Climate data and scenarios: resources for applied ecologists

As an applied ecologist, my research revolves around advancing our understanding of the effects of climate change and land-use dynamics on wildlife species at the biological scale of populations and communities, typically at biogeographical scales. I use a variety of data and types of models (statistical and simulation) to better understand and predict the consequences of past and future climate change. The climate data available to ecologists has grown fast and furiously over the past years. This is a good thing (data is getting better, read more temporally and spatially resolved); so there are more options for matching the most appropriate climate/weather data to fit the purpose of the research. The increasing diversity of options can sometimes make navigating the waters of climate data a bit of choppy ride, or are at least overwhelming. There are differences among the data, with some a better fit for certain applications than others. The following is more of a list of different data sets, with some metadata that are critical as a first-pass info that can help you decide if they are appropriate for your needs. I aim to update as best as possible, but this is definitely never going to be an exhaustive list.

Some acronyms and terminology

RCPs (Representative Concentration Pathways): based on IPCC 5th Assessment that developed Representative Concentration Pathways, which are greenhouse gas concentration (not emission) trajectories. Supersedes but not necessarily replaces (sort of a complement to) the 4th Assessment Special Report on Emission Scenarios (SRES).

GCM (General Circulation Model)

RCM (Regional Circulation Model)

Downscaling: 

  1. Statistical
  2. Dynamical

Modelled data

Worldclim

WorldClim is a set of global climate layers (gridded climate data) with a spatial resolution of about 1 km2. These data can be used for mapping and spatial modeling. (from worldclim website)

Data layers were created based on interpolation of average monthly climate data from weather station data.

Variables: monthly minimum and maximum temperature, precipitation, and 'bioclimatic' variables.
Spatial resolution: 10 minutes, 5 minutes, 2.5 minutes, and 30 seconds resolution
Temporal resolution: 20-year, 30-year averages, depending on time period
Time periods: 
1. Current: ~1960-1990 
2. Future: 2050 (average for 2041-2060) and 2070 (average for 2061-2080)
3. Past: Mid-holocene (~6000 years ago), Last Glacial Maximum (~22,000 years ago) 
 
GCMs: Various, >15. Some models with different runs.
Downscaling method: Delta change
Bias corrected: Yes
IPCC version: 5th Assessment (CMIP5)
SRES/RCPs: 4 RCPS, 2.6, 4.5, 6, 8.5)
Common applications: Climate change impact assessment
Example applications:

CGIAR research program on climate change, agriculture, and security (CCAFS)

The CCAFS-Climate data portal provides global and regional future high-resolution climate datasets that serve as a basis for assessing the climate change impacts and adaptation in a variety of fields including biodiversity, agricultural and livestock production, and ecosystem services and hydrology. from http://www.ccafs-climate.org/

Variables: 
Spatial resolution: 
Temporal resolution: User-specified
Time periods:  
GCMs: Various, >15. Some models with different runs.
Downscaling method: Delta change
Bias corrected: Yes
IPCC version: 5th Assessment (CMIP5)
SRES/RCPs: 4 RCPS, 2.6, 4.5, 6, 8.5)
Common applications: Climate change impact assessment
Example applications:

Bureau of land reclamation

CMIP3 and CMIP5 high resolution projections

Variables: 
Spatial resolution: 
Temporal resolution:
Time periods:  
GCMs:
Downscaling method: 
Bias corrected: 
IPCC version:
SRES/RCPs: 
Common applications: Climate change impact assessment
Example applications:

ClimateNA: Adaptwest, Climate data and scenarios for North America

Variables: 
Spatial resolution: 
Temporal resolution:
Time periods:  
GCMs:
Downscaling method: 
Bias corrected: 
IPCC version:
SRES/RCPs:
Common applications: Climate change impact assessment
Example applications:

ClimateSA: Climate data and scenarios for South America

Software used to extract data is in beta-version.

PRISM Climate data

Interpolated gridded output of observation-based data. Interpolation based on the Parameter-elevation Relationships on Independent Slopes Model.

Climate velocity datasets

Climate velocity is a metric that quantifies potential exposure climate change.
It can be interpreted as the distance a species would need to move to maintain
constant climate conditions. While there are a few algorithms used to calculate
velocity, generally it is the rate of climate change (temporal difference)
divided by the rate of spatial climate variability.
References:
Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD 2009. The 
velocity of climate change. Nature, 462, 1052–1055.
Hamann, A., D. R. Roberts, Q. E. Barber, C. Carroll, and S. E. Nielsen. 2015. 
Velocity of climate change algorithms for guiding conservation and management. 
Global Change Biology 21:997–1004.

Velocity grids for North America

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