Evaluation of the skill and added value of a reanalysis‐driven regional simulation for Alpine temperature

Abstract

ABSTRACT:To determine whether the use of regional climate models improves the representation of climate is a crucial topic in climate modelling. An improvement over coarser scale models is expected especially in areas with complex orography or along coastlines. However, some studies have shown no clear added value for regional climate models. In this study a high-resolution regional climate simulation performed with REMO 5.0 (regional model) over the whole of Europe over the period 1958-1998 is analysed for 2-m temperature over the European Alps and their surroundings called the Greater Alpine region (GAR). The simulation is driven by perfect boundary conditions at the lateral boundaries provided by the ERA40 reanalysis and spectral nudging of the large-scale wind fields towards ERA40 values for the upper layers inside the model domain. The added value of the regional simulation (1/6°resolution) is analysed with respect to the driving reanalysis (1.125°resolution).Both the REMO simulation and the ERA40 reanalysis are validated against different station datasets of monthly and daily mean 2-m temperature. Correlation analysis shows that the temporal variability of temperature is well represented by both REMO and ERA40, whereas both show considerable biases. The REMO bias reaches 3 K in summer in regions known to experience a problem with summer drying in a number of regional models. The comparison of REMO and ERA40 shows that an added value of the former exists for all regions in winter. For the regions surrounding the Alps, the added value is absent in summer, whereas in the inner Alpine subregions with most complex orography, REMO performs better than ERA40 during the whole year. The only moderate value added by REMO in this hindcast set-up may be partly explicable by the fact that meteorological measurements are assimilated in the ERA40 reanalysis but not in the REMO simulation.

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

“…Thus, the added value in RCM simulations is commonly significant in regions with fine-scale surface forcing, whereas the performance of RCMs is less improved or even worse than that of the driving GCM over relatively flat regions. For instance, Prömmel and Geyer (Prömmel et al, 2010) also found that some RCMs show larger biases compared to their driving GCM in relatively flat regions surrounding the Alps, especially in summer. In most cases, the five RCMs perform better than the driving GCM HadGEM2-AO.…”

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

“…This implies that, from a practical viewpoint, the key issue in the nesting regional modelling technique evaluation is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data (Prömmel et al 2010). Generally, studies evaluating the relative skill of RCMs and the driving fields are designated as added value (AV) studies (Bärring and Laprise 2005;Rockel et al 2010).…”

“…In recent years, however, the AV problem received increased attention and become the main subject of several studies (Castro et al 2005;Sotillo et al 2005;Duffy et al 2006;Feser 2006;Kanamitsu and Kanamaru 2007;Rauscher et al 2007;Sanchez-Gomez et al 2009;Winterfeldt and Weisse 2009;De Sales and Xue 2010;Prömmel et al 2010) and a central topic in a number of workshops (Bärring and Laprise 2005;Rockel et al 2010). Although some authors (e.g.…”

“…For example, when downscaling reanalyses data, studies generally show that RCMs add value to their driving data for surface variables (e.g., surface temperature and 10-m wind speed) in regions characterized by small-scale orographic features such as mountainous regions (Feser 2006;Prömmel et al 2010) and coastal areas (Sotillo et al 2005;Winterfeldt and Weisse 2009); but little AV and even degradation is sometimes found in regions with no important small-scale physiographic forcings (Winterfeldt and Weisse 2009). Long-term large-scale features (i.e., general circulation) are generally reasonably well reproduced by RCMs (Feser 2006;Sanchez-Gomez et al 2009), but degradation of large-scale fields arises when considering shorter time scales (e.g., daily mean) (Castro et al 2005;Sanchez-Gomez et al 2009).…”

A Bayesian posterior predictive framework for weighting ensemble regional climate models

“…The main advantages of RCMs over GCMs are increased resolution, more parsimony in terms of representing sub-grid-scale processes, and often improved modelling of spatial patterns, particularly in regions with coastlines and considerable topographic features (e.g. van der Linden and Mitchell, 2009;Prömmel et al, 2010;Feser et al, 2011). Current computing power is now allowing for ensembles of regional climate models to be performed, allowing for sampling of model structural uncertainty (Christensen et al, 2007;Giorgi and Bates, 1989;van der Linden and Mitchell, 2009;Mearns et al, 2013;Solman et al, 2013).…”