This dataset has been prepared for the Climate Adaptation Conservation Planning Database for Western North
America (CACPD) project, funded by the Wilburforce Foundation with additional contributions from the
GenomeCanada AdapTree project.
It is based on Parameter Regression of Independent Slopes Model (PRISM) climate data for the 1961-1990 normal
period (see http://tinyurl.com/ClimateWNA), and climate change projections of the Coupled Model Intercomparison
Project phase 3 (CMIP3) database corresponding to the 4th IPCC Assessment Report for future projections.
The velocity of climate change is an elegant analytical concept that can be used to evaluate the exposure of
organisms to climate change. In essence, one divides the rate of climate change by the rate of spatial climate
variability to obtain a speed at which species must migrate over the surface of the earth to maintain constant
climate conditions. Here, we provide data from an improved algorithm that conforms to standard
velocity calculations if climate equivalents are nearby. Otherwise, the algorithm extends the search for climate
refugia globally. Below we provide velocity surfaces for mean annual temperature (MAT) as well
as a multivariate PCA implementation that searches for climate matches in 12 biologically
relevant climate variables.
Futher we distinguish, forward and backward velocities allowing useful inferences about conservation of species
(present-to-future velocities) and management of species populations (future-to-present velocities). For the
forward calculation we ask: what is the rate at which an organism in the current landscape has
to migrate to maintain constant climate conditions? Conversely, in the reverse calculation we
ask: given the projected future climate habitat of a grid cell, what is the minimum rate of migration for an
organism from equivalent climate conditions to colonize this climate habitat? For more information see:
Hamann,
A. and Roberts, D.R., Barber, Q.E., Carroll, C. and Nielsen, S.E. 2014. Velocity of climate change algorithms for guiding conservation and management. Global
Change Biology Pre-print on-line, DOI: 10.1111/gcb.12736.
Algorithms for generating velocity surfaces
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Download
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Sample data for algorithms below
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ASCII: |
Univariate, complete search, simplest algorithm
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Word: |
Univariate, complete search, faster algorithm
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Word: |
Univariate, kNN search, xy coordinates, fastest algorithm *
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Word *: |
Univariate, kNN search, smoothed grids
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Word: |
Multivariate, kNN search, xy coordinates, fastest algorithm
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Word: |
Multivariate, kNN search, smoothed grids
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Word: |
*) recommended code to get started. Default settings should work with any mean annual temperature
grid.
Click on the thumbnails below and use the zoom tool
to see
high resolution images of univariate and multivariate, forward and reverse climate change velocities for western
North America
Download links for gridded data (1km resolution)
The gridded climate layers downloadable below are in Lambert Conformal Conic projection, at 1km resolution, and
covering North America, west of 100° longitude. The database consists of ESRI grids, a native format of
ArcGIS software, but compatible with many other GIS applications.
Type of velocity of climate change calculation
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Emission scenario1 |
Future period2
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7 selected CMIP3 AOGCMs3
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Average Ensemble Projection
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Mean Annual Temperature, Forward (present to future)
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A1B |
2020s |
ASCII: |
ASCII: |
Mean Annual Temperature, Forward (present to future)
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A1B |
2050s |
ASCII: |
ASCII: |
Mean Annual Temperature, Forward (present to future)
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A1B |
2080s |
ASCII: |
ASCII: |
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Mean Annual Temperature, Reverse (future to present)
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A1B |
2020s |
ASCII: |
ASCII: |
Mean Annual Temperature, Reverse (future to present)
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A1B |
2050s |
ASCII: |
ASCII: |
Mean Annual Temperature, Reverse (future to present)
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A1B |
2080s |
ASCII: |
ASCII: |
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Multivariate, Forward (present to future)
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A1B |
2020s |
ASCII: |
ASCII: |
Multivariate, Forward (present to future)
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A1B |
2050s |
ASCII: |
ASCII: |
Multivariate, Forward (present to future)
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A1B |
2080s |
ASCII: |
ASCII: |
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Multivariate, Reverse (future to present)
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A1B |
2020s |
ASCII: |
ASCII: |
Multivariate, Reverse (future to present)
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A1B |
2050s |
ASCII: |
ASCII: |
Multivariate, Reverse (future to present)
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A1B |
2080s |
ASCII: |
ASCII: |
1) B1: low emssion scenario, A1B: moderate emission scenario, A2: high emssion
scenario.
2) 2020s: average for years 2011-2040, 2050s: 2041-2070, 2080s: 2071-2100.
3) 7 AOGCMs selected based on the table and graph below.NOTE April 20, 2020: A minor downscaling error was found in a subset of the monthly and seasonal variables for HadGEM2-ES, and we have disabled download of velocity data based on this GCM until further notice.
How to select future climate scenarios
Selecting scenarios for climate change impact and adaptation research is a complex task. Not all future projections
have
equal resolution, number of
runs,
and validation statistics against past climate. We selected a representative set of 7
AOGCMs
from 14 high-quality models, from a total of 23 CMIP3 AOGCMs.
To further whittle the number of AOGCMs under consideration down, researchers often select "worst case", "best
case", and "median" climate change projections. However, what constitutes a worst case or best case scenario,
differs by region and by the climate variable of interest.
The plot on the right may help with scenario selection for the entire study area, and the table below for scenario
selection for regions within western North America. We also provide ensemble scenarios for download above, but they
may have unrealistic combinations of individual climate variables.