Beyond the downscaling comparison study

This Special Issue of the International Journal of Climatology draws upon papers given at the session convened by the guest editors at the European Geosciences Union meeting in Vienna in April 2006: ‘Linking climate change modelling to impacts studies: downscaling techniques for hydrological impact studies’. The session explored some of the latest developments in ‘downscaling’ techniques, commonly used to address the scale mismatch between coarse resolution global climate model (GCM) output and the regional or local catchment scales required for climate change impact assessment and hydrological modelling. Presentations covered the development of new downscaling techniques, the inter-comparison of downscaling methods, the downscaling of extremes, and progress with quantifying uncertainties in the estimation of climate change impacts (such as the use of multimodel ensembles and probabilistic methods). The main questions addressed by the session were how can these innovations be used in hydrological impact studies, and what further steps are needed to embed downscaling in the adaptation process? Although the last decade has witnessed a plethora of publications on downscaling from climate models, very few studies consider impacts per se, and even fewer (about one in six of all downscaling studies) examine hydrological impacts. Even when studies do have an applied element, consideration is seldom given to how results might enable stakeholders and managers to make more informed, robust decisions on adaptation in the face of deep uncertainty about the future. In fact, apart from a handful of often cited examples, downscaling studies are conspicuously absent in the recent reviews on climate change and water adaptation (e.g. EEA, 2007; Kundzewicz et al., 2007). Paradoxically, the rhetoric has become much more confident about projected changes in temperature and even precipitation at regional scales (Christensen et al., 2007). Somewhere along the line there has been a disconnection between the suppliers and users of regional climate change scenarios for adaptation and resource planning. It is hoped that this Special Issue will catalyse a debate about applied downscaling research and show the need to begin mainstreaming such work within adaptation frameworks. This will involve the identification of technical (and institutional) constraints, as well as options for improving access to, and use of, downscaled scenarios in climate change risk assessments. Fowler et al. (this Special Issue) set the scene by comprehensively reviewing contemporary downscaling literature through a hydrological lens. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts. The review then considers recent developments in the construction of climate scenarios which offer potential for methodological advances in the field. These include probabilistic modelling using multi-model ensembles, pattern-scaling and downscaling of multiple variables. An example is given to show how these techniques may be merged into a probabilistic climate change scenario framework for assessing uncertainties associated with climate change projections. Recommendations are made for future research priorities, including the provision of decision-making tools for planning and management that are robust to future uncertainties. Salathé et al. (this Special Issue) then review the methods developed by the Climate Impacts Group (CIG) at the University of Washington to evaluate and downscale GCM simulations for the integrated assessment of climate impacts on hydrologic systems in the Pacific Northwest, U.S. The approach is intended to support regional water resource management and the different downscaling methods used by the group are described. Many of these are simple empirical corrections of global climate model data. However, the performance of statistical downscaling and a high-resolution (15 km) dynamical downscaling method are also evaluated. The regional climate model (RCM) shows important differences in the regional climate response from that captured by GCMs and statistical downscaling. For example, localised amplifications of warming unseen by GCMs are shown by the RCM to be due to changes in the local surface radiation budget caused by the loss of snow and increased cloudiness. The next two papers use statistical methods to downscale information from multiple GCMs to examine hydrological impacts and the uncertainties introduced by the choice of GCM and emissions scenario. In Gachon and Dibike (this Special Issue) the downscaling tool SDSM (Wilby et al., 2002) is assessed with respect to simulated changes in mean and extreme temperatures for specific locations in northern Canada. The study uses outputs from two GCMs (CGCM2 and HadCM3) and two emissions scenarios (SRES A2 and B2) to explore temperature projections for 2070–2100. The statistical downscaling step provides additional information on temperature change not captured by the direct use of GCM outputs, including the effects of synoptic scale forcings, and is found to reduce inter-model differences in projections. However, SDSM is found to be conservative in the presence of non-stationarity in the climate system,