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 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.

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 set of files includes downscaled projections of monthly means, and derived annual, seasonal, and decadal means of monthly mean temperatures (in degrees Celsius, no unit conversion necessary) from Jan 2006 - Dec 2100 at 771x771 meter spatial resolution. 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) The downscaling process utilizes PRISM climatological datasets from 1971-2000. 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.

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.

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.

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 dataset consists of four different sub-datasets: degree days below 65°F (or "heating degree days"), degree days below 0°F, degree days below 32°F (or "freezing index"), and degree days above 32°F (or "thawing index"). All were derived from the same dataset of outputs from dynamically downscaling one reanalysis (ERA-Interim) and two CMIP5 GCMs (GFDL-CM3, NCAR-CCSM4) over Alaska using the Weather Research and Forecasting model (WRF). Data from the GCMs are driven exclusively by the RCP 8.5 emissions scenario. Heating degree days, degree days below 0°F, and freezing index were computed in the following way: subtract the daily mean temperature values from the threshold value and compute the sum of this time series for the given calendar year. Thawing index is instead computed as the annual sum of the quantities resulting from subtracting the threshold (32°F) from the daily mean temperature values.

This dataset includes PRISM derived 1961-1990 climatologies of monthly average, maximum, and minimum temperature and total precipitation across Alaska and Western Canada including the Yukon, British Columbia, Alberta, Saskatchewan, and Manitoba. These were obtained from the PRISM Climate Group and mosaicked into a single continuous transboundary extent. Please cite the PRISM Climate Group when using this data.

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.