This set of files includes downscaled historical estimates of monthly total precipitation (in millimeters, no unit conversion necessary, rainwater equivalent) from 1901 - 2013 (CRU TS 3.22) at 10 min x 10 min spatial resolution with global coverage. The downscaling process utilizes CRU CL v. 2.1 climatological datasets from 1961-1990.

This set of files includes downscaled historical estimates of monthly totals, and derived annual, seasonal, and decadal means of monthly total precipitation (in millimeters, no unit conversion necessary) from 1901 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) at 771 x 771 meter spatial resolution.

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 2km x 2km 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 1961-1990. **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.

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 dataset consists of observed and modeled wind data at an hourly temporal resolution for 67 communities in Alaska. Hourly ASOS/AWOS wind data (speed and direction) available via the Iowa Environmental Mesonet AK ASOS network were accessed and assessed for completeness, and 67 of those stations were determined to be sufficiently complete for climatological analysis. Those data were cleaned to produce regular hourly data, and adjusted via a combination of changepoint analysis and quantile mapping to correct for potential changes in sensor location and height. Historical (ERA-Interim reanalysis) and projected (GFDL-CM3 and NCAR-CCSM4) outputs from a dynamical downscaling effort were extracted at pixels intersecting the chosen communities and were bias-corrected using the cleaned station data. This bias-corrected historical and projected data along with cleaned station data make up the entirety of this dataset as a collection of CSV files, for each combination of community and origin (station or model name).

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.

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"

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.

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

These files include downscaled projections of decadal average monthly snow-day fraction ("fs", units = percent probability from 1 – 100) for each month of the decades from 2010-2019 to 2090-2099 at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Output is available for the CCSM4, GFDL-CM3, GISS-E2-R, IPSL-CM5A-LR, and MRI-CGCM3 models and three emissions scenarios (RCP 4.5, RCP 6.0 and RCP 8.5). These snow-day fraction estimates were produced by applying equations relating decadal average monthly temperature to snow-day fraction to downscaled decadal average monthly temperature. Separate equations were used to model the relationship between decadal monthly average temperature and the fraction of wet days with snow for seven geographic regions in the state: Arctic, Western Alaska, Interior, Cook Inlet, SW Islands, SW Interior, and the Gulf of Alaska coast, using regionally specific logistic models of the probability that precipitation falls as snow given temperature based on station data fits as in McAfee et al. 2014. These projections differ from McAfee et al. 2014 in that updated CMIP5 projected temperatures rather than CMIP3 temperatures were used for the future projections. Although the equations developed here provide a reasonable fit to the data, model evaluation demonstrated that some stations are consistently less well described by regional models than others. It is unclear why this occurs, but it is likely related to localized climate conditions. Very few weather stations with long records are located above 500m elevation in Alaska, so the equations used here were developed primarily from low-elevation weather stations. It is not clear whether the equations will be completely appropriate in the mountains. Finally, these equations summarize a long-term monthly relationship between temperature and precipitation type that is the result of short-term weather variability. In using these equations to make projections of future snow, as assume that these relationships remain stable over time, and we do not know how accurate that assumption is. These snow-day fraction estimates were produced by applying equations relating decadal average monthly temperature to snow-day fraction to downscaled projected decadal average monthly temperature. The equations were developed from daily observed climate data in the Global Historical Climatology Network. These data were acquired from the National Climatic Data Center in early 2012. Equations were developed for the seven climate regions described in Perica et al. (2012). Geospatial data describing those regions was provided by Sveta Stuefer. Perica, S., D. Kane, S. Dietz, K. Maitaria, D. Martin, S. Pavlovic, I. Roy, S. Stuefer, A. Tidwell, C. Trypaluk, D. Unruh, M. Yekta, E. Betts, G. Bonnin, S. Heim, L. Hiner, E. Lilly, J. Narayanan, F.Yan, T. Zhao. 2012. NOAA Atlas 14. Precipitation-Frequency Atlas of the United States.