Ensemble Dataset of Permafrost Thaw Conditions, Northern High Latitudes (>45°N)

Ensemble Dataset of Permafrost Conditions, Northern High Latitudes This ensemble dataset includes three raster layers that describe permafrost thaw conditions across the northern high latitudes (> 45°N). All raster layers are resampled to a 1,000 m pixel size in the North Pole Lambert Azimuthal Equal Area Projection (False Northing = 0.0°; False Easting = 0.0°; Central Meridian = 0.0°; Latitude of Origin = 90.0°), WGS84 Datum. Each raster layer is provided in GeoTIFF format. An ArcGIS Pro layer file (.lyrx) is included for each raster layer to maintain the color legend. The primary raster layer in this dataset is the Permafrost Thaw Index (PTI). This index measures permafrost thaw potential using a two-tier ranking system based on two variables: mean annual ground temperature (MAGT; Tier 1) and the trends of MAGT and active layer thickness (Tier 2). The PTI classifies permafrost into 10 ranks, with three ranks in each tier. Rank 11 represents the most stable permafrost, while Rank 33 indicates permafrost with the highest thaw potentials. Additionally, Rank 40 is defined to depict isolated patches located at the southernmost extent, which are immediately susceptible to thaw under current climate conditions. The PTI map is generated using an ensemble approach from a set of open-access satellite datasets. Three additional permafrost condition maps are derived by integrating multiple published products to address inter-product inconsistencies. 1 Permafrost PP Ensemble: This raster layer is a base layer of permafrost existence in percentage. It integrates three published permafrost map products, all of which estimate permafrost percent (PP): the Obu map (Obu et al., 2018); the NIEER map (Northwest Institute of Eco-Environment and Research, China) (Ran et al., 2022), and the annual European Space Agency (ESA) Climate Change Initiative Permafrost_CCI map series (1997–2021) (Westermann et al., 2024). A mean filter is applied to the annual Permafrost_CCI series to produce a representative Permafrost_CCI. Areas with PP > 0% in at least two products are considered permafrost existence, and the ensemble PP is calculated as the median of these three products. 2 Permafrost MAGT Ensemble: This raster layer provides spatial distribution of MAGT. It also integrates the three aforementioned permafrost products. The Obu and NIEER maps estimate MAGT at the top of the permafrost table. The annual Permafrost_CCI series estimates ground surface temperature (GST) at multiple depths. In this study, the GST at 2 m depth is used as an MAGT proxy, and a representative Permafrost_CCI MAGT is extracted by averaging across the 25-year period. The ensemble MAGT is calculated as the median of these three products. 3 Permafrost Land Cover Ensemble: This raster layer represents land cover distribution in permafrost lands. It integrates three published global and regional land cover maps: (1) ESA LandCover_CCI (ESA LandCover_CCI Project Team and Defourny, 2019); (2) Global Land Cover Characteristics (GLCC) Global Ecosystems map (USGS EROS, 2018); and (3) Circumpolar Arctic Vegetation Map (CAVM) (Raynolds and Walker, 2022). In the ensemble process, a majority filter is applied to the annual LandCover_CCI maps (2018-2022) to extract the most frequently classified land cover type during this period. Following the GLCC classification scheme, classes are merged or re-coded, if necessary, to maintain consistency in ecological characteristics. In the High Arctic tundra, five tundra classes are specifically extracted from the CAVM map: barren tundra, herbaceous tundra, dwarf shrub tundra, low shrub tundra, and wetland tundra. This dataset is produced by Harbin Normal University, Heilongjiang Province, China. References of published open-access datasets: ESA Land Cover CCI project team; Defourny, P.: ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis (CEDA), https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c, 2019. Obu, J., Westermann, S., Kääb, A., and Bartsch, A.: Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost [dataset]. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.888600, 2018. Ran, Y., Li, X., Cheng, G., Che, J., Aalto, J., Karjalainen, O., Hjort, J., Luoto, M., Jin, H., Obu, J., Hori, M., Yu, Q., and Chang, X.: New high-resolution estimates of the permafrost thermal state and hydrothermal conditions over the Northern Hemisphere. Earth System Science Data, 14, 865–884. https://doi.org/10.11888/Geocry.tpdc.271190, 2022. Raynolds, M., and Walker, D.: Raster Circumpolar Arctic Vegetation Map, Mendeley Data (Version 2) [Dataset]. Mendeley Data, https://doi.org/10.17632/c4xj5rv6kv.2, 2022. U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center: USGS EROS Archive – Land Cover Products – Global Land Cover Characterization (GLCC) (archived July 11, 2018). U.S. Geological Survey, https://doi.org/10.5066/F7GB230D, 2018.