11.1 The Importance of the Snow Albedo Feedback for Improved Understanding and Projection of Climate Warming over Mountains

Thursday, 16 July 2020: 10:55 AM
Virtual Meeting Room
Justin R. Minder, Univ. at Albany, SUNY, Albany, NY

Handout (13.3 MB)

For many of Earth’s mountain ranges, reduced seasonal snow cover will be one of the most visible manifestations of anthropogenic climate warming. However, this loss of snow cover, and the associated reduction in surface albedo, also actively contributes to climate change via the snow albedo feedback (SAF). The SAF can strongly modulate warming over mountains, producing highly local warming maxima and elevation-dependent warming patterns. These impacts can be represented in regional climate models (RCMs), however, they may be strongly sensitive to model configuration, especially the parameterization of land surface processes.

The role of the SAF in shaping projections of mountain climate change is examined using the Rocky Mountains of the western United States as an example. An ensemble of RCM simulations from the North American component of the Coordinated Regional Downscaling Experiment (NA-CORDEX) is analyzed. Simulations vary in terms of the forcing global model, grid spacing, and RCM used. When comparing historical simulations of the 20th century with RCP-8.5 late 21st century projections, a wide rxange of warming magnitudes and patterns are found during the February to May season. Differences in SAF-strength across ensemble members are shown to strongly contribute to this uncertainty in projected warming. The sources of these differences are investigated, including the role of grid spacing, snow cover extent, and parameterization of surface albedo.

One potential path forward for constraining the simulated SAF is explored using the seasonal cycle. Seasonal warming from gridded station data and albedo changes from satellite remote sensing are used to make observational estimates of SAF-strength. These are then compared to the SAF-strength calculated from RCM simulations of the historical climate to evaluate model realism and narrow the uncertainty in projected mountain climate warming.

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