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Burn probability (BP) for Fireline Intensity Class 5 (FIL5) with flame lengths in the range of 2.4-3.7 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 4 (FIL4) with flame lengths in the range of 1.8-2.4 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 2 (FIL2) with flame lengths in the range of 0.6-1.2 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 6 (FIL6) with flame lengths in the range of 3.7-15 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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Burn probability (BP) raster dataset predicted for the 2080-2100 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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This set of 4 rasters shows precipitation as snow (mm) for Western North America under the B1 Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published here, were converted...
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This set of 4 rasters shows winter (Dec to Feb) mean temperature (deg C * 10) for Western North America under the A1B Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published...
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This data series contains 2868 temporal datasets.These data are climate model outputs that have been downscaled to 4-km spatial resolution using the Bias Corrected Statistical Downscaling (BCSD) method. Moore and Walden have modified the BCSD method described by Wood et al (2002), Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres 107: 4429-4443 and Salathe (2005), Downscaling simulations of future global climate with application to hydrologic modeling. International Journal of Climatology 25: 419-436. The modifications include a different interpolation scheme between GCM grid cells and a different approach to dealing with extreme values (Z-scores...
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Burn probability (BP) for Fireline Intensity Class 1 (FIL1) with flame lengths in the range of 0-0.6 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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Fire type predicted for the 2020-2040 period in the Rio Grande area with five classes: 1) shrub vegetation with torching flames; 2) shrub vegetation without torching flames; 3) forest with torching flames; 4) forest without torching flames; 5) grass or non-vegetation. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model...
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Burn probability (BP) raster dataset predicted for the 2020-2040 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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Conditional Flame Length (CFL) is an estimate of the mean flame lengths for each pixel, and was predicted for the 2050-2070 period in the Rio Grande area using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway. CFL...
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This set of 4 rasters shows summer (Jun to Aug) mean temperature (deg C * 10) for Western North America under the A1B Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published...
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This set of 4 rasters shows mean summer (May to Sep) precipitation (mm) for Western North America under the B1 Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published here,...
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This set of 4 rasters shows mean temperature of the coldest month (deg C * 10) for Western North America under the B1 Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published...
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This set of 4 rasters shows summer (Jun to Aug) precipitation (mm) for Western North America under the B1 Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published here, were...
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This set of 4 rasters shows winter (Dec to Feb) precipitation (mm) for Western North America under the B1 Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published here, were...
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Fire type predicted for the 2080-2100 period in the Rio Grande area with five classes: 1) shrub vegetation with torching flames; 2) shrub vegetation without torching flames; 3) forest with torching flames; 4) forest without torching flames; 5) grass or non-vegetation. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model...
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This set of 4 rasters shows mean annual temperature (deg C * 10) for Western North America under the A1B Emissions Scenario from the Intergovernmental Panel on Climate Change (IPCC). One layer shows the historic period (1961 to 1990), and there are three layers of future climate projections representing the 2020s, the 2050s, and the 2080s. These future layers are ensemble averages across all 23 CMIP3 AOGCMs (Coupled Model Intercomparison Project 3 Atmosphere-Ocean General Circulation Models). All layers have a resolution of 1 km, and are designed to capture climate gradients, temperature inversions, and rain shadows in the mountainous landscape of western North America. These data, originally published here, were...
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These data represent simulated ecological drought conditions for current climate, and for future climate represented by all available climate models at two time periods during the 21st century. These data were used to: 1) describe geographic patterns in ecological drought under historical climate conditions, 2) quantify the direction and magnitude of change in ecological drought, 3) identify areas and ecological drought metrics with projected changes that are robust across climate models, defined as drought metrics and locations where >90% of climate models agree in the direction of change.
map background search result map search result map Downscaled Climate Model Output for the Contiguous United States from IPCC AR4 Scenarios [Bias Corrected Statistical Downscaling (BCSD) Method] Mean Annual Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Mean Temperature of the coldest month under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Mean Summer (May to Sep) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Precipitation as Snow under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Summer (Jun to Aug) Mean Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Summer (Jun to Aug) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Winter (Dec to Feb) Mean Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Winter (Dec to Feb) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2020 to 2040 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Fire type predicted for 2080 to 2100 for Rio Grande study area Robust ecological drought projection data for drylands in the 21st century Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Fire type predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2020 to 2040 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Robust ecological drought projection data for drylands in the 21st century Downscaled Climate Model Output for the Contiguous United States from IPCC AR4 Scenarios [Bias Corrected Statistical Downscaling (BCSD) Method] Mean Annual Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Mean Temperature of the coldest month under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Mean Summer (May to Sep) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Precipitation as Snow under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Summer (Jun to Aug) Mean Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Summer (Jun to Aug) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble) Winter (Dec to Feb) Mean Temperature under the A1B Emissions Scenario (Western North America, 23 AOGCM Ensemble) Winter (Dec to Feb) Precipitation under the B1 Emissions Scenario (Western North America, 23 AOGCM Ensemble)