Item talk:Q319073
From geokb
{
"DOI": {
"doi": "10.5066/p9dhbmz5",
"identifiers": [],
"creators": [
{
"name": "Jeremy Littell",
"nameType": "Personal",
"affiliation": [
"United States Geological Survey"
],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": null,
"nameIdentifierScheme": "ORCID"
}
]
}
],
"titles": [
{
"title": "Alaska Climate Futures (mid and late 21st century) and Historical References (20th century)"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2024,
"subjects": [
{
"subject": "climatology"
},
{
"subject": "temperature"
},
{
"subject": "precipitation"
},
{
"subject": "biota"
},
{
"subject": "water balance"
},
{
"subject": "snow"
},
{
"subject": "fire frequency"
},
{
"subject": "climate futures"
},
{
"subject": "climate projections"
},
{
"subject": "vegetation change"
}
],
"contributors": [],
"dates": [
{
"date": "1910/2100",
"dateType": "Collected"
}
],
"language": null,
"types": {
"ris": "DATA",
"bibtex": "misc",
"citeproc": "dataset",
"schemaOrg": "Dataset",
"resourceType": "Dataset",
"resourceTypeGeneral": "Dataset"
},
"relatedIdentifiers": [],
"relatedItems": [],
"sizes": [],
"formats": [
"tiff"
],
"version": null,
"rightsList": [],
"descriptions": [
{
"description": "To meet the climate change planning and adaptation needs of Alaska managers and decision makers, I developed a set of statewide summaries of available climate change projections that can be further subset using GIS techniques for requests by management unit, watershed, or other location. This facilitates the development of tailored climate futures for decision makers\u2019 regional or subregional management context. This file describes the source data and summaries for purposes of technical /scientific documentation.\n\nThe methods and presentation for these datasets were adapted from products in previous USGS-approved IP products for the AKCASC Building Resilience Today project (e.g, Community of Kotlik et al. 2019).\n\nFor each data product included, summaries (averages or totals) are presented for multiple climate models or specific global warming levels and are average dover two time periods: 2040-2069, or the \u201c2050s\u201d, for near-term decision framing; and 2070-2099, or the \u201c2080s\u201d, for longer-term decision framing. In all cases where possible, both moderate emissions (RCP4.5 or +2C global level) and higher emissions (RCP8.5, or +4C global level) are presented. These choices (model averaging, temporal averaging, and scenario presentation) are tailored to the main sources of uncertainty (Hawkins and Sutton 2009) in climate model projections, specifically differences in climate model construction, climatic variability, and emissions scenario uncertainty (e.g., Littell et al. 2011, Snover et al. 2013, Terando et al. 2020). Not all scenario planning or climate impacts modeling needs can be met with these projections \u2013 these are intended to characterize a range of futures indicated by the available data products and facilitate further exploration of climate impacts modeling and adaptation development options.\n\nVariable descriptions: \n\u00a0\n\n\n1. Statistically downscaled temperature, precipitation, and derived snow variables\n\u00a0\n1.1. Temperature\n\u2022 Average of 5 statistically downscaled climate models: MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4, bias corrected\u00a0\n\u2022 Annual; winter (DJF); spring (MAM); summer (JJA); autumn (SON)\n\u2022 771m resolution (Alaska wide)\u00a0\n\u2022 Original data in degrees C; summary graphics and tables are presented as climatology \u201cdeltas\u201d in both degrees C and degrees F, changes (for the period 2040-2069 and 2070-2099) relative to historical (1970-1999)\u00a0\n\u2022 RCP 4.5 and RCP8.5\n\u2022 Details: Walsh et al. 2018\n\u00a0\n1.2. Precipitation\n\u2022 Average of 5 statistically downscaled climate models: MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4 , bias corrected\u00a0\n\u2022 Annual; winter (DJF); spring (MAM); summer (JJA); autumn (SON)\n\u2022 771m resolution (Alaska wide)\u00a0\n\u2022 Graphics are presented as climatology \u201cdeltas\u201d in percent change in total, changes (for the period 2040-2069 and 2070-2099) relative to historical (1970-1999)\u00a0\n\u2022 Details: Walsh et al. 2018\n\u00a0\n1.3. Snowfall water equivalent\n\u2022 Average of 5 statistically downscaled climate models: MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4, bias corrected\u00a0\n\u2022 Total snowfall water equivalent for October to March\n\u2022 771m resolution (Alaska wide)\u00a0\n\u2022 Graphics are presented as climatology \u201cdeltas\u201d in percent, changes (for the period 2040-2069 and 2070-2099) relative to historical (1970-1999)\u00a0\n\u2022 RCP 4.5 and RCP8.5\n\u2022 Details: Littell et al. 2018\n\u00a0\n1.4. Snow index\n\u2022 Average of 5 statistically downscaled climate models: MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4 , bias corrected\u00a0\n\u2022 Ratio of snowfall water equivalent to total October-March precipitation, equivalent to the fraction of total October to March precipitation entrained in snowfall water equivalent\n\u2022 771m resolution (Alaska wide)\u00a0\n\u2022 Graphics are presented as climatology \u201cdeltas\u201d, changes (for the period 2040-2069 and 2070-2099) relative to historical (1970-1999)\n\u2022 RCP 4.5 and RCP8.5\n\u2022 Details: Littell et al. 2018\u00a0\n\u00a0\n1.5. Change in months of reliable snow\n\u2022 Average of 5 statistically downscaled climate models: MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4, bias corrected\u00a0\n\u2022 Change in count of months with >70% precipitation as snow\n\u2022 771m resolution (Alaska wide)\u00a0\n\u2022 Graphics are presented as climatology \u201cdeltas\u201d, changes (for the period 2040-2069) relative to historical (1970-1999)\n\u2022 Only RCP8.5\n\u00a0\n2. Derived land surface variables consistent with temperature and precipitation above\n\u00a0\n2.1. Changes in fires per century, ALFRESCO landscape fire model\n\u2022 MRI-CGCM3 (lower fire activity) and NCAR-CCSM4 (higher fire activity)\n\u2022 Change (difference) between 1900-1999 and 2000-2099 fires per model pixel, derived from raw \u201crelative flammability\u201d, or the count of model replicate fires per pixel per time period\u00a0\n\u2022 2km resolution (Alaska and NW Canada)\n\u2022 Graphics are presented as \u201cdeltas\u201d changes (for the 21st century relative to historical 20th century) in fires per century\n\u2022 RCP4.5 and RCP8.5\n\u2022 Details: McGuire et al. 2018, Euskirchen et al. 2020\n\u00a0\n2.2. Changes in vegetation per century, ALFRESCO landscape fire model\n\u2022 MRI-CGCM3 (lower fire activity) and NCAR-CCSM4 (higher fire activity)\n\u2022 Change (difference) between 1900-1999 and 2000-2099 vegetation changes per model pixel\n\u2022 2km resolution (Alaska and NW Canada)\n\u2022 Graphics are presented as \u201cdeltas\u201d changes (for the 21st century relative to historical 20th century) in vegetation type per century\n\u2022 RCP4.5 and RCP8.5\n\u2022 Details: McGuire et al. 2018, Euskirchen et al. 2020\n\u00a0\n2.3. New Picea glauca (white spruce) establishment, ALFRESCO landscape fire model\n\u2022 MRI-CGCM3 (lower fire activity) and NCAR-CCSM4 (higher fire activity)\n\u2022 New spruce colonization in 2050 and 2100 in areas without spruce in historical vegetation map.\n\u2022 2km resolution (Alaska and NW Canada)\n\u2022 Graphics are presented as new establishment at two densities, basal area 0-5m^2/ha for 2050 and 2100 and 5-12m^2/ha for 2050\n\u2022 Only RCP8.5\n\u2022 Details: McGuire et al. 2018, Euskirchen et al. 2020\n\u00a0\n3. TerraClimate hydrologic changes\n\u00a0\n3.1. Change in spring runoff\n\u2022 CMIP5 climate model ensemble of deltas at global warming levels (see below)\n\u2022 Change in volume of monthly total runoff (Q) per pixel relative to historical (1981-2010) for Feb, Mar, Apr, May\n\u2022 4km resolution (global, subset for Alaska region)\n\u2022 Graphics are presented as change (difference) mm of total Q from historical reference\n\u2022 No emissions, +2C and +4C global warming levels\n\u2022 Details: Abatzoglou et al. 2018\n\u00a0\n3.2. Change in summer (JJA) and growing season (AMJJAS) potential evapotranspiration\n\u2022 CMIP5 climate model ensemble of deltas at global warming levels (see below)\n\u2022 Change in volume of summer and growing season total potential evapotranspiration (PET) per pixel relative to historical (1981-2010) for Jun-Aug and Apr-Sep\n\u2022 4km resolution (global, subset for Alaska region)\n\u2022 Graphics are presented as change (difference) mm of total PET from historical reference\n\u2022 No emissions, +2C and +4C global warming levels\n\u2022 Details: Abatzoglou et al. 2018\n\u00a0\n3.3. Change in summer (JJA) and growing season (AMJJAS) water balance deficit\n\u2022 CMIP5 climate model ensemble of deltas at global warming levels (see below)\n\u2022 Change in volume of summer and growing season total potential evapotranspiration (PET) minus actual evapotranspiration (AET) per pixel relative to historical (1981-2010) for Jun-Aug and Apr-Sep\n\u2022 4km resolution (global, subset for Alaska region)\n\u2022 Graphics are presented as change (difference) mm of total PET-AET from historical reference\n\u2022 No emissions, +2C and +4C global warming levels\n\u2022 Details: Abatzoglou et al. 2018\n\n\n\nData availability\n\u00a0\nRaw data used to compute the above are available from online archives.\n\u00a0\nLocations:\n\u00a0\n\n- http://data.snap.uaf.edu/data/Base/AK_771m/projected/AR5_CMIP5_models/Projected_Monthly_and_Derived_Temperature_Products_771m_CMIP5_AR5/derived/\n\n\u00a0\n1.2 -\u00a0 http://data.snap.uaf.edu/data/Base/AK_771m/projected/AR5_CMIP5_models/Projected_Monthly_and_Derived_Precipitation_Products_771m_CMIP5_AR5/derived/\n\u00a0\n\u00a0\n1.3 - http://data.snap.uaf.edu/data/Base/AK_771m/projected/AR5_CMIP5_models/SWE/\n\u00a0\n1.4 - http://data.snap.uaf.edu/data/Base/AK_771m/SWE_dSWE_SFEtoPR/\n\u00a0\n1.5 \u2013 derived\n\u00a0\n2.1 - http://data.snap.uaf.edu/data/IEM/Outputs/ALF/Gen_1a/alfresco_relative_spatial_outputs/relative_flammability/AR5_CMIP5/\n\u00a0\n2.2 - http://data.snap.uaf.edu/data/IEM/Outputs/ALF/Gen_1a/alfresco_relative_spatial_outputs/relative_vegetation_change/AR5_CMIP5/\n\u00a0\n2.3 - http://data.snap.uaf.edu/data/IEM/Outputs/ALF/Gen_1a/best_rep_outputs/AR5_CMIP5/\n\u00a0\n3.1 \u2013\n\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate4C_q.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate2C_q.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate19812010_q.nc\n\n3.2 \u2013\n\n\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate4C_pet.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate2C_pet.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate19812010_pet.nc\n\n3.3 \u2013\n\n\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate4C_aet.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate2C_aet.nc\nhttp://thredds.northwestknowledge.net:8080/thredds/catalog/TERRACLIMATE_ALL/summaries/catalog.html?dataset=TERRACLIMATE_ALL_SCAN/summaries/TerraClimate19812010_aet.nc\n\n\u00a0\n\u00a0\nMethods\n\u00a0\n\nTemperature\n\n\u00a0\nSeasonal (Annual \u2013 Jan-Dec; Winter \u2013 Dec-Feb; Spring \u2013 Mar-May; Summer \u2013 Jun-Aug; Autumn \u2013 Sep-Nov) decadally averaged historical and projected future temperature data (GeoTiff raster files) were obtained from UAF/SNAP/IARC for historical decades 1970-1979, 1980-1989, and 1990-1999 and future decades for 2040-2049, 2050-2059, 2060-2069, 2070-2079, 2080-2089, and 2090-2099. Three climatologies were computed in R (4.1.1 \u2013 \u201cKick Things\u201d, with packages = library(sf); library(raster); library(sp); library(rgdal); library(maptools)) for mean temperature by computing averages of three decades: 1970-1999, 2040-2069, and 2070-2099. The future climatologies were further replicated for all five CMIP5 GCMs available (MRI-CGCM3, GISS-E2-R, GFDL-CM3, IPSL-CM5A-LR and NCAR-CCSM4) and for both RCP4.5 and RCP8.5 pathways, resulting in one historical and ten future scenarios for each annual or seasonal variable. The five-GCM average was computed for both RCP4.5 and RCP4.5 for annual and the four seasons as well. The change (\u201cdelta\u201d) in\u00a0 temperature was computed by subtracting the historical (1970-1999) from each projected future climatology. Data are in SI units (centigrade) and are converted to Farenheit (or scale bars appropriately scaled) for display purposes.\n\u00a0\n\nPrecipitation\n\n\u00a0\nSame as for temperature above, except that future changes are computed in percent rather than absolute units of mm, i.e., delta Pr = (future climatology \u2013 historical climatology)/historical climatology.\n\u00a0\n\nSnowfall water equivalent\n\n\u00a0\nSame as for temperature and precipitation, except thirty-year climatologies were pre-calculated as products from Littell et al. 2018. Of note, in Littell et al. 2018, snow day fraction based on McAfee et al. 2013 and downscaled climate in 1.1 and 1.2 are used to calculate 1.3. Raw data are expressed as fractions of 1, so must be scaled to percent for display purposes to be the same as precipitation.\n\u00a0\n\nSnow Index\n\n\u00a0\nSame as snowfall water equivalent, except no deltas are calculated. The interpretation of this index is as a percent value out of 100, so the delta is less meaningful\n\u00a0\n\nChange in months of reliable snow\n\n\u00a0\nHeuristic exploration of historical data for 1.3 suggest that 70% of precipitation as snowfall results in \u201csnow dominated\u201d watersheds by month. 50% was too low \u2013 essentially this results in transitional snowpack, which wouldn\u2019t be considered \u201creliable\u201d. 70% is likely a generous estimate (probably requires more than 70% to be \u201creliable\u201d across interannual variability and for most uses dependent on full landscape coverage of snowpack), but also depends on region, mean snowpack, and snow redistribution, which is not simulated here. Individual months of snowfall water equivalent in Littell et al 2018 were computed only for mid-century (2040-2069) and only for RCP 8.5, and the total number of months with snow day fraction > 70 were calculated for the historical and future. Change in months is future minus historical.\n\u00a0\n\nChanges in fires per century\n\n\u00a0\nRaw data (GeoTiff rasters) for ALFRESCO \u201crelative flammability\u201d were downloaded for two bracketing GCMs and for two different time periods: 1900-2099 and 2000-2099. However, \u201crelative flammability\u201d doesn\u2019t\u2019 give much insight into probability of permanent vegetation change. But if fires per century exceed approximately one, it is unlikely that boreal forest will remain dominated by spruce and will instead transition to deciduous forest. Also a general increase or decrease in number of fires per century is more intuitively scalable to most users than negative or positive relative flammability. The relative flammability data indicate the number of times each cell burned during the specified period given the transient summer climate from the GCM across 200 ALFRESCO model replicates. To convert this value to fires per century, the total number of fires per cell for 2000-2099 (future) must be separated (via subtraction) from the full 1900-2099 (historical + future) period, and then the results must be divided by the number of replicates to get mean fires per century per replicate. The change is calculated as (2000-2099 \u2013 1900-1999)/200.\n\u00a0\n\nChanges in vegetation per century\n\n\u00a0\nSame as 2.1, except for vegetation change data.\n\u00a0\n\nNew Picea glauca establishment\n\n\u00a0\nRaw data (GeoTiff rasters) for ALFRESCO basal area of white spruce were downloaded for two bracketing GCMs. For year 2050 and year 2100, cells with 0-5 m^2/ha were extracted; for 2100, cells with 5-12 m^2/ha were extracted. These are consistent with \u201cnew spruce establishing\u201d and/or \u201cnew spruce established\u201d densities and indicate places on the landscape where tundra vegetation is being rapidly invaded by spruce.\n\u00a0\n3.1 Changes in spring runoff\n\u00a0\nOriginal monthly runoff (Q) data was downloaded from the TerraClimate thredds site as NetCDF files and converted in R to GeoTiff rasters to include with variables in 1 and 2 above, but at coarser resolution (4km native). Historical (1980-2010) monthly and future (analogous +2C global and +4C global warming levels) monthly Q were extracted for Feb, Mar, Apr, and May and the monthly deltas calculated as differences (1980-2010 minus +2C future and, separately, minus +4C future).\n\u00a0\n3.2 Change in summer (JJA) and growing season (AMJJAS) potential evapotranspiration\n\u00a0\nSame as 3.1, but for potential evapotranspiration and for Apr, May, Jun, Jul, Aug and Sep. Growing season (April-September total) and summer (June-August total) potential evapotranspiration were calculated for historical (1980-2010) and future (analogous +2C global and +4C global warming levels). Seasonal deltas calculated as differences (1980-2010 minus +2C future and, separately, minus +4C future).\n\u00a0\n3.3 Change in summer (JJA) and growing season (AMJJAS) water balance deficit\n\u00a0\nSame as 3.2, except deficit is equal to PET minus actual evapotranspiration (PET-AET). AET summaries were calculated as in 3.2, then subtracted from PET before the differencing for historical relative to future.\n\u00a0\n\u00a0\nData files\n\u00a0\nThree archives nominally described according to source/resolution are included.\n\u00a0\n1. Deltas_771m_sources includes summary temperature (dTa), precipitation (dPr), and snowpack (dSFWE) outputs\u00a0\n\n1.1 dTa includes changes (d) in air temperature (Tas) in C degrees\n\ndtas_ANN_5mm.2050s.r45.tif\ndtas_ANN_5mm.2050s.r85.tif\ndtas_ANN_5mm.2080s.r45.tif\ndtas_ANN_5mm.2080s.r85.tif\ndtas_DJF_5mm.2050s.r45.tif\ndtas_DJF_5mm.2050s.r85.tif\ndtas_DJF_5mm.2080s.r45.tif\ndtas_DJF_5mm.2080s.r85.tif\ndtas_JJA_5mm.2050s.r45.tif\ndtas_JJA_5mm.2050s.r85.tif\ndtas_JJA_5mm.2080s.r45.tif\ndtas_JJA_5mm.2080s.r85.tif\ndtas_MAM_5mm.2050s.r45.tif\ndtas_MAM_5mm.2050s.r85.tif\ndtas_MAM_5mm.2080s.r45.tif\ndtas_MAM_5mm.2080s.r85.tif\ndtas_SON_5mm.2050s.r45.tif\ndtas_SON_5mm.2050s.r85.tif\ndtas_SON_5mm.2080s.r45.tif\ndtas_SON_5mm.2080s.r85.tif\n\u00a0\n1.2\u00a0dPr includes changes (d) in precipitation (Pr), in %\n\ndpr_ANN_5mm.2050s.r45.tif\ndpr_ANN_5mm.2050s.r85.tif\ndpr_ANN_5mm.2080s.r45.tif\ndpr_ANN_5mm.2080s.r85.tif\ndpr_DJF_5mm.2050s.r45.tif\ndpr_DJF_5mm.2050s.r85.tif\ndpr_DJF_5mm.2080s.r45.tif\ndpr_DJF_5mm.2080s.r85.tif\ndpr_JJA_5mm.2050s.r45.tif\ndpr_JJA_5mm.2050s.r85.tif\ndpr_JJA_5mm.2080s.r45.tif\ndpr_JJA_5mm.2080s.r85.tif\ndpr_MAM_5mm.2050s.r45.tif\ndpr_MAM_5mm.2050s.r85.tif\ndpr_MAM_5mm.2080s.r45.tif\ndpr_MAM_5mm.2080s.r85.tif\ndpr_SON_5mm.2050s.r45.tif\ndpr_SON_5mm.2050s.r85.tif\ndpr_SON_5mm.2080s.r45.tif\ndpr_SON_5mm.2080s.r85.tif\n\u00a0\n1.3. dSFWE includes changes in \u201creliable\u201d snow (fs_monr70, in months), changes in SWE (dSWE, in ratio, or %/100) and SFE:Pr ratios (SFEtoP, in ratio):\n\ndfs_monr70_5mm.2050.rcp85.tif\ndswe_ONDJFM_5mm.2050s.r45.tif\ndswe_ONDJFM_5mm.2050s.r85.tif\ndswe_ONDJFM_5mm.2080s.r45.tif\ndswe_ONDJFM_5mm.2080s.r85.tif\nSFEtoP_ONDJFM_5mm.2050s.r45.tif\nSFEtoP_ONDJFM_5mm.2050s.r85.tif\nSFEtoP_ONDJFM_5mm.2080s.r45.tif\nSFEtoP_ONDJFM_5mm.2080s.r85.tif\nswe_ONDJFM_hist.1980s.tif\n\n2.\u00a0Deltas_2km_sources includes ALFRESCO outputs (fires per century, vegetation per century, new spruce in \u201cdFpCen, dVegpCen, NuPi\u201d and\u200b\n\n2.1\u00a0dFpCen_dVegpCen_NuPi includes new spruce (NuPi) for basal area 0-5 (BA0t5) and 5-12 (BA5t12), and changes (d) in fires per century (fpcen) and vegetation per century (vgcen):\n\ndfpcen.ccsm4.2100.r45.tif\ndfpcen.ccsm4.2100.r85.tif\ndfpcen.cgcm3.2100.r45.tif\ndfpcen.cgcm3.2100.r85.tif\ndvgcen.ccsm4.2100.r45.tif\ndvgcen.ccsm4.2100.r85.tif\ndvgcen.cgcm3.2100.r45.tif\ndvgcen.cgcm3.2100.r85.tif\nNuPiBA0t5.ccsm4.2050.tif\nNuPiBA0t5.ccsm4.2100.r85.tif\nNuPiBA0t5.cgcm3.2050.r85.tif\nNuPiBA0t5.cgcm3.2100.r85.tif\nNuPiBA5t12.ccsm4.2050.r85.tif\nNuPiBA5t12.cgcm3.2050.r85.tif\n\u00a0\n3.\u00a0Deltas_TerraClimate_sources includes changes (d) in potential evapotranspiration (PET), deficit (DEF), and runoff (Q) outputs:\n\ndDEF_AMJJAS.2C.tif\ndDEF_AMJJAS.4C.tif\ndDEF_JJA.2C.tif\ndDEF_JJA.4C.tif\ndPET_AMJJAS.2C.tif\ndPET_AMJJAS.4C.tif\ndPET_JJA.2C.tif\ndPET_JJA.4C.tif\ndq_APR.2C.tif\ndq_APR.4C.tif\ndq_FEB.2C.tif\ndq_FEB.4C.tif\ndq_MAR.2C.tif\ndq_MAR.4C.tif\ndq_MAY.2C.tif\ndq_MAY.4C.tif\u00a0\n\n\u00a0\nCitations\n\u00a0\nAbatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data.\n\u00a0\nCommunity of Kotlik, Littell, J.S., Fresco, N., Toohey, R.C., and Chase, M., editors. 2020. Looking Forward, Looking Back: Building Resilience Today Community Report. Aleutian Pribilof Islands Association. Kotlik and Fairbanks, AK. 48 pp.\n\u00a0\nEuskirchen, E. S., Timm, K., Breen, A. L., Gray, S., Rupp, T. S., Martin, P., Reynolds, J. H., Sesser, A., Murphy, K., Littell, J. S., Bennett, A., Bolton, W. R., Carman, T., Genet, H., Griffith, B., Kurkowski, T., Lara, M. J., Marchenko, S., Nicolsky, D., \u2026 McGuire, A. D. (2020). Co-producing knowledge: the Integrated Ecosystem Model for resource management in Arctic Alaska. Frontiers in Ecology and the Environment, 18(8), 447\u2013455. https://doi.org/https://doi.org/10.1002/fee.2176\n\u00a0\nMcAfee, S. A., Walsh, J., & Rupp, T. S. (2014). Statistically downscaled projections of snow/rain partitioning for Alaska. Hydrological Processes, 28(12), 3930\u20133946. https://doi.org/10.1002/hyp.9934\n\u00a0\nMcGuire, A. D., Genet, H., Lyu, Z., Pastick, N., Stackpoole, S., Birdsey, R., D\u2019Amore, D., He, Y., Rupp, T. S., Striegl, R., Wylie, B. K., Zhou, X., Zhuang, Q., & Zhu, Z. (2018). Assessing historical and projected carbon balance of Alaska: A synthesis of results and policy/management implications. Ecological Applications, 28(6), 1396\u20131412. https://doi.org/10.1002/EAP.1768\n\u00a0\n\u00a0\nLittell, J. S., McAfee, S. A., & Hayward, G. D. (2018). Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios. Water 2018, Vol. 10, Page 668, 10(5), 668. https://doi.org/10.3390/W10050668\n\u00a0\nLittell, J. S., McKenzie, D., Kerns, B. K., Cushman, S., & Shaw, C. G. (2011). Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere, 2(9), art102. https://doi.org/10.1890/ES11-00114.1\n\u00a0\nSnover, A. K., Mantua, N. J., Littell, J. S., Alexander, M. A., Mcclure, M. M., & Nye, J. (2013). Choosing and Using Climate-Change Scenarios for Ecological-Impact Assessments and Conservation Decisions. Conservation Biology, 27(6), 1147\u20131157. https://doi.org/10.1111/cobi.12163\n\u00a0\nWalsh, J. E., Bhatt, U. S., Littell, J. S., Leonawicz, M., Lindgren, M., Kurkowski, T. A., Bieniek, P. A., Thoman, R., Gray, S., & Rupp, T. S. (2018). Downscaling of climate model output for Alaskan stakeholders. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2018.03.021\n\u00a0\n\nCustom geospatial summaries for management / planning / adaptation requests:\n\n\u00a0\nThe data layers above have been used for a series of custom summaries for specific management units in Alaska. As of Feb 2023, there are 18 products for specific areas in \u201cdraft\u201d format that use these data layers.\n\u00a0\n\nMiddle Kuskokwim community adaptation (ANTHC): Feb 2020\nCopper River Native Association (CRNA): Apr 2020\nTetlin National Wildlife Refuge (USFWS): May 2020\nChugach National Forest (USFS): Oct 2020\nYukon Delta National Wildlife Refuge (USFWS): Mar 2021\nYukon \u2013 Koyukuk community adaptation (ANTHC): Mar 2021\nYukon Flats National Wildlife Refuge (USFWS): Apr 2021\nArctic National Wildlife Refuge (USFWS): Apr 2021\nIzembek National Wildlife Refuge (USFWS): Jun 2021\nKoyukuk, Innoko, Nowitna Wildlife Refuges (USFWS): Aug 2021\nAlaska National Park Networks (USNPS): Feb \u2013 Aug 2021\n\t\nSW Network: Feb 2021\nSE Network: Feb 2021\nCentral AK Network: Aug 2021\nArctic Network: Aug 2021\n\n\nTogiak National Wildlife Refuge (USFWS): Apr 2022\nTongass National forest (USFS): May 2021, Feb 2023\nAleutian Bering Sea Island region (APIA): Oct 2021, Jan 2023\nKlukwan/Chilkat upper Lynn Canal (Chilkat Indian Village): Aug 2019, Feb 2023\n\n\u00a0\nThese are not currently hosted in one place \u2013 access is variable depending on the governmental home of the requestor.",
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