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Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data. [ABSTRACT FROM AUTHOR]

The past and projected future precipitation sum in May-September for two areas in Finland, one located in the south-west (SW) and the other in the north-east (NE), is studied using 13 regional climate simulations and three observational datasets. The conditions in the present-day climate for agricultural crop production are far more favourable in the south-western part of the country than the more continental north-eastern Finland. Based on a new high-resolution observational precipitation dataset for Finland (FMI grid), with a resolution of 10×10 km, the only statistically significant past long-term (1908-2008) precipitation tendencies in the two study regions are positive. Differences between FMI grid and two other observational datasets during 1961-2000 are rather large in the NE, whereas in the SW the datasets agree better. Observational uncertainties stem from the interpolation and sampling errors. The projected increases in precipitation in the early stage of the growing season would be most favourable for agricultural productivity, but the projected increases in August and September might be harmful. Model projections for the future indicate a statistically significant increase in precipitation for most of the growing season by 2100, but the distribution of precipitation within the growing season is not necessarily the most optimal. [ABSTRACT FROM AUTHOR]

A basic analysis is presented for a series of regional climate change simulations that were conducted by the Swedish Rossby Centre and contribute to the PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects) project. For each of the two driving global models HadAM3H and ECHAM4/OPYC3, a 30-year control run and two 30-year scenario runs (based on the SRES A2 and B2 emission scenarios) were made with the regional model. In this way, four realizations of climate change from 1961–1990 to 2071–2100 were obtained. The simulated changes are larger for the A2 than the B2 scenario (although with few qualitative differences) and in most cases in the ECHAM4/OPYC3-driven (RE) than in the HadAM3H-driven (RH) regional simulations. In all the scenario runs, the warming in northern Europe is largest in winter or late autumn. In central and southern Europe, the warming peaks in summer when it locally reaches 10 °C in the RE-A2 simulation and 6–7 °C in the RH-A2 and RE-B2 simulations. The four simulations agree on a general increase in precipitation in northern Europe especially in winter and on a general decrease in precipitation in southern and central Europe in summer, but the magnitude and the geographical patterns of the change differ markedly between RH and RE. This reflects very different changes in the atmospheric circulation during the winter half-year, which also lead to quite different simulated changes in windiness. All four simulations show a large increase in the lowest minimum temperatures in northern, central and eastern Europe, most likely due to reduced snow cover. Extreme daily precipitation increases even in most of those areas where the mean annual precipitation decreases. [ABSTRACT FROM AUTHOR]

This work presents a regional climate model, the Rossby Centre regional Atmospheric model (RCA1), recently developed from the High Resolution Limited Area Model (HIRLAM). The changes in the HIRLAM parametrizations, necessary for climate-length integrations, are described. A regional Baltic Sea ocean model and a modeling system for the Nordic inland lake systems have been coupled with RCA1. The coupled system has been used to downscale 10-year time slices from two different general circulation model (GCM) simulations to provide high-resolution regional interpretation of large-scale modeling. A selection of the results from the control runs, i.e. the present-day climate simulations, are presented: large-scale free atmospheric fields, the surface temperature and precipitation results and results for the on-line simulated regional ocean and lake surface climates. The regional model modifies the surface climate description compared to the GCM simulations, but it is also substantially affected by the biases in the GCM simulations. The regional model also improves the representation of the regional ocean and the inland lakes, compared to the GCM results. [ABSTRACT FROM AUTHOR]