Chapter 1: Climate Dissimilarity for North America

Dataset Overview 

Climate dissimilarity is a metric of climate-change exposure that summarizes the overall change in climate based on a suite of climate variables representing temperature, moisture, and other climate attributes. For each location (pixel in the gridded dataset), climate conditions were summarized using Principal Components Analysis, for baseline/historical conditions and for projected future climate conditions (see chapter glossary for definitions of terms). Climate dissimilarity represents a holistic quantitative estimate of the magnitude of change between baseline and future climate (figure 1.1).

Data Access:

Figure 1.1


Conservation Applications

Potential conservation applications of this dataset could include the following: 

  • Climate dissimilarity is one metric of climate exposure that can provide information on climate vulnerability. Belote et al. (2018) suggest that climate vulnerability be considered along with conservation value for any location to determine optimal conservation strategies; see figure 1 in Belote et al. (2018). For example, in areas with high conservation value and low climate vulnerability (e.g., low climate dissimilarity) managers may wish to emphasize traditional reserves and protected areas, whereas in high-conservation-value areas with high climate vulnerability, greater flexibility in management approaches may be required.
  • The study that produced this dataset found considerable variation among three metrics of climate exposure (climate dissimilarity, and forward and backward velocity). In the context of uncertainty about climate-change exposure magnitude, "no regrets" conservation strategies (e.g., protecting lands from development, invasive species control, and human impacts mitigation) and diversified management approaches may be helpful.

Applicable scales for detailed spatial assessments:

  • For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
  • At the scale of: a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g., the North Cascades), a single state (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest or in western North America), the continental United States, the North American continent.

Applicable scales for assessing general patterns:

  • Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
  • At the scale of: a small (< 1 km2) nature preserve, a state park or state wildlife area, a local watershed (12-digit hydrologic unit code [HUC-12]).

Use of the dataset in conservation applications may be limited by the following considerations:

  • Climate vulnerability for a given geographic area or species may be influenced by a variety of factors that were not considered in the creation of this dataset. Because this dataset primarily represents climate-change exposure rather than climate-change sensitivity or adaptive capacity, it should be considered along with other information for conservation decision-making purposes. For geographic areas, such information could include degree of human modification, landscape connectivity, and projected land-cover change. For species, such information could include physiological tolerance thresholds, population genetics and demographics, and inter-species interactions.

Past or current conservation applications:

  • The dataset has not yet been used in any on-the-ground conservation applications to the knowledge of the authors of this chapter.

Dataset citation: 
Belote, R. T., C. Carroll, S. Martinuzzi, J. Michalak, J. W. Williams, M. A. Williamson, and G. H. Aplet. 2018. Assessing agreement among alternative climate change projections to inform conservation recommendations in the contiguous United States. Scientific Reports 8:1–13.

Dataset documentation link: (open access)

Data access:
The dataset can be downloaded from:

The dataset can be viewed interactively online (for mid-century) at:

and (for end-of-century) at:

Metadata access: Formal metadata is not available for this dataset.

Dataset corresponding author:
R. Travis Belote
The Wilderness Society

Data type category (as defined in the Introduction to this guidebook): Climate

Species or ecosystems represented: This dataset does not represent any individual species or ecosystems.

Units of mapped values: unitless

Range of mapped values: Ranges of values vary depending on future time period and representative concentration pathway (RCP):

RCP 4.5 for mid-century: 0.134 to 1.645
RCP 8.5 for mid-century: 0.251 to 1.903
RCP 4.5 for end-of-century: 0.216 to 1.773
RCP 8.5 for end-of-century: 0.477 to 3.064

These minimum and maximum values represent climate dissimilarity created using ensembles of climate projections across 15 general circulation models (GCMs). Note that climate dissimilarity datasets are also available for eight of these individual GCMs; minimum and maximum values for these datasets vary by GCM. For more information on GCMs and RCPs, see appendices 1 and 2, respectively.

Spatial data type: a raster dataset (grid)

Data file format(s): GeoTiff (.tif)

Spatial resolution: 1 km

Geographic coordinate system: World Geodetic System (WGS) 1984

Projected coordinate system: World Geodetic System (WGS) 1984 Lambert Azimuthal Equal Area

Spatial extent: Continental (North America) excluding Central America and the Caribbean islands

Dataset truncation: The dataset is truncated along the border separating Mexico from Guatemala and Belize.

Time period represented: Future (later than 2020)

Future time period(s) represented: Mid-century (2041–2070), end-of-century (2071–2100)

Baseline time period(against which future conditions were compared):1981–2010

Methods overview:

For each future period, climatic dissimilarity at each location (pixel) was calculated as the Euclidean distance in climate space between baseline (current) climate conditions and future climate conditions (figure 1.2). Eleven variables were selected to summarize climate conditions: mean annual temperature, mean temperature of the warmest month (MWMT), mean temperature of the coldest month (MCMT), difference between MCMT and MWMT, mean annual precipitation, mean summer precipitation, mean winter precipitation, growing degree days, the number of frost-free days, Hargreaves reference evaporation, and Hargreaves climatic moisture index. Principal components analysis was used to summarize these 11 variables, and the first two principal components (PC1 and PC2) were used to represent climate. PC1 primarily represented temperature while PC2 primarily represented moisture availability. These calculations were conducted under two greenhouse-gas scenarios (RCP 4.5 and RCP 8.5) for the baseline period, and for two future time periods (mid-century and end-of-century). For more information, please consult the dataset citation listed in section 2 of this chapter.

Major input data sources for this dataset included:  Historical climate observations or models; future climate projections

This dataset used the following general circulation models (GCMs): CanESM2*, ACCESS1.0, IPSL-CM5A-MR*, MIROC5, MPI-ESM-LR*, CCSM4*, HadGEM2-ES*, CNRM-CM5*, CSIRO-Mk3.6.0, GFDL-CM3*, INM-CM4*, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, and GISS-E2R

Separate files are provided for individual GCMs marked with * and an ensemble across GCMs is also provided. More information about climate models is available in appendix 2. Detailed information about climate models, including model evaluation and comparison among models, is available from Randall et al. (2007) and Rupp et al. (2013).

This dataset used the following greenhouse-gas scenarios:  RCP 4.5, RCP 8.5

More information about greenhouse-gas scenarios is available in appendix 2 and from Knutti and Sedláček (2013).

Creation of this dataset involved the following methods to change the spatial resolution of climate models (e.g., to downscale or resample climate models): Downscaling of climate models was not conducted as part of the study that produced this dataset, however, downscaled climate projections were used as inputs. Information on downscaling methods that produced these inputs is available from Wang et al. (2016).

Figure 1.2

The mapped values of the dataset may be interpreted as follows:

High values for climate dissimilarity represent locations where climate is projected to change dramatically (considering multiple climate variables) whereas low values represent locations with projections of modest climate change. Climate-dissimilarity values can be compared across space (from one location to another) within a given greenhouse-gas scenario and time period, or alternatively can be compared for a given location across time periods and/or greenhouse-gas scenarios. For a given combination of location, greenhouse-gas scenario, and time period, the variability in climate-dissimilarity values across datasets that were created using different GCMs provides information about uncertainty in climate-change projections.

Representations of key concepts in climate-change ecology:

This dataset provides information on climate-change vulnerability by representing climate-change exposure as the overall magnitude of change across a suite of climate variables. The degree of climate-change exposure predicted for a given area is an important consideration in anticipating the nature and rate of potential ecological shifts, such as changes in species compositions (as species leave the area due to unsuitable climate or arrive from other regions tracking newly suitable climate conditions) or species’ adaptations to changing conditions (such as through changes in seasonal timing). Different metrics of climate-change exposure may show different results for a given geographic area (Belote et al., 2018), suggesting that there may be multiple dimensions of climate-change exposure that need to be considered in anticipating a range of possible ecological shifts over time.

This dataset involves the following assumptions, simplifications, and caveats:  

This dataset represents one component of climate-change exposure that may be complemented by also considering other metrics of exposure. Climate dissimilarity is a local metric, meaning that it primarily represents the magnitude of local climate change for an area. Other metrics of climate exposure include forward and backward velocity, which provide information on the rates at which species would need to migrate to keep pace with climate change. Also, the study that produced the climate dissimilarity datasets found considerable geographic variation in the degree of agreement or disagreement across GCMs and greenhouse-gas scenarios regarding the magnitude of climate dissimilarity. This means that confidence is higher in some regions than others about whether climate dissimilarity will be high or low (see figure 2 in Belote et al. 2018).

Quantification of uncertainty:

For each time period (mid-century and end-of-century) and greenhouse-gas scenario (RCP 4.5 and 8.5), uncertainty in climate dissimilarity was assessed by examining variability across eight GCMs.

Field verification:

Field verification of the mapped values in this dataset was not possible because the dataset represents a future condition. Datasets representing the baseline time period were derived from downscaled climate grids from the ClimateNA dataset (Wang et al. 2016). Accuracy of the baseline climate variables in the ClimateNA dataset was assessed using monthly mean observations from 4,891 weather stations across North America, located predominantly in the United States; see figure 1 and table 2 in Wang et al. (2016).

Prior to dataset publication, peer review was conducted by external review (at least two anonymous reviewers, each from a different institution).

Belote, R. T., C. Carroll, S. Martinuzzi, J. Michalak, J. W. Williams, M. A. Williamson, and G. H. Aplet. 2018. Assessing agreement among alternative climate change projections to inform conservation recommendations in the contiguous United States. Scientific Reports 8:1–13.

Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.

Randall, D., R. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, and et al. 2007. Cilmate models and their evaluation. in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. Miller, editors. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, NY.

Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research 118: 884–907.

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.