A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado

The fundamental rationale for statistical downscaling is that the raw outpu ts of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-su rface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most n otably those pertaining to the hydrological cycle) and because GCM output i s often unreliable at individual and sub-grid box scales. By establishing e mpirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictand s (such as precipitation), statistical downscaling has been proposed as a p ractical means of bridging this spatial difference. This study compared thr ee sets of current and future rainfall-runoff scenarios. The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM ou tput; and (3) raw GCM output corrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output o f the UK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in ord er to downscale daily precipitation and temperature series for the Animas R iver in the San Juan River basin, Colorado. Significant differences arose b etween the modelled snowpack and how regimes of the three future climate sc enarios. Overall, the raw GCM output suggests larger reductions in winter/s pring snowpack and summer runoff than the downscaling, relative to current conditions. Further research is required to determine the generality of the water resource implications for other regions, GCM outputs and downscaled scenarios. (C) 1999 Elsevier Science B.V. All rights reserved.