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  • 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 file includes spatial representations of relative flammability produced through summarization of the ALFRESCO model outputs. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR4/CMIP3 climate inputs (IEM Generation 1) and AR5/CMIP5 climate inputs (IEM Generation 2). Relative flammability was defined as the likelihood of a pixel to burn . These parameters were assessed throughout the full spatial domain and 3 temporal domains of the simulations (1900-1999, 2000-2099, and 1900-2099) across all 200 model replicates. We calculated the proportion of years among all the simulations (200 replicate runs x number of years per simulation) that each individual pixel burned. The models and scenarios include: IEM Generation 1, AR4/CMIP3: CCCMA-CGCM3.1(T47) MPI-ECHAM5/MPI-OM under the A1B emission scenario IEM Generation 2, AR5/CMIP5: NCAR-CCSM4 MRI-CGCM3 under RCP 8.5 emission scenario There are several example map layouts for specific Landscape Conservation Cooperatives in Alaska for for IEM Generation 1 outputs only. Relative Flammability: Counts the number of times a pixel burned through all replicates and time and divides that value by the total number of layers (replicates * years) ------------- For background on ALFRESCO, please refer to: Daniel H. Mann, T. Scott Rupp, Mark A. Olson, and Paul A. Duffy (YEAR) Is Alaska's Boreal Forest Now Crossing a Major Ecological Threshold? Arctic, Antarctic, and Alpine Research 2012 44 (3), 319-331

  • 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 sea ice indicators for the Arctic based on daily sea ice concentrations derived from satellite passive microwave measurements. The four indicators available are day of break-up start, day of break-up end, day of freeze-up start, and day of freeze-up end. These “day of year” values indicate the ordinal day of the ice-year on which the event occurred. The ice-year is defined as September 1 through August of the following year. Locally defined indicators can serve as key links between pan-Arctic or global indicators such as sea-ice extent or volume and local uses of sea ice, with the potential to inform community-scale adaptation and response.

  • 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 degrees Celsius, no unit conversion necessary) from 1901 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) or 2015 (CRU TS 4.0) or 2020 (CRU TS 4.05) at 2km x 2km spatial resolution. CRU TS 4.0 is only available as monthly averages, minimum, and maximum files. CRU TS 4.05 is only available as monthly averages. The downscaling process utilizes PRISM climatological datasets from 1961-1990.

  • 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 data contain historical and future projections of percent land cover from 1950 to 2100, as simulated by the Alaska Thermokarst Model for the boreal region. The data are 1km spatial resolution. The climate data used to drive the model simulations are from the Climate Research Unit, Time Series 4.0 (CRU_TS40) for the historical period (1950 to 2015), and from RCP 8.5 and global circulation models NCAR-CCSM4 and MRI-CGCM3 for the projected period (2016 to 2100). Datasets from the years 1950, 2000, 2050, and 2100 are provided. Annual maps are available upon request. 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. If used for publication, these data should be cited as the following: Genet H., Lara M., McGuire A.D., Jorgenson T.M., Euskirchen E.S., Clein J., Carman T., Rutter R., Rupp S., Breen A., Kurkowski T., Bennett A., Torgenson B., Romanovski V., Marchenko S. 2018. Land cover dynamic in the Tanana Flats from 1950 to 2100 driven by thermokarst activity.

  • 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 - 2006 (CRU TS 3.0) or 2009 (CRU TS 3.1) or 2015 (CRU TS 4.0) or 2020 (CRU TS 4.05) at 2km x 2km spatial resolution. CRU TS 4.0 is only available as monthly averages, minimum, and maximum files. CRU TS 4.05 is only available as monthly averages. The downscaling process utilizes PRISM climatological datasets from 1961-1990.

  • This set of files includes annual model outputs from ALFRESCO, 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 climate inputs (IEM Generation 1-AR4) and AR5/CMIP5 climate inputs (IEM Generation 1-AR5). These outputs include data from model rep 171 (IEM Generation 1-AR4) and rep 26 (IEM Generation 1-AR5), 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. 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. **Climate models and emission scenarios:** IEM Generation 1-AR4/CMIP3 CCCMA-CGCMS-3.1 MPI-ECHAM5 under the SRES A1B scenario IEM Generation 1-AR5/CMIP5 MRI-CGCM3 NCAR-CCSM4 under RCP 8.5 scenario Variables include: -------------- **Veg**: The dominant vegetation for this cell. Current values are: 0 = Not Modeled 1 = Black Spruce 2 = White Spruce 3 = Deciduous Forest 4 = Shrub Tundra 5 = Graminoid Tundra 6 = Wetland Tundra 7 = Barren / Lichen / Moss 8 = Temperate Rainforest **Age**: This the age of the vegetation in each cell. An Age value of 0 means it transitioned in the previous year. **Basal Area**: The accumulation of basal area of white spruce in tundra cell, and is influenced by seed dispersal, growth of biomass, climate data, and other factors. units = m^2 / ha **Burn Severity**: This is a categorical burn severity level of the previous burn in the current cell, influenced by fire size and slope. For example, a burn severity value in a file with year 1971 in the file name means that the severity level given to that file occurred in the fire that occurred in year 1970. 0=No Burn 1=Low 2=Moderate 3=High w Low Surface Severity 4=High w/ High Surface Severity **Fire Scar**: These are the unique fire scars. Each cell has three values. Band 1 - Year of burn Band 2 - Unique ID for the simulated fire for that simulation year Band 3 - Whether or not the cell was an ignition location for a fire. There will only be 1 ignition cell per fire per year. 0 = not ignition 1 = ignition point For background on ALFRESCO, please refer to: Is Alaska's Boreal Forest Now Crossing a Major Ecological Threshold? Daniel H. Mann, T. Scott Rupp, Mark A. Olson, and Paul A. Duffy Arctic, Antarctic, and Alpine Research 2012 44 (3), 319-331 [http://www.bioone.org/doi/abs/10.1657/1938-4246-44.3.319](http://www.bioone.org/doi/abs/10.1657/1938-4246-44.3.319)

  • Rain on snow (ROS) events were derived from 20km dynamically downscaled ERA-Interim reanalysis and global climate model (GCM) climate projections data. The GCM data were from RCP 8.5 of GFDL-CM3 and NCAR-CCSM4. The amount of liquid precipitation for each day is provided in the database for each grid cell and was determined to be a ROS event by the temperature being at or near freezing and/or the presence of snow on the ground.