Statistical downscaling of general circulation model output: A comparison of methods

Abstract

Abstract. A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centre's coupled ocean/atmosphere model (HadCM2) forced by combined CO2 and sulfate aerosol changes. Climate model results for 1980-1999 (present) and 2080-2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard "WGEN" method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980-1999 and 2080-2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented. IntroductionThe present generation of global general circulation models (GCMs) and higher-resolution limited area models (LAMs) of the climate system are restricted in their usefulness for many subgrid scale applications (including those to hydrology) by their coarse spatial resolution and the uncertain reliability of their output on timescales of months or less, especially for variables pertaining directly to the hydrologic cycle [Carter et al., 1994]. As Hostetler [1994] has observed, the parameterizations used in GCMs and in hydrological models are least reliable on the scale(s) at which these models interface. Hydrological models are frequently concerned with small, subcatchment scale processes and must parameterize regionalscale ones, whereas atmospheric models deal most proficiently with fluid dynamics at the planetary scale and parameterize many regional and smaller-scale processes.Climate model resolution issues have important implications

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“…Such investigations are also highly relevant to the development of empirical circulation-based methods for downscaling from the relatively coarse general circulation model (GCM) scale to the higher spatial resolution required for impact analysis (Hewitson and Crane, 1996;Goodess and Palutikof, 1998;Wilby et al, 1998;Goodess, 2000). In this approach to downscaling, links are identified between large-scale circulation (defined using circulation patterns and indices) and regional or sub-regional climate (such as daily rainfall) and then applied to GCM output.…”

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