A landfast ice dataset along the Chukchi Sea continental shelf, spanning 1996-2023. Spatial resolution is 100 m. Each month of the ice season (October through July) is summarized over three 9-year periods (1996-2005, 2005-2014, 2014-2023) using the minimum, maximum, median, and mean distance of SLIE from the coastline. The minimum extent indicates the region that was always occupied by landfast ice during a particular calendar month. The median extent indicates where landfast occurred at least 50% of the time. The maximum extent represents regions that may only have been landfast ice on one occasion during the selected time period. The mean SLIE position for the each month and and time period is also included. The dataset is derived from three sources: seaward landfast ice images derived from synthetic aperture radar images from the RadarSAT and EnviSAT constellations (1996-2008), the Alaska Sea Ice Program (ASIP) ice charts (2008-2017, 2019-2022), and the G10013 SIGID-3 Arctic Ice Charts produced by the National Ice Center (NIC; 2017-2019, 2022-2023). Within each GeoTIFF file there are 8 different pixel values representing different characteristics: 0 - Ocean 1 - Maximum Landfast Ice Extent 2 - Median Landfast Ice Extent 3 - Minimum Landfast Ice Extent 4 - Mean Landfast Ice Edge 5 - Land 6 - Out of Domain 7 - Coast Vector Shadow The file naming convention is as follows: Chukchi_$month_$era_SLIE_MMM_summary.tif For example, the name Chukchi_05_2005-2014_SLIE_MMM_summary.tif indicates the file represents data for May 2005-2014. These data were updated on August 21, 2025 to rectify the omission of some NIC chart data sources for the 2017-18 and 2018-19 seasons.

A landfast ice dataset along the Beaufort Sea continental shelf, spanning 1996-2023. Spatial resolution is 100 m. Each month of the ice season (October through July) is summarized over three 9-year periods (1996-2005, 2005-2014, 2014-2023) using the minimum, maximum, median, and mean distance of SLIE from the coastline. The minimum extent indicates the region that was always occupied by landfast ice during a particular calendar month. The median extent indicates where landfast occurred at least 50% of the time. The maximum extent represents regions that may only have been landfast ice on one occasion during the selected time period. The mean SLIE position for the each month and and time period is also included. The dataset is derived from three sources: seaward landfast ice images derived from synthetic aperture radar images from the RadarSAT and EnviSAT constellations (1996-2008), the Alaska Sea Ice Program (ASIP) ice charts (2008-2017, 2019-2022), and the G10013 SIGID-3 Arctic Ice Charts produced by the National Ice Center (NIC; 2017-2019, 2022-2023). Within each GeoTIFF file there are 8 different pixel values representing different characteristics: 0 - Ocean 1 - Maximum Landfast Ice Extent 2 - Median Landfast Ice Extent 3 - Minimum Landfast Ice Extent 4 - Mean Landfast Ice Edge 5 - Land 6 - Out of Domain 7 - Coast Vector Shadow The file naming convention is as follows: Beaufort_$month_$era_SLIE_MMM_summary.tif For example, the name Beaufort_05_2005-2014_SLIE_MMM_summary.tif indicates the file represents data for May 2005-2014. These data were updated on August 21, 2025 to rectify the omission of some NIC chart data sources for the 2017-18 and 2018-19 seasons.

This set of files includes downscaled projections of decadal means of annual day of freeze or thaw (ordinal day of the year), and length of growing season (numbers of days, 0-365) for each decade from 2010 - 2100 at 2km x 2km meter spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data. ---- The spatial extent includes Alaska, the Yukon Territory, British Columbia, Alberta, Saskatchewan, and Manitoba. Each set of files originates from one of five top ranked global circulation models from the CMIP5/AR5 models and RPCs, or is calculated as a 5 Model Average. Day of Freeze, Day of Thaw, Length of Growing Season calculations: Estimated ordinal days of freeze and thaw are calculated by assuming a linear change in temperature between consecutive months. Mean monthly temperatures are used to represent daily temperature on the 15th day of each month. When consecutive monthly midpoints have opposite sign temperatures, the day of transition (freeze or thaw) is the day between them on which temperature crosses zero degrees C. The length of growing season refers to the number of days between the days of thaw and freeze. This amounts to connecting temperature values (y-axis) for each month (x-axis) by line segments and solving for the x-intercepts. Calculating a day of freeze or thaw is simple. However, transitions may occur several times in a year, or not at all. The choice of transition points to use as the thaw and freeze dates which best represent realistic bounds on a growing season is more complex. Rather than iteratively looping over months one at a time, searching from January forward to determine thaw day and from December backward to determine freeze day, stopping as soon as a sign change between two months is identified, the algorithm looks at a snapshot of the signs of all twelve mean monthly temperatures at once, which enables identification of multiple discrete periods of positive and negative temperatures. As a result more realistic days of freeze and thaw and length of growing season can be calculated when there are idiosyncrasies in the data. Please note that these maps represent climatic estimates only. While we have based our work on scientifically accepted data and methods, uncertainty is always present . Uncertainty in model outputs tends to increase for more distant climatic estimates from present day for both historical summaries and future projections.

This dataset consists of observed and modeled wind data at an hourly temporal resolution for 67 communities in Alaska. Hourly ASOS/AWOS wind data (speed and direction) available via the Iowa Environmental Mesonet AK ASOS network were accessed and assessed for completeness, and 67 of those stations were determined to be sufficiently complete for climatological analysis. Those data were cleaned to produce regular hourly data, and adjusted via a combination of changepoint analysis and quantile mapping to correct for potential changes in sensor location and height. Historical (ERA-Interim reanalysis) and projected (GFDL-CM3 and NCAR-CCSM4) outputs from a dynamical downscaling effort were extracted at pixels intersecting the chosen communities and were bias-corrected using the cleaned station data. This bias-corrected historical and projected data along with cleaned station data make up the entirety of this dataset as a collection of CSV files, for each combination of community and origin (station or model name).

This dataset consists of spatial representations of relative vegetation change produced through summarization of ALFRESCO model outputs. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR5/CMIP5 climate inputs (IEM Generation 2).

Annual maximum series-based precipitation frequency estimates with 90% confidence intervals for Alaska derived from WRF-downscaled reanalysis (ERA-Interim) and CMIP5 GCM (GFDL-CM3, NCAR-CCSM4) precipitation data with the RCP 8.5 scenario. Estimates and confidence intervals are based on exceedance probabilities and durations used in the NOAA Atlas 14 study. Projections are present for three future time periods: 2020-2049, 2050-2079, and 2080-2099.

These files include historical downscaled estimates of decadal average monthly snow-day fraction ("fs", units = percent probability from 1 – 100) for each month of the decades from 1900-1909 to 2000-2009 at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Version 1.0 was completed in 2015 using CMIP3. Version 2.0 was completed in 2018 using CMIP5. For more information on the methodology used to create this dataset, and guidelines for appropriate usage of the dataset, please see the data user's guide here: http://data.snap.uaf.edu/data/Base/AK_771m/historical/CRU_TS/snow_day_fraction/snow_fraction_data_users_guide.pdf

This dataset represents the results of a project that compiled available range information for three taxonomic groups representing 211 species (159 birds, 45 mammals, and 5 amphibians) identified as Species of Greatest Conservation Need (SGCN) by the 2015 Alaska Wildlife Action Plan (SWAP) Appendix A (https://www.adfg.alaska.gov/index.cfm?adfg=wildlifediversity.swap) in addition to 2 amphibian species native to Alaska. The goal of this effort was to create an initial set of statewide heatmaps of SGCN richness. Files include: (1) a set of 21 species richness heat maps depicting the sum of overlapping range maps from multiple SGCNs; (2) shapefiles of species range maps for Alaska’s terrestrial SGCN, with all species ranked (high, moderately high, moderate, low) in terms of relative conservation and management priority based on the Alaska Species Ranking System (ASRS; https://accs.uaa.alaska.edu/wildlife/alaska-species-ranking-system); (3) shapefiles of species in decline for birds and marine mammals (as listed in SWAP Appendix A); and (4) a file that cross-walks each SGCN by species code, common name, and scientific name. Complete information describing how environmental variables correlated with species richness is provided in the final report (http://data.snap.uaf.edu/data/Base/Other/Species/State_Wildlife_Grant_Final_Report_20Sept24.pdf). Species richness maps were derived from species-specific, 6th-level hydrologic unit (HUC12) occupancy maps developed by the Alaska Gap Analysis Project (https://accscatalog.uaa.alaska.edu/dataset/alaska-gap-analysis-project). Hotspot maps highlight all HUCs containing more than 60% of considered amphibian species or 80% of the maximum number of co-occurring bird or mammal species. Species richness values were derived by summing the number of species with overlapping ranges. A gradient boosting machine algorithm quantified relationships between SGCN hotspots and a set of 24 climatic, topographic, and habitat predictors. It is important to note that species ranges are modeled and extrapolated from limited data. They may be affected by changes in our understanding of species' ranges, changes in taxonomy, and changes in what we consider to be the best tools and data for creating distribution models using presence-only data, and may overestimate actual ranges. These datasets and any associated maps and other products are intended to provide a landscape-level overview only. It is highly recommended that any use of these datasets be undertaken in conjunction with expert advice from the Alaska Department of Fish and Game (see contact information below).

This dataset includes downscaled historical estimates of monthly average, minimum, and maximum precipitation and derived annual, seasonal, and decadal means of monthly total precipitation (in millimeters, no unit conversion necessary) from 1901 to 2006 (CRU TS 3.0), 2009 (CRU TS 3.1), 2015 (CRU TS 4.0), 2020 (CRU TS 4.05), or 2023 (CRU TS 4.08) at 2km x 2km spatial resolution. CRU TS 4.0 is only available as monthly averages, minimum, and maximum files. CRU TS 4.05 and 4.08 data are only available as monthly averages. The downscaling process utilizes PRISM climatological datasets from 1961-1990.

Mean temperature and precipitation values extracted at community locations across Alaska and Canada from downscaled raster datasets containing historical and projected estimates for these variables.