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Quantitative estimates of the economic damages of climate change usually are based on aggregate relationships linking average temperature change to loss in gross domestic product (GDP). However, there is a clear need for further detail in the regional and sectoral dimensions of impact assessments to design and prioritize adaptation strategies. New developments in regional climate modeling and physical-impact modeling in Europe allow a better exploration of those dimensions. This article quantifies the potential consequences of climate change in Europe in four market impact categories (agriculture, river floods, coastal areas, and tourism) and one nonmarket impact (human health). The methodology integrates a set of coherent, high-resolution climate change projections and physical models into an economic modeling framework. We find that if the climate of the 2080s were to occur today, the annual loss in household welfare in the European Union (EU) resulting from the four market impacts would range between 0.2-1%. If the welfare loss is assumed to be constant over time, climate change may halve the EU's annual welfare growth. Scenarios with warmer temperatures and a higher rise in sea level result in more severe economic damage. However, the results show that there are large variations across European regions. Southern Europe, the British Isles, and Central Europe North appear most sensitive to climate change. Northern Europe, on the other hand, is the only region with net economic benefits, driven mainly by the positive effects on agriculture. Coastal systems, agriculture, and river flooding are the most important of the four market impacts assessed.

A study of seasonal mean temperature, precipitation, and wind speed has been performed for a set of 19 global climate model (GCM) driven high-resolution regional climate model (RCM) simulations forming a complete 5 × 4 GCM × RCM matrix with only one missing simulation. Differences between single simulations and between groups of simulations forced by a specific GCM or a specific RCM are identified. With the help of an analysis of variance (ANOVA) we split the ensemble variance into linear GCM and RCM contributions and cross terms for both mean climate and climate change for the end of the current century according to the RCP8.5 emission scenario. The results document that the choice of GCM generally has a larger influence on the climate change signal than the choice of RCM, having a significant influence for roughly twice as many points in the area for the fields investigated (temperature, precipitation and wind speed). It is also clear that the RCM influence is generally concentrated close to the eastern and northern boundaries and in mountainous areas, i.e., in areas where the added surface detail of e.g. orography, snow and ice seen by the RCM is expected to have considerable influence on the climate, and in areas where the air in general has spent the most time within the regional domain. The analysis results in estimates of areas where the specific identity of either GCM or RCM is formally significant, hence obtaining an indication about regions, seasons, and fields where linear superpositions of GCM and RCM effects are good approximations to an actual simulation for both the mean fields analysed and their changes. In cases where linear superposition works well, the frequently encountered sparse GCM–RCM matrices may be filled with emulated results, leading to the possibility of giving more fair relative weight between model simulations than simple averaging of existing simulations. An important result of the present study is that properties of the specific GCM–RCM combination are generally important for the mean climate, but negligible for climate change for the seasonal-mean surface fields investigated here.

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ∼15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (∼47% under RCP4.5, ∼49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.

Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

Modelling studies have postulated a possible impact of soil-moisture deficit and drought on hot extremes. An analysis of observational indices from central and southeastern Europe confirms that summer hot extremes are linked to soil-moisture deficits in southeastern Europe but does not detect a similar effect in central Europe. Climate change is expected to affect not only the means of climatic variables, but also their variabilities 1 , 2 and extremes such as heat waves 2 , 3 , 4 , 5 , 6 . In particular, modelling studies have postulated a possible impact of soil-moisture deficit and drought on hot extremes 7 , 8 , 9 , 10 , 11 . Such effects could be responsible for impending changes in the occurrence of heat waves in Europe 7 . Here we analyse observational indices based on measurements at 275 meteorological stations in central and southeastern Europe, and on publicly available gridded observations 12 . We find a relationship between soil-moisture deficit, as expressed by the standardized precipitation index 13 , and summer hot extremes in southeastern Europe. This relationship is stronger for the high end of the distribution of temperature extremes. We compare our results with simulations of current climate models and find that the models correctly represent the soil-moisture impacts on temperature extremes in southeastern Europe, but overestimate them in central Europe. Given the memory associated with soil moisture storage, our findings may help with climate-change-adaptation measures, such as early-warning and prediction tools for extreme heat waves.

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using “convection-permitting” models to produce reliable climate change information over the Fenno-Scandinavian region.

Convection-permitting climate models have shown superior performance in simulating important aspects of the precipitation climate including extremes and also to give partly different climate change signals compared to coarser-scale models. Here, we present the first long-term (1998–2018) simulation with a regional convection-permitting climate model for Fenno-Scandinavia. We use the HARMONIE-Climate (HCLIM) model on two nested grids; one covering Europe at 12 km resolution (HCLIM12) using parameterized convection, and one covering Fenno-Scandinavia with 3 km resolution (HCLIM3) with explicit deep convection. HCLIM12 uses lateral boundaries from ERA-Interim reanalysis. Model results are evaluated against reanalysis and various observational data sets, some at high resolutions. HCLIM3 strongly improves the representation of precipitation compared to HCLIM12, most evident through reduced “drizzle” and increased occurrence of higher intensity events as well as improved timing and amplitude of the diurnal cycle. This is the case even though the model exhibits a cold bias in near-surface temperature, particularly for daily maximum temperatures in summer. Simulated winter precipitation is biased high, primarily over complex terrain. Considerable undercatchment in observations may partly explain the wet bias. Examining instead the relative occurrence of snowfall versus rain, which is sensitive to variance in topographic heights it is shown that HCLIM3 provides added value compared to HCLIM12 also for winter precipitation. These results, indicating clear benefits of convection-permitting models, are encouraging motivating further exploration of added value in this region, and provide a valuable basis for impact studies.

Nearly all regions in the world are projected to become dryer in a warming climate. Here, we investigate the Mediterranean region, often referred to as a climate change “hot spot”. From regional climate simulations, it is shown that although enhanced warming and drying over land is projected, the spatial pattern displays high variability. Indeed, drying is largely caused by enhanced warming over land. However, in Northern Europe, soil moisture alleviates warming induced drying by up to 50% due to humidity uptake from land. In already arid regions, the Mediterranean Sea is generally the only humidity source, and drying is only due to land warming. However, over Sahara and the Iberian Peninsula, enhanced warming over land is insufficient to explain the extreme drying. These regions are also isolated from humidity advection by heat lows, which are cyclonic circulation anomalies associated with surface heating over land. The cyclonic circulation scales with the temperature gradient between land and ocean which increases with climate change, reinforcing the cyclonic circulation over Sahara and the Iberian Peninsula, both diverting the zonal advection of humidity to the south of the Iberian Peninsula. The dynamics are therefore key in the warming and drying of the Mediterranean region, with extreme aridification over the Sahara and Iberian Peninsula. In these regions, the risk for human health due to the thermal load which accounts for air temperature and humidity is therefore projected to increase significantly with climate change at a level of extreme danger.

Regional climate model simulations have routinely been applied to assess changes in precipitation extremes at daily time steps. However, shorter sub-daily extremes have not received as much attention. This is likely because of the limited availability of high temporal resolution data, both for observations and for model outputs. Here, summertime depth duration frequencies of a subset of the EURO-CORDEX 0.11∘ ensemble are evaluated with observations for several European countries for durations of 1 to 12 h. Most of the model simulations strongly underestimate 10-year depths for durations up to a few hours but perform better at longer durations. The spatial patterns over Germany are reproduced at least partly at a 12 h duration, but all models fail at shorter durations. Projected changes are assessed by relating relative depth changes to mean temperature changes. A strong relationship with temperature is found across different subregions of Europe, emission scenarios and future time periods. However, the scaling varies considerably between different combinations of global and regional climate models, with a spread in scaling of around 1–10 % K−1 at a 12 h duration and generally higher values at shorter durations.

Scientists utilized a high-resolution climate model as part of a study seeking to quantify the impacts of greenhouse gas forced global warming on heavy or extended precipitation episodes that may cause catastrophic flooding events. Findings from the effort indicated increases in the amount of precipitation that exceeds the 95th percentile are very likely in large sections of Europe. These trends are expected in spite of the fact that average summertime precipitation may be reduced over many areas of Europe. The two emission scenarios considered using the HIRHAM4 regional climate model, which was developed by the Danish Meteorological Institute, are detailed. Model results suggested increased frequency of severe flooding events occurring as counterpoint to general trends toward drier summer conditions.