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Recent winter seasons have evidenced that global warming does not exclude the occurrence of exceptionally cold and/or snowy episodes in the Northern mid-latitudes. The expected rarefaction of such events is likely to exacerbate both their societal and environmental impacts. This paper therefore aims to evaluate model uncertainties underlying the fate of wintertime cold extremes over Europe. Understanding why climate models (1) still show deficiencies in simulating present-day features and (2) differ in their responses under future scenarios for the twentyfirst century indeed constitutes a crucial challenge. Here we propose a weather-regime approach in order to separate the contributions of large-scale circulation and non-dynamical processes to biases or changes in the simulated mean and extreme temperatures. We illustrate our methodology from the wintertime occurrence of extremely cold days in idealized atmosphere-only experiments performed with two of the CMIP5 climate models (CNRM-CM5 and IPSL-CM5A-LR). First we find that most of the present-day temperature biases are due to systematic errors in non-dynamical processes, while the main features of the large-scale dynamics are well captured in such experiments driven by observed sea-surface temperatures, with the exception of a generalized underestimation of blocking episodes. Then we show that uncertainties associated with changes in large-scale circulation modulate the depletion in cold extremes under an idealized scenario for the late twentyfirst century. These preliminary results suggest that the original methodology proposed in this paper can be helpful for understanding spreads of larger model-ensembles when simulating the response of temperature extremes to climate change. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable “a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future applications. [ABSTRACT FROM AUTHOR]

Europe and in particular its southern part are expected to undergo serious climate changes during summer in response to anthropogenic forcing, with large surface warming and decrease in precipitation. Yet, serious uncertainties remain, especially over central and western Europe. Several mechanisms have been suggested to be important in that context but their relative importance and possible interplays are still not well understood. In this paper, the role of soil-atmosphere interactions, cloud-temperature interactions and land-sea warming contrast in summer European climate change and how they interact are analyzed. Models for which evapotranspiration is strongly limited by soil moisture in the present climate are found to tend to simulate larger future decrease in evapotranspiration. Models characterized by stronger present-day anti-correlation between cloud cover and temperature over land tend to simulate larger future decrease in cloud cover. Large model-to-model differences regarding land-sea warming contrast and its impacts are also found. Warming over land is expected to be larger than warming over sea, leading to a decrease in continental relative humidity and precipitation because of the discrepancy between the change in atmospheric moisture capacity over land and the change in specific humidity. Yet, it is not true for all the models over our domain of interest. Models in which evapotranspiration is not limited by soil moisture and with a weak present-day anti-correlation between cloud cover and temperature tend to simulate smaller land surface warming. In these models, change in specific humidity over land is therefore able to match the continental increase in moisture capacity, which leads to virtually no change in continental relative humidity and smaller precipitation change. Because of the physical links that exist between the response to anthropogenic forcing of important impact-related climate variables and the way some mechanisms are simulated in the context of present-day variability, this study suggests some potentially useful metrics to reduce summer European climate change uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes. [ABSTRACT FROM AUTHOR]

This paper analyzes the soil–atmosphere feedbacks and uncertainties under current (1960–1990) and plausible future climate conditions (2070–2100, using the A2 greenhouse gases emission scenario). For this purpose, two vegetation descriptions differing only in the grassland and grass-with-trees proportion in some parts of the domain have been created. The combination of these two different climate scenarios and two vegetation descriptions defines four different 30-year experiments, which have been completed using a regional climate model. The domain is centered around the Mediterranean basin and covers most of Europe. The study focuses on the summer season when there are major differences between the two vegetation descriptions and when the impact of land–surface processes on precipitation is largest. Present climate experiments show large evapotranspiration differences over areas where vegetation changes have taken place. Precipitation increases (up to 3 mm day−1 in some regions) follow evapotranspiration increases, although with a more complex spatial structure. These results indicate a high sensitivity at regional scales of summer precipitation processes to vegetation changes. Future climate simulations show very similar changes to those observed in the current climate experiments. This indicates that the impacts of climate change are relatively independent to the land-cover descriptions used in this study. [ABSTRACT FROM AUTHOR]

There is considerable interest in the potential impact of climate change on the feasibility and predictability of renewable energy sources including wind energy. This paper presents dynamically downscaled near-surface wind fields and examines the impact of climate change on near-surface flow and hence wind energy density across northern Europe. It is shown that: Simulated wind fields from the Rossby Centre coupled Regional Climate Model (RCM) (RCAO) with boundary conditions derived from ECHAM4/OPYC3 AOGCM and the HadAM3H atmosphere-only GCM exhibit reasonable and realistic features as documented in reanalysis data products during the control period (1961–1990). The near-surface wind speeds calculated for a climate change projection period of 2071–2100 are higher than during the control run for two IPCC emission scenarios (A2, B2) for simulations conducted using boundary conditions from ECHAM4/OPYC3. The RCAO simulations conducted using boundary conditions from ECHAM4/OPYC3 indicate evidence for a small increase in the annual wind energy resource over northern Europe between the control run and climate change projection period and for more substantial increases in energy density during the winter season. However, the differences between the RCAO simulations for the climate projection period and the control run are of similar magnitude to differences between the RCAO fields in the control period and the NCEP/NCAR reanalysis data. Additionally, the simulations show a high degree of sensitivity to the boundary conditions, and simulations conducted using boundary conditions from HadAM3H exhibit evidence of slight declines or no change in wind speed and energy density between 1961–1990 and 2071–2100. Hence, the uncertainty of the projected wind changes is relatively high. [ABSTRACT FROM AUTHOR]