An overview of the PRUDENCE fine resolution climate model experiments for Europe is presented in terms of their climate change signals, in particular 2-meter temperature and precipitation. A comparison is made with regard to the seasonal variation in climate change response of the different models participating in the project. In particular, it will be possible to check how representative a particular PRUDENCE regional experiment is of the overall set in terms of seasonal values of temperature and precipitation. This is of relevance for such further studies and impact models that for practical reasons cannot use all the PRUDENCE regional experiments. This paper also provides some guidelines for how to select subsets of the PRUDENCE regional experiments according to such main sources of uncertainty in regional climate simulations as the choice of the emission scenario and of the driving global climate model.

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.

The analysis of possible regional climate changes over Europe as simulated by ten regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models.Two fundamental aspects of model validation are addressed here: the ability to simulate i) the longterm (30 or 40 years) mean climate and ii) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer.In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.4

[1] Within the framework of the European project ENSEMBLES (ensembles-based predictions of climate changes and their impacts) we explore the systematic bias in simulated monthly mean temperature and precipitation for an ensemble of thirteen regional climate models (RCMs). The models have been forced with the European Centre for Medium Range Weather Forecasting Reanalysis (ERA40) and are compared to a new high resolution gridded observational data set. We find that each model has a distinct systematic bias relating both temperature and precipitation bias to the observed mean. By excluding the twenty-five percent warmest and wettest months, respectively, we find that a derived second-order fit from the remaining months can be used to estimate the values of the excluded months. We demonstrate that the common assumption of bias cancellation (invariance) in climate change projections can have significant limitations when temperatures in the warmest months exceed 4-6°C above present day conditions. Citation: Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher (2008), On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709,

Climatic changes, including altered precipitation regimes, will affect key ecosystem processes, such as plant productivity and biodiversity for many terrestrial ecosystems. Past and ongoing precipitation experiments have been conducted to quantify these potential changes. An analysis of these experiments indicates that they have provided important information on how water regulates ecosystem processes. However, they do not adequately represent global biomes nor forecasted precipitation scenarios and their potential contribution to advance our understanding of ecosystem responses to precipitation changes is therefore limited, as is their potential value for the development and testing of ecosystem models. This highlights the need for new precipitation experiments in biomes and ambient climatic conditions hitherto poorly studied applying relevant complex scenarios including changes in precipitation frequency and amplitude, seasonality, extremity and interactions with other global change drivers. A systematic and holistic approach to investigate how soil and plant community characteristics change with altered precipitation regimes and the consequent effects on ecosystem processes and functioning within these experiments will greatly increase their value to the climate change and ecosystem research communities. Experiments should specifically test how changes in precipitation leading to exceedance of biological thresholds affect ecosystem resilience and acclimation.