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.

This set of files includes downscaled future projections of vapor pressure (units=hPa) at a 1km spatial scale. This data has been prepared as model input for the Integrated Ecosystem Model (IEM). There can be errors or serious limitations to the application of this data to other analyses. The data constitute the result of a downscaling procedure using 2 General Circulation Models (GCM) from the Coupled Model Intercomparison Project 5 (CMIP5) for RCP 8.5 scenario (2006-2100) monthly time series and Climatic Research Unit (CRU) TS2.0 (1961-1990,10 min spatial resolution) global climatology data. Please note that this data is used to fill in a gap in available data for the Integrated Ecosystem Model (IEM) and does not constitute a complete or precise measurement of this variable in all locations. RCPs: 8.5 Centers, Model Names, Versions, and Acronyms: National Center for Atmospheric Research,Community Earth System Model 4,NCAR-CCSM4 Meteorological Research Institute,Coupled General Circulation Model v3.0,MRI-CGCM3 Methods of creating downscaled relative humidity data: 1. The GCM input data are distributed as relative humidity along with the CRU CL 2.0, therefore no conversion procedure was necessary before beginning the downscaling procedure. 2. Proportional Anomalies generated using the 20c3m Historical relative humidity data 1961-1990 climatology and the projected relative humidity data (2006-2100). 3. These proportional anomalies are interpolated using a spline interpolation to a 10min resolution grid for downscaling with the CRU CL 2.0 Relative Humidity Data. 4. The GCM proportional anomalies are multiplied by month to the baseline CRU CL 2.0 10min relative humidity climatology for the period 1961-1990. Creating a downscaled relative humidity projected time series 2006-2100. 5. Due to the conversion procedure and the low quality of the input data to begin with, there were values that fell well outside of the range of acceptable relative humidity (meaning that there were values >100 percent), these values were re-set to a relative humidity of 95 at the suggestion of the researchers involved in the project. It is well known that the CRU data is spotty for Alaska and the Circumpolar North, due to a lack of weather stations and poor temporal coverage for those stations that exist. 6. The desired output resolution for the AIEM modeling project is 1km, so the newly created downscaled time series is resampled to this resolution using a standard bilinear interpolation resampling procedure. 7. The final step was to convert the downscaled relative humidity data to vapor pressure using the calculation below, which uses a downscaled temperature data set created utilizing the same downscaling procedure. EQUATION: saturated vapor pressure = 6.112 x exp(17.62 x temperature/(243.12+temperature)) vapor pressure = (relative humidity x saturated vapor pressure)/100

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.

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 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 dataset consists of spatial representations of relative vegetation change produced through summarization of ALFRESCO model outputs. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR5/CMIP5 climate inputs (IEM Generation 2).

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.

These files include downscaled historical decadal average monthly snowfall equivalent ("SWE", in millimeters) for each month at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Historical data for 1910-1919 to 1990-1999 are available for CRU TS3.0-based data and for 1910-1919 to 2000-2009 for CRU TS3.1-based data.

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.