An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections

Abstract

Ten regional climate models (RCM) have been integrated with the standard forcings of the PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre boundary conditions. The response over Europe, calculated as the difference between the 2071-2100 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance in eight sub-European boxes. Four sources of uncertainty can be evaluated with the material provided by the PRUDENCE project. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30). Model uncertainty is due to the fact that the models use different techniques to discretize the equations and to represent sub-grid effects. Radiative uncertainty is due to the fact that IPCC-SRES A2 is merely one hypothesis. Some RCMs have been run with another scenario of greenhouse gas concentration (IPCC-SRES B2). Boundary uncertainty is due to the fact that the regional models have been run under the constraint of the same global model. Some RCMs have been run with other boundary forcings. The contribution of the different sources varies according to the field, the region and the season, but the role of boundary forcing is generally greater than the role of the RCM, in particular for temperature. Maps of minimum expected 2m temperature and precipitation responses for the IPCC-A2 scenario show that, despite the above mentioned uncertainties, the signal from the PRUDENCE ensemble is significant.

Projected Change—Models and Methodology

“…In a recent study, Déqué et al (2012) investigated sources of uncertainty in the ENSEMBLES GCM-RCM ensemble. This new study confirmed the results of Déqué et al (2007) in that the choice of GCM is the dominant source of uncertainty. But there are exceptions, such as for summertime precipitation, when it is RCM formulation that may be the dominant source of uncertainty.…”

“…The PRUDENCE project mainly addressed uncertainty related to RCM formulation with 11 RCMs downscaling one and the same GCM under the same GHG emission scenario, but there were also other GCMs and emission scenarios included in that project . Based on these results, Déqué et al (2007) concluded that uncertainty in future European climate change is generally more associated with the choice of GCM than with which RCM is used, particularly for temperature. Consequently, in the ENSEMBLES project, there was an emphasis on having a larger ensemble with more GCMs involved (van der Linden and Mitchell 2009).…”

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

“…It has been shown that the driving BC are the major contribution to the uncertainties in regional climate projections (Déqué et al 2007;Rowell 2006), although the contribution of the different sources of uncertainties depends on the region, the season and the variable. For example, in a similar experiment carried over Europe (Déqué et al 2014), it was found that SST correction had little impact on the simulated 2-m temperature and precipitation biases, but the improved atmospheric lateral BC as a consequence of using an intermediate-resolution AGCM had a large impact in reducing biases in the historical period for JJA and SON seasons; for the other seasons however, results were different.…”

“…Due to the empirical nature of the correction that assumes that historical biases of SSC persist in the future, it is of course impossible to say whether the differences in projected climate changes with the 3-step DD compared to those obtained with the usual 2-step DD are beneficial or not. But certainly the difference in projected changes is part of the modelling uncertainty at regional scale (Déqué et al 2007). An advantage of the reduced bias of historical GCM-driven RCM simulations is a reduced need for empirical adjustment of RCM-simulated data for use by impact community.…”

Grenzen und Herausforderungen der regionalen Klimamodellierung

“…Viele Untersuchungen haben gezeigt, dass unterschiedliche Regionalmodelle und Modellkonfigurationen den beobachteten Jahresgang und das klimatologische Mittel von Niederschlag, Temperatur und großräumiger Zirkulation über Europa mehrheitlich gut wiedergeben (Giorgi und Mearns 1999;Déqué et al 2007;Jacob et al 2007;Kotlarski et al 2014). Die Regionalmodelle reproduzieren dabei generell die großräumige Zirkulation des antreibenden Globalmodells (Vautard et al 2013).…”