Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.

ClimateNA is a standalone MS Windows application that uses gridded monthly climate normal data (800 x 800 m) to generate scale-free climate data and climate surfaces for specific locations. It calculates and derives many (>200) monthly, seasonal and annual climate variables. ClimateNA also uses the scale-free data as a baseline to downscale historical and future climate variables for individual years and periods between 1901 and 2100, covering the entirety of North America.

1961-1990 normal climate for all 4-km gridded points.

The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.

Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.

Average summer (June-August) maximum temperature for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.

Average winter (December-February) minimum temperature for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.

Data files in this project were used in an integrated population model for three breeding populations of Wilson’s Warbler (Cardellina pusilla). Data span the years 1992-2008. Data include counts from the North American Breeding Bird Survey (https://www.pwrc.usgs.gov/BBS/RawData/; wiwa_bbs.csv), adult capture histories (wiwa_ch.csv) and age-specific capture data (wiwa_pdat.csv) from the Monitoring Avian Productivity and Survivorship program (http://www.birdpop.org/pages/maps.php), and covariate data derived from the ClimateNA (https://sites.ualberta.ca/~ahamann/data/climatena.html; wiwa_cmd_wt.csv and wiwa_tave_sp.csv) and National Centers for Environmental Prediction (NCEP) ⁄ National Center for Atmospheric Research (NCAR) Reanalysis (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html; wiwa_wind.csv) data sets. Data were prepared in R (https://www.R-project.org/) and the model was implemented with JAGS (http://mcmc-jags.sourceforge.net/). Details of data use, including model code, are reported in Saracco and Rubenstein (in review): “Integrating broad-scale data to assess demographic and climatic contributions to population change in a declining songbird”.

This dataset includes climate data used in the Seedlot Selection Tool. Data were generated by ClimateNA and converted to NetCDF format. Data are organized by region. The regions codes are below:

• ak2 - Alaska
• mexico1 - Mexico
• nc1 - North Central (central Canada, North-central United States)
• ne1 - North East (Eastern Canada, North-Eastern United States)
• uscentral1 - Central United States
• useast1 - Eastern United States
• west2 - Western United States

Average annual total precipitation for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.

Patch-level summary of 8 climatic characteristics at each of 1,865 talus patches across 3 regions in the Rocky Mountains. Dataset notes the year in which the patch was surveyed for pikas, the values of its climatic characteristics (estimated from the ClimateNA dataset), the name of each talus patch, and which of the 3 regions each patch occurs in.

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

Herbarium records provide a broad spatial and temporal range with which to investigate plant responses to environmental change. Research on plant phenology and its sensitivity to climate has advanced with the increasing availability of digitized herbarium specimens, but limitations of specimen-derived data can undermine the inferences derived from such research. One issue that has received little attention is collection site uncertainty (i.e., error distance), a measure of confidence in the location from which a specimen was collected. We conducted comparative analyses of phenoclimatic models to determine whether spatial deviations of 2, 5, 15, or 25 km between recorded and simulated collection sites, as well as the error distance reported in digitized records, affect estimates of the phenological sensitivity of flowering time to annual temperature and precipitation in a widespread annual California wildflower. In this approach, we considered both spatial and interannual variation in climatic conditions. Simulated site displacements led to increasingly weak estimates of phenological sensitivity to temperature and precipitation anomalies with increasing distances. However, we found no significant effect of reported error distance magnitude on estimates of phenological sensitivity to climate normals or anomalies. These findings suggest that the spatial uncertainty of collection sites among specimens of widely collected plant species may not adversely affect estimates of phenological sensitivity to climate, even though real discrepancies and georeferencing inaccuracy can negatively impact such estimates. Collection site uncertainty merits further attention as a potential source of noise in herbarium data, especially for research on how plant traits respond to spatial and interannual climatic variation. Methods Nemophila menziesii herbarium data were downloaded from the Consortium for California Herbaria (CCH) (https://ucjeps.berkeley.edu/consortium/) and cleaned to remove duplicate specimens (collected on the same date within ~1 km of one another). We then selected specimens collected between 1901 and 2019 (N=1677), the period for which climate data was available. Climate data for our analyses were extracted from ClimateNA v6.40 (see http://climatena.ca/Help for more information). The file 'nemo_full_1901_2019.csv' contains all herbarium records and their associated data, plus climate data associated with each individual herbarium record. New datasets were generated using this primary .csv file to investigate the effects of collection location discrepancies on phenological sensitivity to climate. Specifically, new collection coordinates were generated for each digitized herbarium record by displacing the original coordinates of each by one of four distances (2, 5, 15, or 25 km) in a random direction (replicated 200 times). These data were used to construct phenoclimatic models whose results were compared to those of the original data set. See the README file for more details.

This publication contains spatial data, tabular data and scripts used to analyze the spatial patterns of refugia and associated plant communities following each of several fires in northern New Mexico. Four of the geotiff files were derived during the project (*Kernel.tif) using dNBR (delta Normalized Burn Ratio) or dNDVI (delta Normalized Difference Vegetation Index). The kernel raster data represent density of unburned/low severity grid cells in approximately 10-hectare neighborhoods following the Cerro Grande, Dome, La Mesa, and Las Conchas fire events in 2000, 1996, 1977, and 2011, respectively. The data were produced using a kernel smooth process, with output values range from 0 to 1, representing a gradient in neighborhood density of refugia. In addition, geotiff files of the dNBR for Las Conchas (this version is not available at mtbs.gov, but was provided for the study by S. Howard, USGS-EROS), the dNDVI for La Mesa and the La Mesa footprint (both developed for the Fire atlas for the Gila and Aldo Leopold Wilderness Areas project; https://doi.org/10.2737/RDS-2015-0023) are also included. Finally, the archive contains a digital elevation model (developed by USGS-EROS), cropped to the study area; the DEM was used to derive terrain metrics describing topographic heterogeneity at local and catchment scales. The text files contain R scripts and associated tabular data that can be used to repeat the analysis presented in the publication by performing the following functions: 1) generate the kernel rasters (kernel geotiffs described, above); 2) generate terrain metrics from DEM (geotiff included), 3) sample the kernel rasters, terrain metric outputs and 1 kilometer resolution bioclimatic data (downloaded from https://adaptwest.databasin.org/pages/adaptwest-climatena); 4) develop environmental models from the raster sample data (text file included); and 5) conduct a multivariate analysis of species and communities using species data recorded in the field (text file included).

Survival, phenology and climate dataArtemisia tridentata common garden trait data and corresponding population-source climate variables. Information on column headers for climate variables can be found at: https://adaptwest.databasin.org/pages/adaptwest-climatena. Each sheet of the excel file will need to be saved as a .csv file and run with the R code found at: https://github.com/lchaney/Sagebrush_Synth.Richardson_dryad.xlsx

Climate Distance Mapper is an interactive web mapping application designed to facilitate informed seed sourcing decisions and to aid in directing regional seed collections. Implemented as a shiny web application (Chang et al. 2017), Climate Distance Mapper is hosted on the web at: https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/. The application is designed to guide restoration seed sourcing in the desert southwest by allowing users to interactively match seed sources with restoration sites climatic differences – in the form of multivariate climate distance values – between restoration sites and the surrounding landscape. Climatic distances are based on a combination of variables likely to influence patterns of local adaptation among plant populations, including: mean annual temperature, summer maximum temperature, winter minimum temperature, temperature seasonality, annual temperature range, mean annual precipitation, winter precipitation, summer precipitation, precipitation seasonality, long-term winter precipitation variability, and long-term summer precipitation variability. The climate variables are first transformed into principal components (PCA analysis), which standardizes the variables and accounts for collinearity while emphasizing the most important climate gradients. All climate data is obtained through ClimateNA (Wang et al. 2016), an application for dynamically downscaling PRISM climate data (Daly et al. 2008). The fundamental unit of measure in Climate Distance Mapper is the multivariate climate distance, which is defined as the multivariate Euclidean distance between climate-based principal components at input points and those at other grid cells throughout the chosen spatial extent. All distance calculations incorporate 5 principal components derived from an original set of 12 climate variables. The conversion to principal components standardizes and accounts for collinearity in climate variables, while ensuring that the most important climate gradients are given the most weight (i.e., because principal components are ordered in terms of the variability they express). Conceptually, multivariate Euclidean distance with principal components may be viewed as an approximation of the multivariate Mahalanobis distance calculated on the original climate variables. Mahalanobis distance is the distance between groups weighted by the within-group dispersion. Our procedure for calculating climate distance is thereby meant to emphasize natural gradients that distinguish climatic regimes across landscapes without giving any variable undue weight due to differences in units or scale. All multivariate climate distance values are relativized to the 95th percentile of the maximum possible climate distance in a given region (regions may be set dynamically by the user), such that values roughly correspond to a percentage of the total climate variability (using the 95th percentile of the maximum climate distance reduces the influence of outlier grid cells). This means that a climate distance of 0.2 is roughly analogous to 20% of the total climate variability in the selected region. Within Climate Distance Mapper, users can also constrain results to a specific level of climate similarity (e.g., areas with 90% similar climates). Climate Distance Mapper also supports projections into future climate – either by comparing the current climate at input points with the future climate across the landscape (forward projection, from current climate forward to future climate), or by comparing the future climate at input points with the current climate across the landscape (backward projection, from future climate back to current climate). Future climate is defined as the predicted 30-year average for the 2040-2070 period using an ensemble average of three models from the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report for future projections (IPCC 2014). We selected the RCP 4.5 (moderate emission) and RCP8.5 (high emission) scenarios for projections. The future climate models include CCSM4 (Community Climate System Model, version 4.0), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model, version 3), and HadGEM2-ES (Hadley Centre Global Environmental Model, version 2 (Earth System). All future climate data were generated using ClimateNA (Wang et al. 2016). References: Chang, W., Cheng, J., Allaire, JJ., Xie, Y., McPherson, J. 2017. shiny: Web Application Framework for R. R package version 1.0.0. https://CRAN.R-project.org/package=shiny. Daly, C., M. Halbleib, J. J. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology 28:2031–2064. IPCC. 2014. Climate Change 2014: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11: e0156720. https://doi.org/10.1371/journal.pone.0156720

For assistance, contact Shelby Sundquist, ss3988@nau.edu, https://orcid.org/0000-0001-5379-0008

Folder structure
raw - select raw data products used, if available and a reasonable size. See supplementary data for where to find large data.
UVAFME_src - source code for version of UVAFME model used in this study
mgmt_calc - data associated with management scenario inputs
scripts - creating input files for UVAFME and analyzing output
scripts/inputs - creating input files for TVSF and KLC study areas
scripts/analyses - creating figures from model output in TVSF and KLC
scripts/.../intermediate_data - intermediate data products which required significant computational time or were created by hand
Model input/ output/ logs/ jobfiles/ file_lists
input_data - input data for TVSF under different climate and management scenarios
file_lists - file lists to specify inputs for UVAFME runs
jobfiles - jobs to call UVAFME with each file list
logs - output from jobs to screen for errors
output_data - output data for TVSF under different climate and management scenarios

Subdirectories for above folders
mgmt_testing - contains all 15,819 sites simulated
cf, bau, prj - counterfactual, business-as-usual, adaptive ("projected") management scenarios
hist, gcm45, gcm85 - climate scenario. Historical, RCP4.5 (SSP2), RCP8.5 (SSP5)
unit_XX - each forest subunit. These are separated to improve runtime. In dynamic management scenarios, all sites in a subunit must be run together.
site_mgmt/bsp - 500 black spruce sites selected to test fuel treatments
cf, harv, harvplant, prune, shear, shearplant, thin - different management options UVAFME can test. "-plant" means white spruce seedlings were planted after treatment. Not all of these scenarios were included in published figures.
0degCC:4degCC - linear climate forcings tested to see how much this matters for regrowth. Ultimately, only the historic climate scenario (0degCC) was used in published figures.

Previously published data not included in this zip:
Climate and climate change (temperature, precipitation, relative humidity): Wang et al. 2016 10.1371/journal.pone.0156720; https://climatena.ca/; version 7.31
Topography: Porter et al. 2023 https://www.pgc.umn.edu/data/arcticdem/
Soils:
Sand content: Hengl 2018c https://zenodo.org/records/2525662
Bulk density: Hengl 2018 https://zenodo.org/records/2525665
Topographic wetness index: Hengl 2018a https://zenodo.org/record/1447210
Soil water content Hengl and Gupta 2019 https://zenodo.org/records/2784001
Wind speed: Fick and Hijmans 2017 https://worldclim.org/
Cloud cover: Harris et al. 2014 https://www.cru.uea.ac.uk/

Citations
Fick, S.E. and Hijmans, R.J. (2017) ‘WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas’, International Journal of Climatology, 37(12), pp. 4302–4315. Available at: https://doi.org/10.1002/joc.5086.

Harris, I. et al. (2014) ‘Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset’, International Journal of Climatology, 34(3), pp. 623–642. Available at: https://doi.org/10.1002/joc.3711.

Hengl, T. (2018a) ‘Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM’. Zenodo. Available at: https://doi.org/10.5281/zenodo.1447210.

Hengl, T. (2018b) ‘Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Available at: https://doi.org/10.5281/zenodo.2525662.

Hengl, T. (2018c) ‘Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Zenodo. Available at: https://doi.org/10.5281/zenodo.2525665.

Hengl, T. and Gupta, S. (2019) ‘Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Zenodo. Available at: https://doi.org/10.5281/zenodo.2784001.

Porter, Claire, Ian Howat, Myoung-Jon Noh, Erik Husby, Samuel Khuvis, Evan Danish, Karen Tomko, et al. 2023. “ArcticDEM - Mosaics, Version 4.1.” Harvard Dataverse. https://doi.org/10.7910/DVN/3VDC4W.

Wang, T. et al. (2016) ‘Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America’, PLOS ONE, 11(6), p. e0156720. Available at: https://doi.org/10.1371/journal.pone.0156720.

The raster data layers contained in this archive represent climatic dissimilarity as the distance in multivariate climate space (represented by the 1st and 2nd axes of a principal components analysis based on 11 bioclimatic variables) between current (1981-2010) and future (either 2041-2070 ("2055") or 2071-2100 ("2085")) time periods. Ensemble data are based on mean projections from 15 CMIP5 models (CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R) that were chosen to represent all major clusters of similar AOGCMs. Projections from 8 of these 15 GCMs were also used to generate dissimilarity values based on individual GCM projections. Input bioclimatic data was developed by statistical downscaling using the ClimateNA software developed by T. Wang. Additional description of the data can be found at https://adaptwest.databasin.org/pages/climatic-dissimilarity.

This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.

Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (https://psl.noaa.gov/data/gridded/data.narr.html).

Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (https://climatena.ca/).

Climate Distance Mapper is an interactive web mapping application designed to facilitate informed seed sourcing decisions and to aid in directing regional seed collections. Implemented as a shiny web application (Chang et al. 2017), Climate Distance Mapper is hosted on the web at: https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/. The application is designed to guide restoration seed sourcing in the desert southwest by allowing users to interactively match seed sources with restoration sites climatic differences – in the form of multivariate climate distance values – between restoration sites and the surrounding landscape. Climatic distances are based on a combination of variables likely to influence patterns of local adaptation among plant populations, including: mean annual temperature, summer maximum temperature, winter minimum temperature, temperature seasonality, annual temperature range, mean annual precipitation, winter precipitation, summer precipitation, precipitation seasonality, long-term winter precipitation variability, and long-term summer precipitation variability. The climate variables are first transformed into principal components (PCA analysis), which standardizes the variables and accounts for collinearity while emphasizing the most important climate gradients. All climate data is obtained through ClimateNA (Wang et al. 2016), an application for dynamically downscaling PRISM climate data (Daly et al. 2008). The fundamental unit of measure in Climate Distance Mapper is the multivariate climate distance, which is defined as the multivariate Euclidean distance between climate-based principal components at input points and those at other grid cells throughout the chosen spatial extent. All distance calculations incorporate 5 principal components derived from an original set of 12 climate variables. The conversion to principal components standardizes and accounts for collinearity in climate variables, while ensuring that the most important climate gradients are given the most weight (i.e., because principal components are ordered in terms of the variability they express). Conceptually, multivariate Euclidean distance with principal components may be viewed as an approximation of the multivariate Mahalanobis distance calculated on the original climate variables. Mahalanobis distance is the distance between groups weighted by the within-group dispersion. Our procedure for calculating climate distance is thereby meant to emphasize natural gradients that distinguish climatic regimes across landscapes without giving any variable undue weight due to differences in units or scale. All multivariate climate distance values are relativized to the 95th percentile of the maximum possible climate distance in a given region (regions may be set dynamically by the user), such that values roughly correspond to a percentage of the total climate variability (using the 95th percentile of the maximum climate distance reduces the influence of outlier grid cells). This means that a climate distance of 0.2 is roughly analogous to 20% of the total climate variability in the selected region. Within Climate Distance Mapper, users can also constrain results to a specific level of climate similarity (e.g., areas with 90% similar climates). Climate Distance Mapper also supports projections into future climate – either by comparing the current climate at input points with the future climate across the landscape (forward projection, from current climate forward to future climate), or by comparing the future climate at input points with the current climate across the landscape (backward projection, from future climate back to current climate). Future climate is defined as the predicted 30-year average for the 2040-2070 period using an ensemble average of three models from the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report for future projections (IPCC 2014). We selected the RCP 4.5 (moderate emission) and RCP8.5 (high emission) scenarios for projections. The future climate models include CCSM4 (Community Climate System Model, version 4.0), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model, version 3), and HadGEM2-ES (Hadley Centre Global Environmental Model, version 2 (Earth System). All future climate data were generated using ClimateNA (Wang et al. 2016). References: Chang, W., Cheng, J., Allaire, JJ., Xie, Y., McPherson, J. 2017. shiny: Web Application Framework for R. R package version 1.0.0. https://CRAN.R-project.org/package=shiny. Daly, C., M. Halbleib, J. J. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology 28:2031–2064. IPCC. 2014. Climate Change 2014: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11: e0156720. https://doi.org/10.1371/journal.pone.0156720