This set of files includes downscaled historical estimates 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 1910 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) at at 771 x 771 meter spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data.

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.

This dataset contains climate "indicators" (also referred to as climate indices or metrics) computed over one historical period (1980-2009) using the NCAR Daymet dataset, and two future periods (2040-2069, 2070-2099) using two statistically downscaled global climate model projections, each run under two plausible greenhouse gas futures (RCP 4.5 and 8.5). The indicators within this dataset include: hd: “Hot day” threshold -- the highest observed daily maximum 2 m air temperature such that there are 5 other observations equal to or greater than this value. cd: “Cold day” threshold -- the lowest observed daily minimum 2 m air temperature such that there are 5 other observations equal to or less than this value. rx1day: Maximum 1-day precipitation su: "Summer Days" –- Annual number of days with maximum 2 m air temperature above 25 C dw: "Deep Winter days" –- Annual number of days with minimum 2 m air temperature below -30 C wsdi: Warm Spell Duration Index -- Annual count of occurrences of at least 5 consecutive days with daily mean 2 m air temperature above 90th percentile of historical values for the date cdsi: Cold Spell Duration Index -- Same as WDSI, but for daily mean 2 m air temperature below 10th percentile rx5day: Maximum 5-day precipitation r10mm: Number of days with precipitation > 10 mm cwd: Consecutive wet days –- number of the most consecutive days with precipitation > 1 mm cdd: Consecutive dry days –- number of the most consecutive days with precipitation < 1 mm

This dataset is the product of a climate-driven model of beetle survival and reproduction in Alaska. We used that model to create this dataset of landscape-level “risk” of the climatic component of beetle infestation across the forested areas of Alaska. This risk component can best be applied as protection of the landscape offered by the climate and is categorized as high, medium, and low. It does not consider other major factors, such as existing beetle and predator populations or forest susceptibility. We computed these values over one historical period (1988-2017) using Daymet data, and three future periods (2010-2039, 2040-2069, 2070-2099) using four statistically downscaled global climate model projections, each run under two plausible greenhouse gas futures (RCP 4.5 and 8.5).

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 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 dataset of landfast ice extent along the Alaska coast of the Beaufort Sea and adjacent waters in Canada 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: beaufort_$YYYYMMDD_$source_slie.tif For example, the name beaufort_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.

This data includes quantile-mapped historical and projected model runs of AR5 daily mean near surface wind velocity (m/s) for each day of every year from 1958 - 2100 at 2.5 x 2.5 degree spatial resolution across 3 AR5 models. They are 365 multi-band geotiff files, one file per year, each band representing one day of the year, with no leap years.

This set of files includes downscaled historical estimates 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 1910 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) at 2x2 kilometer spatial resolution. Each file represents a decadal mean of an annual mean calculated from mean monthly data. **Day of freeze or thaw units are ordinal day 15-350 with the below special cases.** *Day of Freeze (DOF)* `0` = Primarily Frozen `365` = Rarely Freezes *Day of Thaw (DOT)* `0` = Rarely Freezes `365` = Primarily Frozen *Length of Growing Season (LOGS)* is simply the number of days between the DOT and DOF. ---- The spatial extent includes Alaska, the Yukon Territories, British Columbia, Alberta, Saskatchewan, and Manitoba. Each set of files originates from the Climatic Research Unit (CRU, http://www.cru.uea.ac.uk/) TS 3.0 or 3.1 dataset. TS 3.0 extends through December 2006 while 3.1 extends to December 2009. **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.

These GeoTIFFs include annual spatial representations of the following variables produced through summarization of ALFRESCO model outputs across 200 replicates: Flammability: likelihood of a pixel to burn across 200 replicates Modal vegetation type: statistical mode of vegetation type across 200 replicates Percent vegetation type: percent of each possible vegetation type across 200 replicates These outputs were derived from AR5/CMIP5 climate inputs, historical fire inputs from the Alaska Interagency Coordination Center (AICC), and several fire management options (FMO) inputs.