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The winter of 2009/2010 was characterized by record persistence of the negative phase of the North‐Atlantic Oscillation (NAO) which caused several severe cold spells over Northern and Western Europe. This somehow unusual winter with respect to the most recent ones arose concurrently with public debate on climate change, during and after the Copenhagen climate negotiations. We show however that the cold European temperature anomaly of winter 2010 was (i) not extreme relative to winters of the past six decades, and (ii) warmer than expected from its record‐breaking seasonal circulation indices such as NAO or blocking frequency. Daily flow‐analogues of winter 2010, taken in past winters, were associated with much colder temperatures. The winter 2010 thus provides a consistent picture of a regional cold event mitigated by long‐term climate warming.

The increased exposure of human populations to heat stress is one of the likely consequences of global warming, and it has detrimental effects on health and labor capacity. Here, we consider the evolution of heat stress under climate change using 21 general circulation models (GCMs). Three heat stress indicators, based on both temperature and humidity conditions, are used to investigate present-day model biases and spreads in future climate projections. Present day estimates of heat stress indicators from observational data shows that humid tropical areas tend to experience more frequent heat stress than other regions do, with a total frequency of heat stress 250-300 d yr−1. The most severe heat stress is found in the Sahel and south India. Present-day GCM simulations tend to underestimate heat stress over the tropics due to dry and cold model biases. The model based estimates are in better agreement with observation in mid to high latitudes, but this is due to compensating errors in humidity and temperature. The severity of heat stress is projected to increase by the end of the century under climate change scenario RCP8.5, reaching unprecedented levels in some regions compared with observations. An analysis of the different factors contributing to the total spread of projected heat stress shows that spread is primarily driven by the choice of GCMs rather than the choice of indicators, even when the simulated indicators are bias-corrected. This supports the utility of the multi-model ensemble approach to assess the impacts of climate change on heat stress.

The electricity sector is currently considered mainly on the emission side of the climate change equation. In order to limit climate warming to below 2 °C, or even 1.5 °C, it must undergo a rapid transition towards carbon neutral production by the mid-century. Simultaneously, electricity generating technologies will be vulnerable to climate change. Here, we assess the impacts of climate change on wind, solar photovoltaic, hydro and thermoelectric power generation in Europe using a consistent modelling approach across the different technologies. We compare the impacts for different global warming scenarios: +1.5 °C, +2 °C and +3 °C. Results show that climate change has negative impacts on electricity production in most countries and for most technologies. Such impacts remain limited for a 1.5 °C warming, and roughly double for a 3 °C warming. Impacts are relatively limited for solar photovoltaic and wind power potential which may reduce up to 10%, while hydropower and thermoelectric generation may decrease by up to 20%. Generally, impacts are more severe in southern Europe than in northern Europe, inducing inequity between EU countries. We show that a higher share of renewables could reduce the vulnerability of power generation to climate change, although the variability of wind and solar PV production remains a significant challenge.

In the context of rapid human-caused climate change, regular updates of the state of knowledge of current and future climate are needed. New statistical methods using observational constraints underpinned estimates of present-day human-induced warming and projected future warming in the most recent IPCC report. As time goes by, and new updated observational records become available, how should estimates of the current and projected human-caused climate change be updated? Here, we use a perfect model framework and show that incorporating observations from every new year in observationally constrained projections improves their accuracy, without causing major year-to-year spurious variability on outcomes. The forced warming estimated for the current year also exhibits high enough stability to be considered as a robust indicator of the state of the climate system. This study shows that incorporating observations from every new year in constrained projections of forced warming improves estimates of the expected warming in response to different emission scenarios.

A novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high‐resolution, physically consistent, three‐dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large‐scale global climate model (GCM) 3‐dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies. Key Points Novel hybrid dynamical and statistical regional climate downscaling technique Decreases substantially temperature and precipitation biases Produces physically consistent unbiased 3D fields for climate impact models

We present a new method that allows a separation of the attribution of human influence in extreme events into changes in atmospheric flows and changes in other processes. Assuming two data sets of model simulations or observations representing a natural, or 'counter-factual' climate, and the actual, or 'factual' climate, we show how flow analogs used across data sets can provide quantitative estimates of each contribution to the changes in probabilities of extreme events. We apply this method to the extreme January precipitation amounts in Southern UK such as were observed in the winter of 2013/2014. Using large ensembles of an atmospheric model forced by factual and counterfactual sea surface temperatures, we demonstrate that about a third of the increase in January precipitation amounts can be attributed to changes in weather circulation patterns and two thirds of the increase to thermodynamic changes. This method can be generalized to many classes of events and regions and provides, in the above case study, similar results to those obtained in Schaller et al (2016 Nat. Clim. Change 6 627-34) who used a simple circulation index, describing only a local feature of the circulation, as in other methods using circulation indices (van Ulden and van Oldenborgh 2006 Atmos. Chem. Phys. 6 863-81).

A recently launched project under the auspices of the World Climate Research Program’s (WCRP) Coordinated Regional Downscaling Experiments Flagship Pilot Studies program (CORDEX-FPS) is presented. This initiative aims to build first-of-its-kind ensemble climate experiments of convection permitting models to investigate present and future convective processes and related extremes over Europe and the Mediterranean. In this manuscript the rationale, scientific aims and approaches are presented along with some preliminary results from the testing phase of the project. Three test cases were selected in order to obtain a first look at the ensemble performance. The test cases covered a summertime extreme precipitation event over Austria, a fall Foehn event over the Swiss Alps and an intensively documented fall event along the Mediterranean coast. The test cases were run in both “weather-like” (WL, initialized just before the event in question) and “climate” (CM, initialized 1 month before the event) modes. Ensembles of 18–21 members, representing six different modeling systems with different physics and modelling chain options, was generated for the test cases (27 modeling teams have committed to perform the longer climate simulations). Results indicate that, when run in WL mode, the ensemble captures all three events quite well with ensemble correlation skill scores of 0.67, 0.82 and 0.91. They suggest that the more the event is driven by large-scale conditions, the closer the agreement between the ensemble members. Even in climate mode the large-scale driven events over the Swiss Alps and the Mediterranean coasts are still captured (ensemble correlation skill scores of 0.90 and 0.62, respectively), but the inter-model spread increases as expected. In the case over Mediterranean the effects of local-scale interactions between flow and orography and land–ocean contrasts are readily apparent. However, there is a much larger, though not surprising, increase in the spread for the Austrian event, which was weakly forced by the large-scale flow. Though the ensemble correlation skill score is still quite high (0.80). The preliminary results illustrate both the promise and the challenges that convection permitting modeling faces and make a strong argument for an ensemble-based approach to investigating high impact convective processes.

In the framework of the EURO-CORDEX initiative an ensemble of European-wide high-resolution regional climate simulations on a 0 . 11 ∘ ( ∼ 12.5 km ) grid has been generated. This study investigates whether the fine-gridded regional climate models are found to add value to the simulated mean and extreme daily and sub-daily precipitation compared to their coarser-gridded 0 . 44 ∘ ( ∼ 50 km ) counterparts. Therefore, pairs of fine- and coarse-gridded simulations of eight reanalysis-driven models are compared to fine-gridded observations in the Alps, Germany, Sweden, Norway, France, the Carpathians, and Spain. A clear result is that the 0 . 11 ∘ simulations are found to better reproduce mean and extreme precipitation for almost all regions and seasons, even on the scale of the coarser-gridded simulations (50 km). This is primarily caused by the improved representation of orography in the 0 . 11 ∘ simulations and therefore largest improvements can be found in regions with substantial orographic features. Improvements in reproducing precipitation in the summer season appear also due to the fact that in the fine-gridded simulations the larger scales of convection are captured by the resolved-scale dynamics . The 0 . 11 ∘ simulations reduce biases in large areas of the investigated regions, have an improved representation of spatial precipitation patterns, and precipitation distributions are improved for daily and in particular for 3 hourly precipitation sums in Switzerland. When the evaluation is conducted on the fine (12.5 km) grid, the added value of the 0 . 11 ∘ models becomes even more obvious.

In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell–Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max −2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40–60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (−2.8 °C); this location suggests that land–atmosphere rather than cloud–radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain–Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15–30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.

The variability of the relative sensitivity of photochemical ozone formation to volatile organic compounds (VOC) and NOx emissions, the chemical regime, over Europe during summers 2001 to 2003 is simulated with a regional scale transport-chemistry model. The robustness and variability of chemical regimes is shown. A VOC sensitive regime over North-Western Europe and a mainly NOx sensitive regime over the Mediterranean basin and Eastern Europe are found, confirming earlier published results. The chemical regime time variability, its robustness with respect to several environmental factors (seasonality, interannual variability) and with respect to model uncertainty are thoroughly analysed. For the regions with well pronounced chemical regimes over North-Western Europe and the Mediterranean, the chemical regime occurrence only slightly depends on the ozone target considered - daily ozone or Ox (= O3 + NO2 ) maximum or mean, AOT's, SOMO35, ... For these regions, differences between particular years and summer months are weak, day to day variability is significant but does not change the occurrence of one or another chemical regime. On the contrary, over North-Eastern Germany, the chemical regime changes form one day to another and is also dependent on the ozone target chosen. Expected decreases in anthropogenic NOx emissions over Europe since the last and for the next few decades have shifted and will shift chemical regimes to more NOx sensitive. The predictive skill of chemical regime indicator species is made evident at continental scale, extending their spatial range of applicability with respect to earlier studies. Several sensitivity tests were performed in order to account for major sources of model uncertainty. With the exception of regions near ship tracks over the Mediterranean basin, the spatial pattern of chemical regimes appears to be robust with respect to model uncertainty for all cases tested.