Filters: Tags: {"type":"Subject","name":"climate"} (X)
1,449 results (21ms)|
Filters
Date Range
Extensions (Less) Types (Less)
Contacts (Less)
Categories (Less) Tag Schemes
|
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and baseline climate conditions, and then projecting these correlations into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-5 show...
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal Mean Annual Ground Temperature at 1 Meter Depth (°C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 2km spatial resolution. It represents the A2 emissions scenario and the spatial extent is the NOS REA study area.
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average January temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA...
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average May temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA study...
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average of spring (March, April, May) total precipitation (in millimeters) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean of seasonal totals calculated from monthly totals, using the A2 emissions...
For each variable the per pixel change between the recent time slice (1981-2012) or future timslice (2050s) and the baseline (1900-1980) was calculated, identifying climate “deltas” for each pixel. Recent deltas are 800m resolution and use PRISM as the source dataset. Future deltas are 4km resolution and use ClimateWNA as the source dataset. Delta = later timeslice (recent or future) - baseline. Raster values are expressed in climate units either mm for precipitation or degrees c for temperature. delta ratio values are included for precipitation and CMD, which are ratios of change (1 = no change, < 1 = decreasing, > 1 = increasing).
For each variable the per pixel change between the recent time slice (1981-2012) or future timslice (2050s) and the baseline (1900-1980) was calculated, identifying climate “deltas” for each pixel. Recent deltas are 800m resolution and use PRISM as the source dataset. Future deltas are 4km resolution and use ClimateWNA as the source dataset. Delta = later timeslice (recent or future) - baseline. Raster values are expressed in climate units either mm for precipitation or degrees c for temperature. delta ratio values are included for precipitation and CMD, which are ratios of change (1 = no change, < 1 = decreasing, > 1 = increasing).
Some of the CYR rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - The Relative Flammability raster is a spatial representation of the total number of times each pixel within the Central Yukon REA boundary burned in 1,000 simulations of the Alaska Frame-Based Ecosystem Code (ALFRESCO) model. Pixel counts of simulated burns from 200 runs each of five downscaled Global Climate Models (cccma_cgcm3_1,...
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
Some of the YKL rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal mean Day of Thaw (in ordinal dates) for the decade 2060-2069 at 771x771 meter spatial resolution. Day of Thaw approximates when the running mean rises above 0°C. Although raster values represent ordinal dates, the values 0 and 365 are special classes. A value of 0 indicates...
|
|
