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.

This dataset includes quantile-mapped historical and projected model runs of AR5 daily mean mean temperature (tas, degrees C) for each day of every year from 1958 - 2100 at 2.5 x 2.5 degree spatial resolution across 3 CMIP5 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 monthly temperature (in degrees Celsius, no unit conversion necessary) from 1901 - 2013 (CRU TS 3.22) at 10 min x 10 min spatial resolution. The downscaling process utilizes CRU CL v. 2.1 climatological datasets from 1961-1990.

This dataset includes downscaled historical estimates 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 1901 to 2006 (CRU TS 3.0), 2009 (CRU TS 3.1), 2015 (CRU TS 4.0), 2020 (CRU TS 4.05), or 2023 (CRU TS 4.08) at 2km x 2km spatial resolution. CRU TS 4.0 is only available as monthly averages, minimum, and maximum files. CRU TS 4.05 and 4.08 are only available as monthly averages. The downscaling process utilizes PRISM climatological datasets from 1961-1990.

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 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.

This set of files includes downscaled projections of monthly totals, and derived annual, seasonal, and decadal means of monthly average temperature (in degrees Celsius, no unit conversion necessary) from 1901 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) at 771 x 771 meter spatial resolution.

These files include climatological summaries of downscaled historical and projected decadal average monthly snowfall (i.e. snow-water) equivalent (SWE) in millimeters, the ratio of snowfall equivalent to precipitation, and future change in snowfall for October-March at 771-meter spatial resolution across the state of Alaska. Data are for summary October to March Alaska climatologies for: 1) historical and future snowfall equivalent (SWE), 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) historical and future ratio of SWE to precipitation (SFEtoP), SFEtoP is the ratio of October to March total SWE to October to March total precipitation is calculated as total SWE / total precipitation (expressed as percent, 0-100). 3) future change in snowfall equivalent relative to historical ("dSWE"), calculated as (SWE future – SWE historical) / SWE historical (no units, multiply by 100 to obtain percent). The historical reference period is 1970-1999, (file name “H70.99”), calculated from downscaled CRU TS 3.1 data Future climatologies (both RCP 4.5 and 8.5) are for: - 2020s (2010-2039) - 2050s (2040-2069) - 2080s (2070-2099) across 5 GCMs: NCAR-CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5, and MRI-CGCM3 as well as a 5-model mean (“5MM”). Following Elsner et al. (2010), <0.1 is rain dominated, 0.1 < SFE:P < 0.4 is transitional, and >0.4 is snow dominated. Only calculated for historical reference climatology 1970-1999 and three future climatologies: 2010-2039, 2040-2069, and 2070-2090, with each climatology representing the mean of three decadal averages from the available decadal grids. Snow 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 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"

These files include historical downscaled estimates of decadal average monthly snow-day fraction ("fs", units = percent probability from 1 – 100) for each month of the decades from 1900-1909 to 2000-2009 at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Version 1.0 was completed in 2015 using CMIP3. Version 2.0 was completed in 2018 using CMIP5. For more information on the methodology used to create this dataset, and guidelines for appropriate usage of the dataset, please see the data user's guide here: http://data.snap.uaf.edu/data/Base/AK_771m/historical/CRU_TS/snow_day_fraction/snow_fraction_data_users_guide.pdf