Chapter 2: Multivariate Climate Velocity

Dataset Overview

This dataset provides estimates of climate-change velocity (Hamann et al. 2015) produced in several ways (see chapter glossary for definitions of terms). Forward velocity provides a measure of the rate at which an organism would need to migrate over the land surface to maintain similar climate conditions, as regional climate conditions change. Backward (or reverse) velocity utilizes a target location (pixel) of interest and measures the rate at which an organism from similar climate conditions would need to migrate to colonize that target pixel (figure 2.1). These two types of multivariate climate velocity metrics can complement each other. Whereas forward velocity provides climate-change exposure information for species or populations inhabiting particular areas, backward velocity provides information for the areas themselves, in terms of the accessibility of those locations to the biotic communities that could potentially inhabit them in the future.

Data Access:

Figure 2.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset could help inform climate-vulnerability assessments for species or geographic areas. For this application, the climate velocity metrics in this dataset would ideally be combined with other sources of information, such as species-specific information (e.g., population demographics and dispersal abilities), "biotic velocity" metrics (see Carroll et al. 2015), and fine-scale assessments representing possible microclimate refugia.

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:

  • A variety of considerations that may contribute to climate vulnerability for species or geographic areas were not considered in the creation of this dataset. For example, this dataset primarily represents climate-change exposure rather than climate-change sensitivity, such that it does not represent possible differences among species in their tolerances of changing climate conditions. Other relevant considerations for climate-change vulnerability analyses may include information on population genetics and demographics, non-climate threats such as pollution and land-use change, and inter-species interactions such as the effects of invasive species.

Past or current conservation applications:

  • At the time of the publication of this guidebook, 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:

Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.

Hamann, A., D. Roberts, Q. Barber, C. Carroll, and S. Nielsen. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21:997–1004.

Dataset documentation links: (open access) (open access)

Data access:

The dataset can be downloaded from:

Note: the two journal articles referenced above describe the general methods used to derive this dataset, however, the dataset at the link above is an updated version of an original dataset that is available from:

The dataset can be viewed interactively online at:

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

Dataset corresponding author:
Carlos Carroll
Klamath Center for Conservation Research
[email protected]

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: kilometers per year (km / year)

Range of mapped values: The range of mapped values depends on the individual data layer. Data layers are provided for both forward and backward velocity, for two future periods, and for two greenhouse-gas scenarios (see sections 5 and 6).

Spatial data type: a raster dataset (grid)

Data file format(s): GeoTiff (.tif), ASCII (.asc)

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: First, 11 climate variables were synthesized using Principal Components Analysis. These 11 variables included: mean annual temperature, mean temperature of the warmest month, mean temperature of the coldest month, mean annual precipitation, mean growing season (May to August) precipitation, degree-days above 5 °C, number of frost-free days, and several indices representing moisture conditions. The results of this analysis were used to find, for each pixel with its given current climate conditions, a set of future climate analogs, i.e., a set of pixels that are predicted to have similar climate conditions in the future. Then, the geographically nearest pixel with analogous future climate was selected and used to calculate climate velocity, which is the straight-line distance from the target pixel of interest to its nearest analog, divided by the time difference between the current time period and the future time period under consideration. For more information, please consult the dataset citation listed in section 2 of this chapter.

This dataset employed the following specific models: yaImpute package in the R software environment (Crookston and Finley, 2008)

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, GISS-E2R

Separate files are provided for each GCM and an ensemble across GCMs is also provided. More information about climate models is available in appendix 1. 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).

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

Forward climate velocity primarily represents climate-change exposure for species inhabiting the geographic areas considered. High forward climate velocity values may suggest a greater threat of local extirpation of species inhabiting a site due to the rapid migration that would be needed to track changing climate conditions. Backward (reverse) climate velocity primarily represents climate-change implications for the geographic area itself that is being considered. High backward velocity indicates that it might be difficult for species to colonize that site in the future (in response to climate change), and according to Carroll et al. (2015), "a site with high backward velocity is threatened with holding a depauperate complement of species adapted to its future climate, with consequent effects on ecosystem function and services".

Representations of key concepts in climate-change ecology:

This dataset represents two ways of conceptualizing climate-change vulnerability based on climate-change exposure (forward and backward climate velocity). Both types of climate velocity have implications for the adaptive capacity of organisms to respond to climate change by migrating across the landscape to track suitable climate conditions. These two types of climate velocity provide different, and potentially complementary, types of information. Locations with high forward velocity may have species at greater risk of local extirpation, whereas locations with high backward velocity may be at greater risk of having few future species adapted to the new local climate. Consideration of multiple metrics of climate-change exposure (including forward and backward velocity along with additional metrics) along with species’ sensitivity to climate change and capacity to adapt to changing conditions is important for a holistic assessment of potential climate-change vulnerability for a given location, habitat, or species range.

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

Unlike the related concept of "biotic velocity" (see Carroll et al., 2015), climate velocities in this dataset do not consider species-specific climate tolerances. Therefore, the backward and forward climate velocities in this dataset represent an upper bound on the migration rate required, because for some species slower migration rates will be enabled due to greater tolerance of changing climate conditions and potentially greater ability to persist in place. In addition, because the hypothetical migration trajectories to future climate analogs were conceptualized as straight lines, actual barriers on the landscape impeding migration were not considered. These might include human-created barriers (e.g., transportation infrastructure or land use) as well as intervening areas of inhospitable climate (e.g., a warm valley separating two cold mountain peaks).

Quantification of uncertainty:

This dataset does not include any quantification of uncertainty relating to the mapped values.

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 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).

Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.

Crookston, N., and A. Finley. 2008. yaImpute: An R Package for kNN Imputation. Journal of Statistical Software 23:1–16.

Hamann, A., D. Roberts, Q. Barber, C. Carroll, and S. Nielsen. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21:997–1004.

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, 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.