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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).
Trends measure the magnitude and statistical significance within the recent 32-year timeslice (1981-2012) using a combination of two tests. Theil-Sen slope was used to calculate magnitude of change within the recent timeframe; a Theil-Sen linear regression line is fit to the 32-year time series. The change in the value of this line across the 32-year period indicates magnitude of climate change. The Mann-Kendall test was used to calculate p-values to measure the statistical significance of the magnitude of the 32-year trend. Change was only deemed statistically significant in places where the Mann-Kendall p-value was less than 0.05.
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 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. - The fine-scale invasion vulnerability model, combining higher probability sites for non-native plant importation and establishment, suggests that the region currently and into the near term is likely to have a non-native plant species restricted to a very small area. By 2060 however, all villages and the human footprint associated...
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 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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
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 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 Snow Day Fraction (%) for the decades 2010-2019, 2020-2029, and 2060-2069, and months January, February, March, April, May, September, October, November, and December at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly means, using the...
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. - The fine-scale invasion vulnerability model, combining higher probability sites for non-native plant importation and establishment, suggests that the region currently and into the near term is likely to have a non-native plant species restricted to a very small area. By 2060 however, all villages and the human footprint associated...
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. - This file includes a downscaled projection of decadal average summer (June, July, August) 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 seasonal averages, which in turn were calculated from monthly means, using...
Trends measure the magnitude and statistical significance within the recent 32-year timeslice (1981-2012) using a combination of two tests. Theil-Sen slope was used to calculate magnitude of change within the recent timeframe; a Theil-Sen linear regression line is fit to the 32-year time series. The change in the value of this line across the 32-year period indicates magnitude of climate change. The Mann-Kendall test was used to calculate p-values to measure the statistical significance of the magnitude of the 32-year trend. Change was only deemed statistically significant in places where the Mann-Kendall p-value was less than 0.05.
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 average of winter (December, January, February) total precipitation (in millimeters) for the decade 2010-2019 at 771x771 meter spatial resolution. The file represents a decadal mean of seasonal totals calculated from monthly totals, using the A1B emissions scenario. The...
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 average of summer (June, July, August) total precipitation (in millimeters) for the decade 2010-2019 at 771x771 meter spatial resolution. The file represents a decadal mean of seasonal totals calculated from monthly totals, using the A1B emissions scenario. The spatial...
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 average of November Snow Day Fraction (in percent) for the decade 2010-2019 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly (November) averages, using the A2 emissions scenario. Snow Day Fraction is the percentage of days...
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 average of October Snow Day Fraction (in percent) for the decade 2010-2019 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly (October) averages, using the A2 emissions scenario. Snow Day Fraction is the percentage of days in...
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 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.
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