Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain

Abstract

[1] Model Output Statistics (MOS) has been recently proposed as an alternative to the standard perfect prognosis statistical downscaling approach for Regional Climate Model (RCM) outputs. In this case, the model output for the variable of interest (e.g. precipitation) is directly downscaled using observations. In this paper we test the performance of a MOS implementation of the popular analog methodology (referred to as MOS analog) applied to downscale daily precipitation outputs over Spain. To this aim, we consider the state-of-the-art ERA40-driven RCMs provided by the EU-funded ENSEMBLES project and the Spain02 gridded observations data set, using the common period 1961-2000. The MOS analog method improves the representation of the mean regimes, the annual cycle, the frequency and the extremes of precipitation for all RCMs, regardless of the region and the model reliability (including relatively low-performing models), while preserving the daily accuracy. The good performance of the method in this complex climatic region suggests its potential transferability to other regions. Furthermore, in order to test the robustness of the method in changing climate conditions, a cross-validation in driest or wettest years was performed. The method improves the RCM results in both cases, especially in the former.Citation: Turco, M., P. Quintana-Seguí, M. C. Llasat, S. Herrera, and J. M. Gutiérrez (2011), Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain,

Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy

“…Our future research would address the problem of the impact of climate change on hydrology, evaluating and calibrating the high-resolution outputs of COSMO-CLM (Zollo et al, 2012) by means of a hybrid downscaling strategy (Turco et al, 2011). This study is the first step in this direction, providing the groundwork to choose the most reliable dataset to develop regional scenarios over the Alpine region.…”

“…The role of this algorithm in atmospheric modelling is extensively described by Kalnay (2003). The method used to generate NWIOI is an implementation of OI, described in detail by Uboldi et al (2008) and concisely presented here. This method differs from kriging since it assumes a meteorological background as first-guess field.…”

Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

“…Previously used scores include overall measures, such as the root mean square error (Piani et al, 2010b) or the Kolmogorov-Smirnov two sample statistic (Dosio and Paruolo, 2011). Other suggested scores assess specific moments of the distribution including the mean (Engen-Skaugen, 2007;Li et al, 2010;Dosio and Paruolo, 2011;Themeßl et al, 2011;Turco et al, 2011;Teutschbein and Seibert, 2012), the standard deviation (Engen-Skaugen, 2007;Li et al, 2010;Themeßl et al, 2011;Teutschbein and Seibert, 2012) and the skewness (Li et al, 2010). A variety of further scores are based on the comparison of the frequency of days with precipitation (Schmidli et al, 2006(Schmidli et al, , 2007Themeßl et al, 2011) and the magnitude of selected (mostly high) percentiles (Schmidli et al, 2006(Schmidli et al, , 2007Li et al, 2010;Themeßl et al, 2011;Teutschbein and Seibert, 2012).…”

“…In recent years a multitude of studies has investigated different post processing techniques, aiming at providing reliable estimators of observed precipitation climatologies given RCM output (e.g. Ines and Hansen, 2006;Engen-Skaugen, 2007;Schmidli et al, 2007;Dosio and Paruolo, 2011;Themeßl et al, 2011;Turco et al, 2011;Chen et al, 2011b;Teutschbein and Seibert, 2012). Among the most popular approaches are statistical transformations that aim to adjust (selected aspects of) the distribution of RCM (e.g.…”

Modelling climate impact on floods with ensemble climate projections

“…MOS removes much of the model error in the precipitation, making it more useful in impact studies, which is considered by many to allow confidence in the examination of future changes in flow regimes in catchments from Europe and the wider world (e.g. Bell et al, 2007, Fowler et al, 2007bLeander and Buishand, 2007;Akhtar et al, 2009;Linde et al, 2010;Marke et al, 2011;Rojas et al, 2011;Turco et al, 2011). However, MOS can potentially also remove much of the spread in the driving variables, which could disrupt signals of climate change.…”