Abstract
Abstract. Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.
Highlights
Global climate models (GCMs) are currently the best tools to model changes in the global climate caused by an increase in radiatively active gases (IPCC, 2007)
The precipitation statistics for daily means and max5day show that all Model Output Statistics (MOS) techniques can remove the mean bias in the daily precipitation (Table 2)
This study evaluated three different methods to further downscale regional climate models (RCMs) precipitation for use in impact studies, the direct method, quantile-quantile mapping and distributions-based scaling
Summary
Global climate models (GCMs) are currently the best tools to model changes in the global climate caused by an increase in radiatively active gases (IPCC, 2007). Despite constant improvements in model resolution and the description of the physical processes, modelling of precipitation in the current model versions is still inadequate for use in most, if not all, local impact studies (Leith and Chandler, 2010; Beven, 2011). Dynamical downscaling, where a regional climate model (RCM) is forced with boundary conditions from a GCM, includes many feedback processes which are important for the energy, radiation and water balances. These models are very expensive to run both in terms of time and resources, and the ability to investigate the full
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