Elevation gradients of European climate change in the regional climate model COSMO-CLM

A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as wel

Regional climate model simulations as input for hydrological applications: evaluation of uncertainties

International audienceThe ERA15 Reanalysis (1979-1993) has been dynamically downscaled over Central Europe using 4 different regional climate models. The regional simulations were analysed with respect to 2m temperature and total precipitation, the main input parameters for hydrological applications. Model results were validated against three reference data sets (ERA15, CRU, DWD) and uncertainty ranges were derived. For mean annual 2 m temperature over Germany, the simulation bias lies between -1.1°C and +0.9°C depending on the combination of model and reference data set. The bias of mean annual precipitation varies between -31 and +108 mm/year. Differences between RCM results are of the same magnitude as differences between the reference data sets

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days?90pWET, contribution of the very wet days to total precipitation?R95pTOT and number of consecutive dry days?CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators? spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods.The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354). We also thank two anonymous referees for their useful comments that helped to improve the original manuscript

Assessing and Improving the Local Added Value of WRF for Wind Downscaling

Limited area models (LAMs) are widely used tools to downscale the wind speed forecasts issued by general circulation models. However, only a few studies have systematically analyzed the value added by the LAMs to the coarser-resolution-model wind. The goal of the present work is to investigate how added value depends on the resolution of the driving global model. With this aim, the Weather Research and Forecasting (WRF) Model was used to downscale three different global datasets (GFS, ERA-Interim, and NCEP?NCAR) to a 9-km-resolution grid for a 1-yr period. Model results were compared with a large set of surface observations, including land station and offshore buoy data. Substantial biases were found at this resolution over mountainous terrain, and a slight modification to the subgrid orographic drag parameterization was introduced to alleviate the problem. It was found that, at this resolution, WRF is able to produce significant added value with respect to the NCEP?NCAR reanalysis and ERA-Interim but only a small amount of added value with respect to GFS forecasts. Results suggest that, as model resolution increases, traditional skill scores tend to saturate. Thus, adding value to high-resolution global models becomes significantly more difficult.The authors thank Puertos del Estado (Spanish National Ports and Harbour Authority) and AEMET (Spanish Meteorological Agency) for providing buoy and land observational records. This work was partly supported by the projects EXTREMBLES (CGL2010-21869) and CORWES (GL2010-22158-C02-01), funded by the Spanish R&D program. The WRF simulations performed in this study were managed by WRF4G, which is an open-source tool funded by the Spanish government and cofunded by the European Regional Development Fund under Grant CGL2011-28864

Climate Scenarios for Switzerland CH2018 – Approach and Implications

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland

Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44? resolution and five Statistical Downscaling Methods (SDMs) ?analog resampling, weather typing and generalized linear models? trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices ?mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days? taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and theWorking Group on CoupledModelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure and AEMET and University of Cantabria for the Spain02 dataset (available at http: //www.meteo.unican.es/en/datasets/spain02). All the statistical downscaling experiments have been computed using theMeteoLab software (http://www.meteo.unican.es/software/meteolab), which is an open-source Matlab toolbox for statistical downscaling. This work has been partially supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme. AC thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354), JMG acknowledges the support from the SPECS project (FP7-ENV-2012-308378) and JF is grateful to the EUPORIAS project (FP7-ENV-2012-308291). We also thank three anonymous referees for their useful comments that helped to improve the original manuscript

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree‐days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS‐derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC‐2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment