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The data publication contains supplementary data to the article "Supplementary Dataset to: The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: Fast-forward into the future" This data publication includes four datasets: 1. Lake change datasets for 1999-2014 and 2017-2018 based on Landsat and Sentinel-1 data as Polygon Shapefiles 2. Lake change datasets for 2017 and 2018 based on high-temporal resolution PlanetScope imagery as Polygon Shapefiles and csv. 3. Lake ice simulations for the study area for 1980-2018. 4. Study sites in two versions: a) including seawater and b) clipped to land area. Files are Polygon Shapefiles. The datasets cover the land area of the Baldwin Peninsula and northern Seward Peninsula in north-western Alaska. The datasets are (#1) remote sensing based observations and (#3) modelled data. Methods are described in detail in the original manuscript (open access). Dataset #4 is the extent of the study site in two versions, a) full extent including seawater and b) land only including lakes. The land boundary was clipped with the “Global Self-consistent, Hierarchical, High-resolution Geography Database” (GSHHG; Wessel and Smith, 1996) dataset in scale “h”. The datasets cover different temporal periods and have a different temporal resolution. Data were collected to measure the extent of a rapid and widespread thermokarst lake drainage event in northwestern Alaska in 2018 and to compare the affected number of lakes and area to previous periods. Lake-ice model data were calculated to simulate lake-ice conditions since 1980 and to put the lake-ice and weather conditions in 2017/2018 into context.

Narrowing uncertainties about carbon cycling is important in the Arctic where rapid environmental changes contribute to enhanced mobilization of carbon. Here we quantify soil organic carbon (SOC) contents of permafrost soils along the Yukon Coastal Plain and determine the annual fluxes from erosion. Different terrain units are assessed based on surficial geology, morphology, and ground ice conditions. To account for the volume of wedge ice and massive ice in a unit, sample SOC contents are reduced by 19% and sediment contents by 16%. The SOC content in a 1 m**2 column of soil varies according to the height of the bluff, ranging from 30 to 662 kg, with a mean value of 183 kg. Forty-four per cent of the SOC is within the top 1 m of soil and values vary based on surficial materials, ranging from 30 to 53 kg C/m**3, with a mean of 41 kg. Eighty per cent of the shoreline is erosive with a mean annual rate of change is 0.7 m/a. This results in a SOC flux per meter of shoreline of 131 kg C/m/a, and a total flux for the entire Yukon coast of 35.5 10**6 kg C/a (0.036 Tg C/a). The mean flux of sediment per meter of shoreline is 5.3 10**3 kg/m/a, with a total flux of 1,832.0 10**6 kg/a (1.832 Tg/a). Sedimentation rates indicate that approximately 13% of the eroded carbon is sequestered in nearshore sediments, where the overwhelming majority of organic carbon is of terrestrial origin. Supplement to: Couture, Nicole; Irrgang, Anna Maria; Pollard, Wayne H; Lantuit, Hugues; Fritz, Michael (2018): Coastal Erosion of Permafrost Soils Along the Yukon Coastal Plain and Fluxes of Organic Carbon to the Canadian Beaufort Sea. Journal of Geophysical Research: Biogeosciences

This dataset is part of the ESA Data User Element (DUE) Permafrost Full Product Set (doi:10.1594/PANGAEA.780111).The Land Surface Temperature (LST) products and services identified by users for the pan-Arctic (25 km resolution) scales include weekly and monthly averages from 2000 to 2010 from which annual averages can also be calculated. The LST processing integrates the LST level 2 products from MODIS and AATSR distributed by NASA and ESA, respectively. Post-processing functions supply University Waterloo-level-3 weekly and monthly LST products for regional (1 km) and pan-Arctic (25 km) scales. Thepan-Arctic product, with a spatial resolution of 25 km, is produced by spatial averaging of 1-km observations. MOD11_L2 and MYD11_L2 LST (Version 5 from NASA Terra and Aqua satellites) and ATS_NR_2P (from ESA Envisat satellite) products at 1 km resolution are used as input data to generate pan-Arctic and regional products. The original geo-located LST observations are characterized by an irregular distribution based on the satellite orbits. The Northern Hemisphere EASE-Grid Lambert Equal Area Azimuthal projection with a sphere datum (with a radius of 6371.228 km) was selected as the standard projection for the operational pan-Arctic and regional products. Original MODIS and AATSR LST level 2 observations are projected using the EASE-Grid coordinate system and interpolated to a regular EASE-Grid with 1 km spacing using triangulation. The EASE-Grid projection was chosen since this is the system adopted by the GlobSnow project and for most snow and ice products distributed by NSIDC. Local time is calculated using UTC acquisition time and longitude. UTC is extracted from ADS information for AATSR data and from the file name of MODIS level 2 (Terra and Aqua) products, yielding a temporal accuracy of ± 15 minutes, which is found to be sufficient for weekly and monthly products. Temporal aggregation is applied to both 1 km and 25 km data to produce weekly and monthly LST averages. Interpolated LST observations on a 1 km grid (regional product) and 25 km (pan-Arctic product) are aggregated into two bins; a day-time bin (from 6 a.m. to 6 p.m. local time) and a night-time bin (6 p.m. to 6 a.m. of the next day) within the aggregation period (week or month). The definition of day and night does not take in account the notion of polar darkness and does not consider the seasonal changes of day length. It was defined to force final products to have an equal number of observations around the day. A mid range average is calculated by taking the day-time and night-time average to avoid daily diurnal fluctuations during the week or month of interest. Known issues: the LST data are all measured during clear-sky conditions. The influence of clouds on surface temperature (e.g. temperature warmer under clouds in winter) is not reflected in the LSTs. This makes the LST colder than in reality due to the isolative effect of clouds. Each LST file contains 6 bands: the datafiles 001 to 006, bands 001, 003, 005 are the LST averages and bands 002, 004, 006 are supplementary quality information: Bands with averages of LST: 001 - Weekly or monthly aggregated average LST product based on equal weight of average day-time (003) and night-time (005) LST values. 003 - Average day-time weekly or monthly LST based on all cloud free observations falling during 6 a.m. to 6 p.m. local time. 005 - Average night-time weekly or monthly LST based on all cloud free observations falling into each pixel cell during 6 p.m. to 6 a.m. local time. Supplementary information bands: 002 - Number of LST cloud free observations falling into each pixel for the aggregation (weekly or monthly) period. Associated with LST file 001. 004 - Number of LST cloud free observations during day-time (6 a.m. to 6 p.m. local time) falling into each pixel. Associated with LST file 003. 006 - Number of LST cloud free observations during night-time (6 p.m. to 6 a.m. local time) falling into each pixel. Associated with LST file 005.