EU High Resolution Temperature and Precipitation

During the last decades, the effects of global warming have become apparent also in Europe, causing relevant impacts in many sectors. Under projected future global warming, such a tendency can be expected to persist until the end of this century and beyond. Identifying which climate-related impacts are likely to increase, and by how much, is an important element of any effective strategy for managing future climate risks. This study investigates whether energy demand for cooling and heating buildings can be expected to increase or decrease under climate change. Two indicators of weather-related energy consumption for heating and cooling buildings are considered: heating degree-days (HDD) and cooling degree-days (CDD). The evolution of these indicators has been analysed based on 11 high-resolution bias-adjusted EURO-CORDEX simulations for two emission representative concentration pathways (RCP4.5 and RCP8.5). Both indicators have been validated over the period 1981–2010 using an independent data set that contains more than 4000 station data, showing very high correlation over most of Europe. Trends ofHDDand CDDfrom 1981 to 2100, together with their uncertainties, are analysed. For both RCPs, all simulations project a significant decrease for HDD, especially over Scandinavia and European Russia, and an increase of CDD which peaks over the Mediterranean region and the Balkans. Overall, degree-day trends do not show remarkable differences if population weighting is applied. If a constant population scenario is considered, the decrease in HDD will outbalance the increase in CDD in the 21st century over most of Europe. Thus the related energy demand (expressed as Energy Degree-days, EDD) is expected to decrease. If, however, population projections over the 21st century are included in the calculations, it is shown that despite the persisting warming, EDD will increase over northern Europe, the Baltic countries, Great Britain, Ireland, Benelux, the Alps, Spain, and Cyprus, resulting in an overall increase in EDD over Europe.

Based on high-resolution models, we investigate the change in climate extremes and impact-relevant indicators over Europe under different levels of global warming. We specifically assess the robustness of the changes and the benefits of limiting warming to 1.5°C instead of 2°C. Compared to 1.5°C world, a further 0.5°C warming results in a robust change of minimum summer temperature indices (mean, Tn10p, and Tn900p) over more than 70% of Europe. Robust changes (more than 0.5°C) in maximum temperature affect smaller areas (usually less than 20%). There is a substantial nonlinear change of fixed-threshold indices, with more than 60% increase of the number of tropical nights over southern Europe and more than 50% decrease in the number of frost days over central Europe. The change in mean precipitation due to 0.5°C warming is mostly nonsignificant at the grid point level, but, locally, it is accompanied by a more marked change in extreme rainfall.

Statistical bias-adjustment of climate 5 models’ outputs is being increasingly used for assessing the impact of climate change on several sectors. It is generally known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect of bias-adjustment on the projected occurrence of climate extremes is less commonly investigated and it is the focus of this study. Here, the outputs of an ensemble of regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment (CORDEX) has been bias-adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) has been calculated for the present (2981-2010) and future (2071-2100) climate. Indices include absolute thresholds indices, percentile-based thresholds indices, and indices based on the duration of an event. Results show that absolute-threshold indices are the ones most affected by the bias-adjustment, as they depend strongly on both the present-climate value, usually largely biased in the original RCMs, and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration on event (e.g., consec23 utive dry days, or heat waves) although the present climate value can differ sensibly between original and bias-adjusted results. However, indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias-adjustment, although, when analysing an ensemble of RCMs, the differences are usually smaller than the inter-model variability. D

A statistical bias correction technique is applied to a set of high resolution climate change simulations for Europe from 11 state-of-theart regional climate models (RCMs) from the project ENSEMBLES. Modelled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period 1961-1990, which are then applied to the decade 1991-2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both inter-model variability, and longer (e.g. decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias corrected results are ensemble-averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on e.g. hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.