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

Flooding is a very costly natural hazard in the UK and is expected to increase further under future climate change scenarios. Flood defences are commonly deployed to protect communities and property from flooding, but in recent years flood management policy has looked towards solutions that seek to mitigate flood risk at flood-prone sites through targeted interventions throughout the catchment, sometimes using techniques which involve working with natural processes. This paper describes a project to provide a succinct summary of the natural science evidence base concerning the effectiveness of catchment-based ‘natural’ flood management in the UK. The evidence summary is designed to be read by an informed but not technically specialist audience. Each evidence statement is placed into one of four categories describing the nature of the underlying information. The evidence summary forms the appendix to this paper and an annotated bibliography is provided in the electronic supplementary material.

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Six statistical and two dynamical downscaling models were compared with regard to their ability to downscale seven seasonal indices of heavy precipitation for two station networks in northwest and southeast England. The skill among the eight downscaling models was high for those indices and seasons that had greater spatial coherence. Generally, winter showed the highest downscaling skill and summer the lowest. The rainfall indices that were indicative of rainfall occurrence were better modelled than those indicative of intensity. Models based on non-linear artificial neural networks were found to be the best at modelling the inter-annual variability of the indices; however, their strong negative biases implied a tendency to underestimate extremes. A novel approach used in one of the neural network models to output the rainfall probability and the gamma distribution scale and shape parameters for each day meant that resampling methods could be used to circumvent the underestimation of extremes. Six of the models were applied to the Hadley Centre global circulation model HadAM3P forced by emissions according to two SRES scenarios. This revealed that the inter-model differences between the future changes in the downscaled precipitation indices were at least as large as the differences between the emission scenarios for a single model. This implies caution when interpreting the output from a single model or a single type of model (e.g. regional climate models) and the advantage of including as many different types of downscaling models, global models and emission scenarios as possible when developing climate-change projections at the local scale.

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