SNAP GeoNetwork
SNAP GeoNetwork (Scenarios Network for Alaska & Arctic Planning)
Scenarios Network for Alaska & Arctic Planning
Continents, countries, sea regions of the world.
Historical and Projected Decadal Average Monthly Snowfall Equivalent and the Ratio of Snowfall Equivalent to Precipitation 771m CMIP5/AR5/CRU TS3.1
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"
Annual ALFRESCO Model Outputs with Historical Fire Scars
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.
Historical Monthly Precipitation Products - 10 min CMIP5/AR5
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.
Beaufort Sea Landfast Sea Ice Extent Summarized by Month and 9-year Periods
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.
Historical Monthly Temperature Products - 10 min CMIP5/AR5
This set of files includes downscaled modeled historical estimates of monthly temperature (in degrees Celsius, no unit conversion necessary) from 1901 - 2005 at 15km x 15km spatial resolution. 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.
Historical and projected climate indicators for Alaska at 12km
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
Projected Monthly and Derived Temperature Products - 2km CMIP5/AR5
This set of files includes downscaled projections of monthly average, minimum, and maximum temperature and derived annual, seasonal, and decadal means of monthly average temperature (in degrees Celsius, no unit conversion necessary) from Jan 2006 - Dec 2100 at 2km x 2km spatial resolution across Alaska and parts of Canada. 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) 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 1961-1990. 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.
Treeline Maps - ALFRESCO Model Outputs - Linear Coupled
These are map products depicting modeled treeline dynamics. The left panel indicates modeled treeline dynamics from a single 2014 baseline year to the year 2100. The right panel indicates basal area accumulation on a 1km x 1km pixel basis during the year 2100, which gives an indication where possible further treeline advance may occur beyond 2100. The source datasets used to create these maps can be found here: https://catalog.snap.uaf.edu/geonetwork/srv/eng/catalog.search#/metadata/53b35453-7b88-4ea7-8321-5447f8926c48 ALFRESCO is a landscape scale fire and vegetation dynamics model. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR4/CMIP3 and AR5/CMIP5 climate inputs (IEM Generation 1a). These outputs include data from model rep 171(AR4/CMIP3) and rep 26(AR5/CMIP5), referred to as the “best rep” out of 200 replicates. The best rep was chosen through comparing ALFRESCO’s historical fire outputs to observed historical fire patterns. Single rep analysis is not recommended as a best practice, but can be used to visualize possible changes. The IEM Generation 1 is driven by outputs from 4 climate models, and two emission scenarios: AR4/CMIP3 SRES A1B CCCMA-CGCMS-3.1 MPI-ECHAM5 AR5/CMIP5 RCP 8.5 MRI-CGCM3 NCAR-CCSM4
Atmospheric Rivers of Alaska Modeled from ERA5 (1980-2022)
Atmospheric rivers (ARs) were detected from ERA5 6hr pressure level data, using a detection algorithm adapted from Guan & Waliser (2015). The algorithm uses a combination of vertically integrated water vapor transport (IVT), geometric shape, and directional criteria to define ARs. See the sources listed below and the GitHub repository for more detail and other references. The AR database is a zipped archive containing multiple attributed shapefiles. Polygon data includes individual timestep ARs, ARs making landfall in Alaska, and aggregated landfalling AR events. Point data includes coastal impact points landfalling AR events.
Projected Derived DOF/DOT/LOGS - 2km CMIP5/AR5
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.
Elsner et al., 2010: Implications of 21st century climate change for the hydrology of Washington State
McAfee et al., 2014: Statistically downscaled projections of snow/rain partitioning for Alaska
Walsh et al., 2018: Downscaling of climate model output for Alaskan stakeholders
Littell et al., 2018: Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios
Beaufort Sea Seaward Landfast Ice Edge (SLIE) Extent Summarized by Month and 9-year Periods
Mahoney AR, Bieniek P, Danielson S, Einhorn AH, Hedstrom KS, Jones JM, Klenz, T. (University of Alaska, Fairbanks, AK). 2024. Landfast ice climatology within the Arctic OCS. Anchorage (AK): U.S. Department of the Interior, Bureau of Ocean Energy Management. 121 p. Report No.: OCS Study BOEM 2024-034.
Mahoney, A. R., H. Eicken, A. G. Gaylord, and R. Gens (2014), Landfast sea ice extent in the Chukchi and Beaufort Seas: The annual cycle and decadal variability, Cold Reg. Sci. Tech., 103(0), 41-56, doi:
URL to web-accessible folder containing downloads for this data set.
Guan, B., and Waliser, D. E. (2015). Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies.
Atmospheric Rivers and Avalanches GitHub Repository
Scenarios Network for Alaska and Arctic Planning
Scenarios Network for Alaska & Arctic Planning (SNAP)