This set of files includes downscaled future projections of vapor pressure (units=hPa) at a 1km spatial scale. This data has been prepared as model input for the Integrated Ecosystem Model (IEM). There can be errors or serious limitations to the application of this data to other analyses. The data constitute the result of a downscaling procedure using 2 General Circulation Models (GCM) from the Coupled Model Intercomparison Project 5 (CMIP5) for RCP 8.5 scenario (2006-2100) monthly time series and Climatic Research Unit (CRU) TS2.0 (1961-1990,10 min spatial resolution) global climatology data. 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. RCPs: 8.5 Centers, Model Names, Versions, and Acronyms: National Center for Atmospheric Research,Community Earth System Model 4,NCAR-CCSM4 Meteorological Research Institute,Coupled General Circulation Model v3.0,MRI-CGCM3 Methods of creating downscaled relative humidity data: 1. The GCM input data are distributed as relative humidity along with the CRU CL 2.0, therefore no conversion procedure was necessary before beginning the downscaling procedure. 2. Proportional Anomalies generated using the 20c3m Historical relative humidity data 1961-1990 climatology and the projected relative humidity data (2006-2100). 3. These proportional anomalies are interpolated using a spline interpolation to a 10min resolution grid for downscaling with the CRU CL 2.0 Relative Humidity Data. 4. The GCM proportional anomalies are multiplied by month to the baseline CRU CL 2.0 10min relative humidity climatology for the period 1961-1990. Creating a downscaled relative humidity projected time series 2006-2100. 5. Due to the conversion procedure and the low quality of the input data to begin with, there were values that fell well outside of the range of acceptable relative humidity (meaning that there were values >100 percent), these values were re-set to a relative humidity of 95 at the suggestion of the researchers involved in the project. It is well known that the CRU data is spotty for Alaska and the Circumpolar North, due to a lack of weather stations and poor temporal coverage for those stations that exist. 6. The desired output resolution for the AIEM modeling project is 1km, so the newly created downscaled time series is resampled to this resolution using a standard bilinear interpolation resampling procedure. 7. The final step was to convert the downscaled relative humidity data to vapor pressure using the calculation below, which uses a downscaled temperature data set created utilizing the same downscaling procedure. EQUATION: saturated vapor pressure = 6.112 x exp(17.62 x temperature/(243.12+temperature)) vapor pressure = (relative humidity x saturated vapor pressure)/100

This dataset consists of spatial representations of vegetation types produced through summarization of ALFRESCO model outputs. These specific outputs are from the Integrated Ecosystem Model (IEM) project, AR5/CMIP5 climate inputs (IEM Generation 2). ALFRESCO outputs were summarized over three future eras (2010-2039, 2040-269, 2070-2099) and a historical era (1950-2008). Both the proportions of all possible vegetation types and the modal vegetation type (most common type over a given era) are available as sub-datasets. Each are summarized over two future emissions scenarios for five CMIP5 models.

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.

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.

This dataset includes 42,120 GeoTIFFs (spatial resolution: 12 km) that represent decadal (15 decades between 1950-2099) means of monthly summaries of the following variables (units, abbreviations and case match those used in the source daily resolution dataset). There are three distinct groups of variables: Meteorological, Water State, and Water Flux. Meteorological Variables - tmax (Maximum daily 2-m air temperature, °C) - tmin (Minimum daily 2-m air temperature, °C) - pcp (Daily precipitation, mm per day) Water State Variables - SWE (Snow water equivalent, mm) - IWE (Ice water equivalent, mm) - SM1 (Soil moisture layer 1: surface to 0.02 m depth, mm) - SM2 (Soil moisture layer 2: 0.02 m to 0.97 m depth, mm) - SM3 (Soil moisture layer 3: 0.97 m to 3.0 m depth, mm) Water Flux Variables - RUNOFF (Surface runoff, mm per day) - EVAP (Actual evapotranspiration, mm per day) - SNOW_MELT (Snow melt, mm per day) - GLACIER_MELT (Ice melt, mm per day) Monthly summary functions, or how the daily frequency source data are condensed into a single monthly value, are as follows: - Sum: pcp, SNOW_MELT, EVAP, GLACIER_MELT, RUNOFF - Mean: tmin, tmax, SM1, SM2, SM3 - Maximum: IWE, SWE The model-scenario combinations used to represent various plausible climate futures are: - ACCESS1-3, RCP 4.5 - ACCESS1-3, RCP 8.5 - CanESM2, RCP 4.5 - CanESM2, RCP 8.5 - CCSM4, RCP 4.5 - CCSM4, RCP 8.5 - CSIRO-Mk3-6-0, RCP 4.5 - CSIRO-Mk3-6-0, RCP 8.5 - GFDL-ESM2M, RCP 4.5 - GFDL-ESM2M, RCP 8.5 - HadGEM2-ES, RCP 4.5 - HadGEM2-ES, RCP 8.5 - inmcm4, RCP 4.5 - inmcm4, RCP 8.5 - MIROC5, RCP 4.5 - MIROC5, RCP 8.5 - MPI-ESM-MR, RCP 4.5 - MPI-ESM-MR, RCP 8.5 - MRI-CGCM3, RCP 4.5 - MRI-CGCM3, RCP 8.5 The .zip files that are available for download are organized by variable. One .zip file has all the models and scenarios and decades and months for that variable. Each GeoTIFF file has a naming convention like this: {climate variable}_{units}_{model}_{scenario}_{month abbreviation}_{summary function}_{decade start}-{decade end}_mean.tif Each GeoTIFF has a 12 km by 12 km pixel size, and is projected to EPSG:3338 (Alaska Albers).

These annual fire history grids (0=no fire, 1=fire) were produced directly from the BLM Alaska Fire Service database and the Canadian National Fire Database. They are simply a 1x1km raster representation of their fire history polygon database that can be obtained from: http://fire.ak.blm.gov/predsvcs/maps.php http://cwfis.cfs.nrcan.gc.ca/datamart Note, fire history data is very unreliable before ~1950 in Alaska. Fires may have been recorded in a given year, but that does not mean all fires that occurred were successfully recorded. This data was assembled from every recorded fire that has been entered into Alaska and Canadian databases. This results in several years containing no fires at all.

This set of files includes downscaled projected estimates of monthly temperature (in degrees Celsius, no unit conversion necessary) from 2006-2300* 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. *Some datasets from the five models used in modeling work by SNAP only have data going out to 2100. This metadata record serves to describe all of these models outputs for the full length of future time available. The downscaling process utilizes CRU CL v. 2.1 climatological datasets from 1961-1990 as the baseline for the Delta Downscaling method.

A dataset of landfast ice extent along the Alaska coast of the Chukchi Sea and adjacent waters in Russia, spanning the winters of 1996-2023. Landfast ice extent is defined as the area between the coast and the seaward landfast ice edge (SLIE), meaning that small areas of open water than can form at the coast springtime will not be represented. Spatial resolution is 100 m. Compilation of the dataset is described in detail by Mahoney et al (2024). In brief, it is derived from three sources: From 1996-2008, the dataset is derived from analysis of sequential synthetic aperture radar (SAR) images from the RadarSAT and EnviSAT constellations, as described by Mahoney et al (2014); From 2008-2023, the data represent an average landfast extent identified in ice charts from the U.S. National Weather Service Alaska Sea Ice Program (ASIP) and the U.S. National Ice Center (NIC). Within each GeoTIFF file there are 5 different pixel values representing different characteristics: 0 - Not Landfast Ice 32 - Coast Vector Shadow 64 - Out of Bounds 128 - Land 255 - Landfast ice The file naming convention is as follows: chukchi_$YYYYMMDD_$source_slie.tif For example, the name chukchi_20170302_asip_and_nic_average_slie.tif indicates the file represents data for March 2, 2017 and that the data is derived from an average of the ASIP and NIC data sources. 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.

These files include climatological summaries of downscaled historical and projected decadal average monthly derived snow variables and summaries at 771 meter spatial resolution across Alaska. There are three types of files: 1). The historical and future snowfall water equivalent (SWE) in millimeters, produced by multiplying snow-day fraction by decadal average monthly precipitation and summing over 6 months from October to March to estimate the total SWE on April 1. 2). The historical and future ratio of SWE to total precipitation (SFEtoP) in percent. SFEtoP is calculated as (SWE / total precipitation) and also represents the six month October to March period. 3). The future difference in SWE with respect to the historical baseline (dSWE) in percent. dSWE is calculated as ((future SWE – historical SWE) / historical SWE) * 100. These data are also summary for the six month October to March period. The historical baseline period is 1970-1999, (file naming convention “H70.99”) and data are calculated from downscaled CRU TS 3.1 data. Projected variables exist for RCP 4.5 and RCP 8.5 emission scenarios and for 5 GCMs: NCAR-CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5, and MRI-CGCM3. The 5-model mean (file naming convention "5MM") was also computed. Projections exist for three thirty-year climatologies: the 2020s (2010-2039), the 2050s (2040-2069), and the 2080s (2070-2099). The snow-day fraction data used can be found here: http://ckan.snap.uaf.edu/dataset/projected-decadal-averages-of-monthly-snow-day-fraction-771m-cmip5-ar5 http://ckan.snap.uaf.edu/dataset/historical-decadal-averages-of-monthly-snow-day-fraction-771m-cru-ts3-0-3-1 The precipitation data used can be found here: http://ckan.snap.uaf.edu/dataset/projected-monthly-and-derived-precipitation-products-771m-cmip5-ar5 http://ckan.snap.uaf.edu/dataset/historical-monthly-and-derived-precipitation-products-771m-cru-ts Note: In Littell et al. 2018, "SWE" is referred to as "SFE", and "SFEtoP" as "SFE:P"