data is used as a high-precision and high-resolution input variable for the future variation of multi-year average ground temperature, further obtaining a 1km resolution annual average ground temperature in the northern hemisphere. By using multiple machine learning methods to obtain multi-mode averages, the accuracy and resolution of the annual average ground temperature dataset are improved. The RMSE of the output result of the ensemble average mode is 1.01 ℃, MAE is 0.69 ℃, and R is 0.89.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The machine learning models used include logistic regression (LR), random forest (RF), and LightGBM (LGB). Considering that using a single method may lead to overfitting in the simulation, we used the set and average results of the three methods mentioned above when simulating MAGT. Using TDD, FDD, leaf area index, precipitation, snow cover, solar radiation, soil moisture content, soil organic matter, and gravel volume content as input variables, corresponding to station observations, the model was established with 90% of the data as the test set and 10% of the data as the validation set to evaluate the accuracy of the model. In order to reduce the uncertainty of a single run, this study ran the three machine learning models 100 times and used their ensemble average results to simulate the distribution changes of annual average ground temperature in permafrost areas at different periods by combining environmental variables at each period.
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