This data set consists of PRSIM mean air temperature climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are degrees Celsius. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

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 data set consists of PRSIM precipitation climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are millimeters. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

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.

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.

This dataset includes downscaled historical estimates of monthly average, minimum, and maximum precipitation and derived annual, seasonal, and decadal means of monthly total precipitation (in millimeters, 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 data are only available as monthly averages. The downscaling process utilizes PRISM climatological datasets from 1961-1990.

A dataset of landfast ice extent along the Alaska coast of the Chukchi Sea and adjacent waters in Russia, 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: chukchi_$YYYYMMDD_$source_slie.tif For example, the name chukchi_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.

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.

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 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.