SNAP employs a variety of modeling and research methods that have been approved by the scientific community through large-scale research programs and peer-reviewed publications. In order to make global climate data useful for planning, SNAP downscales global model outputs to the local level.
SNAP selects the 5 Global Climate Models (GCM) that perform best in Alaska and the Arctic. Outputs from these models are then downscaled using PRISM data—which accounts for land features such as slope, elevation, and proximity to coastlines—as baseline climate data. This same downscaling procedure is applied to historical Climate Research Unit (CRU) data. The final products are high resolution monthly climate data for ~1901-2100 for Alaska and large regions of Canada. Where PRISM data are not available, GCM and historical data are downscaled to other baseline climate datasets such as CRU data products. Outputs are available for individual models and for a 5 model average, which reduces some types of errors associated with dependence on a single model. As new data become available, we continually update the SNAP climate datasets, applying these same methods to other areas of the Arctic and the world.
Our principal products are downscaled historical and projected monthly climate data, primarily temperature and precipitation. Projected data are produced for three emission scenarios (B1, A1B, A2). Additionally, SNAP produces derived data from the above base datasets through various modeling efforts. Derived data products include potential evapotranspiration, vegetation, fire, permafrost, day of freeze, day of thaw, the subsequent length of growing season, as well as decadal, seasonal and annual averages. For a full list of our available data, please visit the SNAP Data page. To explore the data with an interactive map, please visit the map tool.
As with any data, analysis or interpretation, multiple sources of uncertainty are always present. Understanding the uncertainty inherent in the input and output data can help in determining how these climate projections are best utilized and interpreted.