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.

continents. The focus for this particular evaluation was meteorological parameters relevant to air 51 quality processes such as transport and mixing, chemistry, and surface fluxes. The unprecedented 52 scale of the exercise (one year, two continents) allowed us to examine the general characteristics of 53 meteorological models' skill and uncertainty. In particular, we found that there was a large 54 variability between models or even model versions in predicting key parameters such as surface 55 shortwave radiation. We also found several systematic model biases such as wind speed 56 overestimations, particularly during stable conditions. We conclude that major challenges still remain 57 in the simulation of meteorology, such as nighttime meteorology and cloud/radiation processes, for 58 air quality simulation. 59 60 61 -3 -

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and climate changes since 1948, e.g., in frequencies of extremes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges, etc.) over many decades. The acronym coastDat stands for the set of consistent ocean and atmospheric data, where the atmospheric data where used as forcing for the reconstruction of the sea state. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013; doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for the entire European continent, including the Baltic Sea and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with the regional climate model COSMO-CLM (CCLM) and a horizontal grid size of 0.22 degree in rotated coordinates. Global reanalysis data of NCEP1 were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly. MotivationThe precursor of coastDat2, coastDat1 (Weisse et al., 2009), was widely used. About 50 % of the coastDat1 users were commercial, while 25 % were academic and another 25 % were from the authorities. Applications range from assessing long-term variability and change to risk assessment and design, for example of offshore wind farms. As coastDat1 terminated in 2007, and as there were strong requests for an upgrade comprising the most recent years at higher spatial resolution, the coastDat2 effort was implemented. The atmospheric part of coastDat2 described in this paper was produced with the community model COSMO-CLM on the current super computer of the German Climate Computing Center (DKRZ). It is the successor of the coastDat1 regional atmospheric simulation done with REMO5.0 (Feser et al., 2001;Jacob et al., 2001). For coastal areas the higher resolution is the main advantage. The overall advantage is the availability of the last 5 years. Model setupFor the reconstruction the COSMO model in CLimate Mode (COSMO-CLM) version 4.8_clm_11 Baldauf et al., 2011;Steppeler et al., 2003) was used. The COSMO model is the non-hydrostatic operational weather prediction model applied and further developed by the national weather services affiliated in the COnsortium for SMall-scale MOdeling (COSMO). The climate mode is applied and developed by the Climate Limitedarea Modelling Community (http://www.clm-community. eu). The use of the model is well supported by the members of the community and documented mainly via the COSMO documentation (http://www.cosmo-model.org/ content/model/documentation/core/default.htm).The simulation was done on a regular grid in rotated coordinates with a rotated pole at 170.0• W and 35.0• N with a resolution of 0.22• , a time step of 150 s and hourly output. Figure 1 presents the model domain; 40 vertical levels up to 27.2 km height and 10 soil levels down...

ABSTRACT:To determine whether the use of regional climate models improves the representation of climate is a crucial topic in climate modelling. An improvement over coarser scale models is expected especially in areas with complex orography or along coastlines. However, some studies have shown no clear added value for regional climate models. In this study a high-resolution regional climate simulation performed with REMO 5.0 (regional model) over the whole of Europe over the period 1958-1998 is analysed for 2-m temperature over the European Alps and their surroundings called the Greater Alpine region (GAR). The simulation is driven by perfect boundary conditions at the lateral boundaries provided by the ERA40 reanalysis and spectral nudging of the large-scale wind fields towards ERA40 values for the upper layers inside the model domain. The added value of the regional simulation (1/6°resolution) is analysed with respect to the driving reanalysis (1.125°resolution).Both the REMO simulation and the ERA40 reanalysis are validated against different station datasets of monthly and daily mean 2-m temperature. Correlation analysis shows that the temporal variability of temperature is well represented by both REMO and ERA40, whereas both show considerable biases. The REMO bias reaches 3 K in summer in regions known to experience a problem with summer drying in a number of regional models. The comparison of REMO and ERA40 shows that an added value of the former exists for all regions in winter. For the regions surrounding the Alps, the added value is absent in summer, whereas in the inner Alpine subregions with most complex orography, REMO performs better than ERA40 during the whole year. The only moderate value added by REMO in this hindcast set-up may be partly explicable by the fact that meteorological measurements are assimilated in the ERA40 reanalysis but not in the REMO simulation.

ABSTRACT:Hindcasts with reanalysis-driven regional climate models (RCMs) are a common tool to assess weather statistics (i.e. climate) and recent changes and trends. A remote sensing-based method to investigate the added value of surface marine RCM wind speed is introduced. The capability of the dynamical downscaling approach (with spectral nudging applied) to add value to the reanalysis wind speed forcing is assessed by the comparison with QuikSCAT Level 2B 12.5 km (L2B12) swath data in European waters for 2000-2007. Co-location criteria are within 0.1°and 0.06°in longitudinal and latitudinal distance from RCM grid points and within 10 min. In the wind speed range, QuikSCAT L2B12 is reliably reproducing (3-20 m s −1 ), dynamically downscaled wind speed does not show an added value in 'open ocean' areas. However, in coastal areas with complex topography, the regional models show an added value, especially around Iceland and the Iberian Peninsula and in the Mediterranean, Baltic and Irish Seas, validating the findings of previous in situ data-based studies on the added value. Strong interseasonal differences exist, in winter enhanced cyclonic and meso-cyclonic activity increases the potential of dynamical downscaling. In winter time, the added value is more pronounced around Iceland and Greenland, south of Iceland and within the Gulf of Lyon/Mistral region. Summarizing the presented method can be easily applied for other ocean areas, making QuikSCAT a valuable tool to identify marine regions where dynamical downscaling adds value to surface marine wind speed. A detailed comparison of 10 m winds from the National Centres of Environmental Prediction (NCEP)/National Centre for Atmospheric Research (NCAR) and the newer NCEP/DOE-II reanalyses is presented in the annex, motivating the use of the NCEP/NCAR reanalysis in the added value assessment.

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The European Union has set ambitious CO2 reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms−1 depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.

This study is part of the Global Mercury Observation System (GMOS), a European FP7 project dedicated to the improvement and validation of mercury models to assist in establishing a global monitoring network and to support political decisions. One key question about the global mercury cycle is the efficiency of its removal out of the atmosphere into other environmental compartments. So far, the evaluation of modelled wet deposition of mercury was difficult because of a lack of long term measurements of oxidized and elemental mercury. The oxidized mercury species GOM (Gaseous Oxidized Mercury) and PBM (Particle Bound Mercury) which are found in the atmosphere in typical concentrations of a few to a few tens pg/m³ are the relevant components for the wet deposition of mercury. In this study, the first European long-term dataset of speciated mercury taken at Waldhof/Germany was used to evaluate deposition fields modelled with the chemistry transport model (CTM) CMAQ and to analyse the influence of the governing parameters. The influence of the parameters precipitation and atmospheric concentration was evaluated using different input datasets for a variety of CMAQ simulations for the year 2009. It was found, that on the basis of daily and weekly measurement data the bias of modelled depositions could be explained by the bias of precipitation fields and atmospheric concentrations of GOM and PBM. A correction of the modelled wet deposition using observed daily precipitation increased the correlation, on average, from 0.17 to 0.78. An additional correction based on the daily average GOM and PBM concentration lead to a 50% decrease of the model error for all CMAQ scenarios. Monthly deposition measurements were found to have a too low temporal resolution to adequately analyse model deficiencies in wet deposition processes due to the non-linear nature of the scavenging process. Moreover, the general overestimation of atmospheric GOM by the CTM in combination with an underestimation of low precipitation events in the meteorological models lead to a good agreement of total annual wet deposition besides the large error in weekly deposition estimates. Moreover, it was found that the current speciation profiles for GOM emissions are the main factor for the overestimation of atmospheric GOM concentrations and might need to be revised in the future. The assumption of zero emissions of GOM lead to an improvement of the mean normalized bias for three-hourly observations of atmospheric GOM from 9.7 to 0.5, Furthermore, the diurnal correlation between model and observation increased from 0.01 to 0.64. This is a strong indicator, that GOM is not directly emitted from primary sources but is mainly created by oxidation of GEM.