This dataset consists of single band GeoTIFFs containing total annual counts of wet days for each year from 1980-2100 for one downscaled reanalysis (ERA-Interim, 1980-2015) and two downscaled CMIP5 global climate models driven under the RCP 8.5 baseline emissions scenario (NCAR-CCSM4 and GFDL-CM3, 2006-2100), all derived from the same dynamical downscaling effort using the Weather Research and Forecasting (WRF) model (Version 3.5). A day is counted as a "wet day" if the total precipitation for that day is 1 mm or greater.

These data contain historical and future projections of percent land cover from 1950 to 2100, as simulated by the Alaska Thermokarst Model for the boreal region. The data are 1km spatial resolution. The climate data used to drive the model simulations are from the Climate Research Unit, Time Series 4.0 (CRU_TS40) for the historical period (1950 to 2015), and from RCP 8.5 and global circulation models NCAR-CCSM4 and MRI-CGCM3 for the projected period (2016 to 2100). Datasets from the years 1950, 2000, 2050, and 2100 are provided. Annual maps are available upon request. Please note that this data is used to fill in a gap in available data for the Integrated Ecosystem Model (IEM) and does not constitute a complete or precise measurement of this variable in all locations. If used for publication, these data should be cited as the following: Genet H., Lara M., McGuire A.D., Jorgenson T.M., Euskirchen E.S., Clein J., Carman T., Rutter R., Rupp S., Breen A., Kurkowski T., Bennett A., Torgenson B., Romanovski V., Marchenko S. 2018. Land cover dynamic in the Tanana Flats from 1950 to 2100 driven by thermokarst activity.

This data set includes weekly (January 1954 to December 2013) and monthly (January 1850 to December 2022) midpoint historical sea ice concentration (0 - 100%) estimates at 1/4 x 1/4 degree spatial resolution for the ocean region around the state of Alaska, USA. This value-added dataset was developed by compiling the below historical data sources into spatially and temporally standardized datasets. Gaps in temporal or spatial resolutions were filled in with spatial and temporal analog month approaches. This dataset is no longer being updated. The NSIDC provides a new version in netCDF format receiving ongoing updates: https://nsidc.org/data/nsidc-0051/versions/2.

This set of files includes downscaled historical estimates of monthly total precipitation (in mm, no unit conversion necessary) from 1901 - 2005, at 15km x 15km spatial resolution. They include data for Alaska and Western Canada. Each set of files originates from one of five top ranked global circulation models from the CMIP5/AR5 models and RCPs, or is calculated as a 5 Model Average. These outputs are from the Historical runs of the GCMs. The downscaling process utilizes CRU CL v. 2.1 climatological datasets from 1961-1990 as the baseline for the Delta Downscaling method.

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).

This data set consists of PRSIM mean air temperature climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are degrees Celsius. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

This set of files includes downscaled projections of monthly totals, and derived annual, seasonal, and decadal means of monthly total precipitation (in millimeters, no unit conversion necessary) from Jan 2006 - Dec 2100 at 771x771 meter spatial resolution. 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. The downscaling process utilizes PRISM climatological datasets from 1971-2000. Brief descriptions of the datasets: Monthly precipitation totals: The total precipitation, in mm, for the month. For Decadal outputs: 1. Decadal Average Total Monthly Precipitation: 10 year average of total monthly precipitation. Example: All January precipitation files for a decade are added together and divided by ten. 2. Decadal Average Seasonal Precipitation Totals: 10 year average of seasonal precipitation totals. Example: MAM seasonal totals for every year in a decade are added together and divided by ten. 3. Decadal Average Annual Precipitation Totals: 10 year average of annual cumulative precipitation. For seasonal means, the four seasons are referred to by the first letter of 3 months making up that season: * `JJA`: summer (June, July, August) * `SON`: fall (September, October, November) * `DJF`: winter (December, January, February) * `MAM`: spring (March, April, May) 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.

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 771x771 meter spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data. ---- The spatial extent includes Alaska. 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.