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The increasing intensity and frequency of high-temperature events in response to climate change can potentially impact the aviation industry, since aircraft takeoff and landing performances depend on near-surface air temperature. Previous studies have combined climate data with aircraft technical data to estimate the future impact of rising high temperatures on aircraft takeoff. They found a decrease of maximum takeoff weights and the lengthening of takeoff distances. The Mediterranean region is a climate change "hot spot" area, specially concerned by extreme high-temperatures increase. In this study, the magnitude and trends of the daily maximum near-surface temperature extremes in summer were analysed over major airports in Southwestern Europe. Trends in the period 1961–2014 were analysed from observations and reanalysis. Future changes by 2021–2050 and 2071–2100, with respect to 1961–2005, were analysed from simulations performed with Regional and Global Climate Models (RCMs and GCMs). Before assessing future climate projections, climate models were evaluated in present climate, and the RCM and GCM ensembles were compared to each other. No clear added value was found for RCMs over GCMs in present climate at the airport scale in these terms. GCMs project larger temperature changes than RCMs over the same locations. Multi-model ensemble mean projected changes under the RCP8.5 scenario range between + 1.7 and + 3.2 ∘ C by the near term, and between + 4.9 and + 8.5 ∘ C by the long term, across the airports and the RCM and GCM ensembles. This increase of high-temperature extremes would impact airport operations. Adaptation or mitigation policies would become necessary. [ABSTRACT FROM AUTHOR]

Since our climate is in a rapid changing phase, it is crucial to get information about the regional patterns of basic climatic parameters and indices. The EURO-CORDEX project provides high-quality regional climate model outputs, but these raw datasets are not convenient for the application in wider geoscience studies. According to the authors' knowledge, there is a lack of published spatial information about basic climate parameters and indices in Central Europe and especially in the broader Carpathian region therefore the basic aim of this study to fill this gap. The study presents the future trends in daily air temperature and precipitation and various climatic indices in the broader Carpathian Basin region during the 21st century. The indices are calculated using multi-model average temperature and precipitation data from EURO-CORDEX model simulations for the future time periods (2021-2050, 2071-2100) and emission scenarios (RCP4.5, RCP8.5). The indices present the future trends of the heat load, energy demand as well as extreme precipitation and drought characteristics. Based on the results the temperature increase is obvious and the heat load and energy demand quantifying indices follow the temperature trend. However, the trend is difficult to evaluate in the case of precipitation. The changes in the precipitation and the related indices can be considered small and appear within the regions. The future changes are the most considerable in the Carpathian Basin, but the entire examined region faces crucial changes in the following decades. [ABSTRACT FROM AUTHOR]

[Display omitted] • The model performs good in capturing the seasonal pattern and peak values of river water temperature. • Air temperature and day of year are the major factorson modeling river water temperature. • The model is a promising tool to investigate the impact of climate change on river thermal dynamics. Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics. [ABSTRACT FROM AUTHOR]

Future anthropogenic climate change is likely to increase the air temperature ( T) across Europe and increase the frequency, duration and magnitude of severe heat stress events. Heat stress events are generally associated with clear-sky conditions and high T, which give rise to high radiant heat load, i.e. mean radiant temperature ( T). In urban environments, T is strongly influenced by urban geometry. The present study examines the effect of urban geometry on daytime heat stress in three European cities (Gothenburg in Sweden, Frankfurt in Germany and Porto in Portugal) under present and future climates, using T as an indicator of heat stress. It is found that severe heat stress occurs in all three cities. Similar maximum daytime T is found in open areas in all three cities despite of the latitudinal differences in average daytime T. In contrast, dense urban structures like narrow street canyons are able to mitigate heat stress in the summer, without causing substantial changes in T in the winter. Although the T averages are similar for the north-south and east-west street canyons in each city, the number of hours when T exceeds the threshold values of 55.5 and 59.4 °C-used as indicators of moderate and severe heat stress-in the north-south canyons is much higher than that in the east-west canyons. Using statistically downscaled data from a regional climate model, it is found that the study sites were generally warmer in the future scenario, especially Porto, which would further exacerbate heat stress in urban areas. However, a decrease in solar radiation in Gothenburg and Frankfurt reduces T in the spring, while the reduction in T is somewhat offset by increasing T in other seasons. It suggests that changes in the T under the future scenario are dominated by variations in T. Nonetheless, the intra-urban differences remain relatively stable in the future. These findings suggest that dense urban structure can reduce daytime heat stress since it reduces the number of hours of high T in the summer and does not cause substantial changes in average and minimum T in the winter. In dense urban settings, a more diverse urban thermal environment is also preferred to compensate for reduced solar access in the winter. The extent to which the urban geometry can be optimized for the future climate is also influenced by local urban characteristics. [ABSTRACT FROM AUTHOR]

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling. [ABSTRACT FROM AUTHOR]

The occurrence and intensity of heatwaves is expected to increase with climate change. Early warnings of hot summers have therefore a great socio-economical value. Previous studies have shown that hot summers are preceded by a Southern European rainfall deficit during winter, and higher spring temperatures. Changes in the surface energy budget are believed to drive this evolution, in particular changes in the latent and sensible heat fluxes. However these have rarely been investigated due to the lack of long-term reliable observation data. In this study, we analyzed several data-derived gridded products of latent and sensible heat fluxes, based on flux tower observations, together with re-analyses and regional climate model simulations over Europe. We find that warm summers are preceded by an increase in latent heat flux in early spring. During warm summers, an increase in available energy results in an excess of both latent and sensible heat fluxes over most of Europe, but a latent heat flux decrease over the Iberian Peninsula. This indicates that, on average, a summertime soil-moisture limited evapotranspiration regime only prevails in the Iberian Peninsula. In general, the models that we analyzed overestimate latent heat and underestimate sensible heat as compared to the flux tower derived data-product. Most models show considerable drying during warm seasons, leading to the establishment of a soil-moisture limited regime across Europe in summer. This over-estimation by the current generation of models of latent heat and hence of soil moisture deficit over Europe in summer has potential consequences for future summertime climate projections and the projected frequency of heat waves. We also show that a northward propagation of drought during warm summers is found in model results, a phenomenon which is also seen in the flux tower data-product. Our results lead to a better understanding of the role of latent and sensible heat flux in summer heatwaves, and provide a framework for benchmark of modeling studies. [ABSTRACT FROM AUTHOR]

The study evaluates relationships between the North Atlantic Oscillation (NAO) index and winter temperatures (including indices of extremes) over Europe in an ensemble of transient simulations of current global climate models (GCMs). We focus on identification of areas in which the NAO index is linked to winter temperatures and temperature extremes in simulations of the recent climate (1961-2000), and evaluate how these relationships change in climate change scenarios for the late 21st century (2071-2100). Most GCMs are able to reproduce main features of the observed links. The NAO index is more important for cold than warm extremes, which is also reproduced by the GCMs. However, all GCMs underestimate the magnitude of the NAO influence on cold extremes when averaged over northern and western Europe. For future scenarios, the links between the NAO and temperatures are mostly analogous to those in the recent climate, except for one GCM (CM3) in which the influence of the NAO on temperature almost disappears over whole Europe. This suggests that future scenarios from this particular GCM should be evaluated with caution. The NAO index is found to represent a useful covariate that explains an important fraction of variability of cold extremes in winter, and its incorporation into extreme value models for daily temperatures (and their possible changes under climate change) may improve performance of these models and reliability of estimates of extremes and their uncertainty. [ABSTRACT FROM AUTHOR]