摘要：Monitoring the thermal state of permafrost (TSP) is important in many environmental science and engineering applications. However, such data are generally unavailable, mainly due to the lack of ground observations and the uncertainty of traditional physical models. This study produces novel permafrost datasets for the Northern Hemisphere (NH), including predictions of the mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA) (approximately 3 to 25 m) and active layer thickness (ALT) with 1 km resolution for the period of 2000–2016, as well as estimates of the probability of permafrost occurrence and permafrost zonation based on hydrothermal conditions. These datasets integrate unprecedentedly large amounts of field data (1002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modeling with an ensemble strategy. Thus, the resulting data are more accurate than those of previous circumpolar maps (bias = 0.02±0.16 ∘C and RMSE = 1.32±0.13 ∘C for MAGT; bias = 2.71±16.46 cm and RMSE = 86.93±19.61 cm for ALT). The datasets suggest that the areal extent of permafrost (MAGT ≤0 ∘C) in the NH, excluding glaciers and lakes, is approximately 14.77 (13.60–18.97) × 106 km2 and that the areal extent of permafrost regions (permafrost probability >0) is approximately 19.82×106 km2. The areal fractions of humid, semiarid/subhumid, and arid permafrost regions are 51.56 %, 45.07 %, and 3.37 %, respectively. The areal fractions of cold (≤−3.0≤-3.0≤-3.0 ∘C), cool (−3.0 ∘C to −1.5 ∘C), and warm (>−1.5>-1.5>-1.5 ∘C) permafrost regions are 37.80 %, 14.30 %, and 47.90 %, respectively. These new datasets based on the most comprehensive field data to date contribute to an updated understanding of the thermal state and zonation of permafrost in the NH. The datasets are potentially useful for various fields, such as climatology, hydrology, ecology, agriculture, public health, and engineering planning. All of the datasets are published through the National Tibetan Plateau Data Center (TPDC), and the link is https://doi.org/10.11888/Geocry.tpdc.271190 (Ran et al., 2021a).
