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 outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub-grid box scales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling has been proposed as a practical means of bridging this spatial difference. This study compared three sets of current and future rainfall-runoff scenarios. The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM output; and (3) raw GCM output corrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output of the UK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in order to downscale daily precipitation and temperature series for the Animas River in the San Juan River basin, Colorado. Significant differences arose between the modelled snowpack and how regimes of the three future climate scenarios. Overall, the raw GCM output suggests larger reductions in winter/spring 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.