Réf. Smiatek & al. 2009 - A

Référence bibliographique complète

SMIATEK, G., KUNSTMANN, H., KNOCHE, R., MARX, A. 2009. Precipitation and temperature statistics in high-resolution regional climate models: Evaluation for the European Alps. Journal of Geophysical Research, Vol. 114, D19107, doi:10.1029/2008JD011353

Abstract: In this study, high-resolution climate change data from the regional climate models COSMO-CLM, HIRHAM, RegCM, and REMO were evaluated in the Greater Alpine Region (GAR; 4°W–19°W and 43°N–49°N) and three additional subareas of 1.5° by 1° in size. Evaluation statistics include mean temperature and precipitation, frequency of days with precipitation over 1 mm and over 15 mm, 90% quantile of the frequency distribution, and maximum number of consecutive dry days. The evaluation for the 1961–1990 period indicates that the models reproduce spatial precipitation patterns and the annual cycle. The mean precipitation domain bias varies between 11% and 40% in winter season and between –14.5% and 11% in summer. Larger errors are found for other statistics and in the various regions. No single best model could be identified comparing modeled precipitation characteristics with observational reference. The study shows that there is still high uncertainty in the expected climate change. Furthermore, future temperature and precipitation changes simulated with different SRES scenarios and calculated by different RCMs overlap. The temperature calculations for the period 2071–2100 related to the period 1961–1990 in the GAR area show an increase in the monthly mean 2m temperature of up to 4.8 K in summer. In the GAR area, a precipitation decrease of up to 29% in summer and precipitation increase of approximately 20% in the winter season is simulated. Summer and autumn temperatures are expected to increase more than winter and spring temperatures. Detailed analysis reveals that the different regional climate model runs based on different regional models, different driving global models and different emission scenarios show similar trends, but differ in the magnitude of the expected climate change signal. All models seem to agree on the increased frequency of high-precipitation events in the winter season.


Organismes / Contact

Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlsruhe, Garmisch-Partenkirchen, Germany - gerhard.smiatek@imk.fzk.de

The work was partly funded within the Interreg III B Alpine Space project ClimChAlp by the Bayerisches Landesamt für Umwelt (LfU)

(1) - Paramètre(s) atmosphérique(s) modifié(s)
(2) - Elément(s) du milieu impacté(s)
(3) - Type(s) d'aléa impacté(s)
(3) - Sous-type(s) d'aléa
Temperature, Precipitation      

Pays / Zone
Massif / Secteur
Site(s) d'étude
Période(s) d'observation
Alps Greater Alpine Region        

(1) - Modifications des paramètres atmosphériques

Future Climate

The small number of high-resolution RCM climate change data sets available does not allow for generating and assessing meaningful ensemble statistics. For this reason, the corresponding figures depict the temperature and precipitation change predicted by the particular models. The monthly temperature differences between the scenario and the control run as well as the precipitation ratios of the scenario and control run for the UG-1 area are shown as annual cycles in Figure 9. The control run covers the period from 1961 to 1990. The scenario runs cover the periods from 2070 to 2099 for RegCM and from 2071 to 2100 for all other models. The temperature calculations in the UG-1 area show an increase in the monthly mean 2 m temperature of more than 5 K in August/September. Even in the B1/B2 scenarios, a temperature increase in summer of more than 3 K and an increase of 2 K to 3.4 K in winter are calculated. In UG-1, additional scenario B1 and A1B data from the REMO model demonstrate that the results based on different scenarios and different models overlap.

In the GAR and in the other investigation areas, the RCM models simulate a temperature increase in the winter season in the range of 2 K to over 3 K and of almost 5 K in summer as shown. The regions UG-2 and UG-3 show an even higher temperature increase.

Analysis of monthly mean values from the six GCMs and the SRES A1B scenario for the GAR area and the period 2070–2100 relative to 1961–1990 revealed an increase in temperature of 3.4 K in winter and 4.3 K in summer. The model ranges were 2 K–4.3 K in winter and 2.3 K–5.3 K in summer. On the average, the models show a precipitation increase by 10% with a range of 2.5%–25% in winter and a precipitation decrease by 30% with a range of –2.5% to –48% in summer. The simulated annual average temperature increase of 3.5 K is higher than the global mean temperature increase simulated for the A1B scenario. ECHAM5 and HadCM3 simulate a temperature increase in summer of 5.3 K in summer and 4.3 K (ECHAM5) and 3.5 K (HadCM3) in the winter season. The modeled precipitation decrease in summer is almost 40% (ECHAM5) and 48% (HadCM3). COSMO-CLM employing ECHAM5 boundary forcings and A1B scenario indicates a precipitation decrease of 29% in summer. Generally, there is a large discrepancy between the GCMs. However, results from ECHAM5 and HadCM3 are quite similar.

The precipitation change are illustrated as the ratio of the scenario mean to the control mean modeled by the investigated RCM models. In the GAR area, a precipitation decrease up to 29% in summer is simulated. In the winter season, all models indicate a precipitation increase. The range is from +4.5% (COSMO-CLM) to +25%(HIRHAM). In particular regions, however, the RCM models simulate much higher differences of almost 30% increase in the winter season and more than 40% decrease in summer.

In PRUDENCE [Christensen and Christensen, 2007], an ensemble of 25 model runs, mostly with a horizontal resolution of 0.5°, for the A2 scenario revealed an ensemble mean increase in the seasonal mean 2m temperature in the Alps (5°–15°E and 44°–48°N) of 3.53 K in winter (DJF), 3.32 K in spring (MAM), 5.04 K in summer (JJA), and 4.15 K in autumn (SON) compared to the 1961–1990 mean. The relative seasonal mean precipitation change was quantified to be +20% in winter, +2% in spring, –26% in summer, and –7% in autumn. The present investigation with the high-resolution data supports this trend, showing even higher differences on local scales.

All models agree in simulating an increasing frequency of strong precipitation events in the winter season. The monthly values in UG-1 suggest an increase in spring as well as in autumn.

In general, interpretation of the results remains difficult due to the overlap of the different scenarios and often contradictory results, e.g., increasing frequency of strong precipitation events in UG-1 in summer simulated by REMO for the B1 scenario and decreasing frequency simulated by RegCM for the B2 scenario. Here more highresolution modeling efforts with resolutions of at least 10 km are definitely needed.

The transient runs of the REMO and COSMO-CLM models allow for the consideration of periods until 2070. The simulated change of precipitation for the UG-1 area is shown for the REMO and for the COSMO-CLM model. In REMO, a precipitation increase is simulated for the winter (DJF) and spring (MAM), with the highest values in the B1 scenario being reached in the period 2016–2045 already, whereas the highest values for the A2 scenario are encountered in spring and 2056–2085. COSMO-CLM supports the B1 finding that might result from the driving GCM. However, the highest precipitation increase occurs in spring. Both models agree in a precipitation reduction in the summer season of around 20% in the A2 scenario. The typically dry autumn season might see a precipitation increase until 2045 or 2065, respectively.

Summary and Conclusions

Global climate models simulate a temperature increase of up to 5 degrees and substantial decrease in precipitation in summer for the Alps. With increasing spatial resolution of the regional climate models and increasing computational resources, the community’s interest in an assessment of the climatic change even in small river catchments is growing. This study focused on the systematic evaluation of the regional climate models RegCM, REMO, HIRHAM, and COSMO-CLM with a number of climatologies available. The evaluation shows that the models reproduce the monthly mean temperature and mean precipitation in the Greater Alpine Region. The mean precipitation domain bias varies between 11% and 40% in the winter season and between –14.5% and 11% in summer. Larger errors are found for other statistics. The fact that the models of higher resolution (HIRHAM and REMO) seemed to perform better in the smaller investigation areas might indicate a stronger need for high-resolution data with a spatial resolution in the order of 10 km or better. It was shown that there are still large biases in the reproduction of the present climate and substantial uncertainties in the expected climate change. Furthermore, temperature and precipitation changes resulting from different scenarios and different RCMs overlap. This means, for example, that the climate change signal derived by one RCM based on a more ecologically friendly SRES emission scenario is similar to a climate change signal derived by another RCM based on a less ecologically friendly emission scenario.

The temperature calculations in the GAR area show an increase in the monthly mean 2m temperature of up to 5 K in August. Even in the B2 scenario, a temperature increase in summer of up to 3.8 K and a moderate increase of up to 2 K in winter are calculated. In the GAR area, a precipitation decrease of up to 29% in summer is simulated. For the winter season, the models see a precipitation increase of approximately 20%.

Summer and autumn temperatures are expected to increase more than winter and spring temperatures. The observed trend of decreasing summer precipitation and increasing winter precipitation is expected to continue in the future. In certain regions, precipitation increase may be in the order of +40%, while the simulated precipitation decrease in the summer reaches values of up to –30%. Detailed analysis has shown that the regional climate models, global driving data sets, and various SRES emission scenarios exhibit similar trends. However, they differ in the magnitude of the expected climate change signal. All models seem to agree on an increased frequency of high precipitation amounts in the winter season. In summer, the signal does not allow for a clear conclusion to be drawn. With the exception of the HIRHAM model, all models simulate an increase of up to 20% in the 90% percentile value in UG-1 for the A2/A1B scenarios. This indicates that extreme precipitation events might increase although the total precipitation amount will decrease in large parts of the Alps.

As the systematic evaluation did not allow for an identification of a single best regional climate model, hydrological impact analysis should be based on the variety of climate model results in order to quantify their uncertainties. Due to the nonlinear runoff response and the aggregating properties of catchments, it is impossible to draw conclusions with respect to the performance of regional climate runs for hydrological fields from meteorological fields only. Shortfalls, e.g., in the winter precipitation, will be propagated till spring or summer, and an acceptable summer precipitation performance will not necessarily yield accurate summer runoffs. Before using the evaluated regional climate change data sets in a hydrological impact analysis, application of bias correction techniques is recommended, as for example, shown by Kunstmann and Stadler [2005], Kleinn et al. [2005], and Leander and Buishand [2007].

Evaluation of regional climate models for the Alpine region clearly revealed that there are still major obstacles to deriving reliable climate change trends: Firstly, improved high-resolution gridded precipitation climatologies must be generated in order to better assess the performance of regional models. Secondly, further high-resolution RCM simulations must be performed, preferably with nonhydrostatic RCMs that allow for resolutions finer than 10 km. Thirdly, bias correction techniques have to be developed and adjusted to the specific requirements of the highly variable Alpine precipitation and temperature distribution.


Informations complémentaires (données utilisées, méthode, scénarios, etc.)

Model evaluation and the assessment of the future climate change were performed in four investigation areas, including the entire Greater Alpine Region (GAR) (4°W–19°W and 43°N–49°N). In order to assess climate change effects in various parts of the Alpine region, investigation areas UG-1, UG-2, and UG-3 of 1.5° by 1° in size were chosen. UG-1 represents a part north of the main Alpine ridge. Here a hydrological impact study was performed within ClimChAlp. The UG-2 area represents the area south of the main Alpine ridge, while UG-3 is situated in the southwestern part. This selection was motivated by a previous study with the regional model MM5 driven with boundary forcings from the ECHAM4 GCM, which revealed in these regions greater precipitation changes as in the average of the Alpine area [Smiatek et al., 2007].

The climatological statistics applied were selected from a hydrological point of view. The focus was placed on performance analysis for subsequent hydrological impact modeling. It included mean monthly temperature as well as daily mean precipitation, the frequency of wet days with precipitation above 1 mm/d and strong precipitation frequency with precipitation above 15 mm/d, 90% quantile of the precipitation distribution on wet days, and the number of consecutive dry days. Acronyms and the applied statistics are listed in Table 2.

Acronym Definition Unit
MEA-P Mean climatological precipitation mm/d
MEA-T Mean climatological temperature K
FRE-1 Frequency (ratio) of days with precipitation >1 mm fraction
FRE-15 Frequency (ratio) of days with precipitation >15 mm fraction
Q90 90% quantile of distribution function on wet days mm/d
XCCD Maximum number of consecutive dry days d

Temperature and Precipitation Statistics Used in This Study

Despite the dense monitoring station network, there is still a limited availability of high-resolution gridded reference data at daily resolution.

As part of the ALP-IMP Project (Multicentennial climate variability in the Alps based on Instrumental data, Model simulations and Proxy data), high resolution gridded (10 arc seconds resolution) climatic data (CRU ALP-IMP) were generated for the Greater Alpine Region at the Climatic Research Unit (CRU), Norwich, UK. The monthly precipitation totals for the 1800–2003 period are based on 192 long-term homogenized precipitation series [Auer et al., 2005] from meteorological stations and a highr esolution precipitation climatology for the 1971–1990 period [Efthymiadis et al., 2006]. The major data source in CRU ALP-IMP is the HISTALP (Historical instrumental climatological surface time series of the Greater Alpine Region) database. The extent covered by the CRU ALPIMP climatology data is identical with the investigation area GAR. An additional data set available is the gridded 10 arc minutes time series CRU TS 1.2 data set covering the European land surface [Mitchell et al., 2004; Mitchell and Jones, 2005]. The data comprise 1200 grids of climate observed during the period from 1901 to 2000 using methodology described by New et al. [2000]. From the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project, a first version of the European daily high-resolution gridded data set (ECA) of surface temperature and precipitation of 0.25° resolution is available [Haylock et al., 2008]. The data are expected to be extended to a higher resolution in the future. Gridded monthly and annual global air temperature and precipitation data for the period from 1950 to 1999 at 0.5 degrees by 0.5 degrees of latitude/longitude grid are also available from the University of Delaware, where they were derived from the global historical climatology network and station records provided by Legates and Willmott [1990a, 1990b]. Additional data are the Alpine precipitation analyses from high-resolution rain gauge observations of the years 1971–1990 in grid resolution of about 25 km available from the Data Centre of the Mesoscale Alpine Programme (MAP [Frei and Schär, 1998]).

Tests with the climatology data available for the annual precipitation cycle as in CRU ALP-IMP and CRU 1.2 showed in the GAR area differences of up to 0.3 mm/d or 20% of the CRU ALP-IMP observed value. In order to increase the accuracy especially of daily statistics, daily precipitation data from the monitoring station network were acquired for the UG-1 area. The observed data DWD-HD from the Hydrographischer Dienst Land Tirol and Hydrographischer Dienst Salzburg as well as from Deutscher Wetterdienst (DWD) were rasterised in 10 arc minutes by 10 arc minutes grid by averaging all monitoring station data available within a grid cell.

Grid cells over the Mediterranean Sea were excluded from comparisons with observational data, as these data are limited to the land surface. In the analysis of the future climate and in comparisons with the present climate, however, sea grid cells were included. The precipitation statistics were derived from daily data with 30 days/month in case of data driven by HadAM3H and real calendar data in case of the COSMO-CLM and REMO models. HadAM3H is an atmosphere-only model derived from the atmospheric part of HadCM3 [Pope et al., 2000]. In HadCM3 distortions of the seasonal cycle are minimized by shifting the date of perihelion from 2.5 days to 3.2 days after the beginning of the year.

As a result, more than 840 values were available for each month, which ensured robust frequency and distribution characteristics. The daily statistics of each cell were averaged over the years and over the investigation area. In order to reach the highest possible accuracy, all model data were accessed in their original map projections without any interpolation. The available data, driving GCMs, resolutions as well as the employed SRES [Nakicenovic, 2000] scenarios are shown in Table 3. The full extent of the employed RCM data is depicted in Figure 2. REMO and COSMOCLM data are also available in hourly resolution.






SRES Scenarios





2001– 2100

B1, A1B, A2 CERA database



2001– 2100

B1, A1B CERA database



2070– 2100


20 km


2070– 2099

B2,A2 personal communication

Table 3. RCM Data Used in the Present Study [see details in the study]
(In the present investigation, data of daily resolution were applied)

(2) - Effets du changement climatique sur le milieu naturel

Sensibilité du milieu à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)


(3) - Effets du changement climatique sur l'aléa

Paramètre de l'aléa
Sensibilité des paramètres de l'aléa à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)


(4) - Remarques générales

In the last century the mean temperature in the European Alps has increased by 1.1 K [Böhm et al., 2001]. This is roughly twice the global average of 0.6 K [Jones et al., 1999]. Global climate models indicate for the Alps a possible future temperature increase relative to the period 1980–1999 of up to 5 K [IPCC, 2007], as well as a substantial decrease in the precipitation amount in the summer season. Therefore the Alps are the subject of extensive investigation of the expected climate change and its impact.

Most climate change studies rely on simulations of the future climate by statistical downscaling models (SDM) or regional climate models (RCM) forced by boundary data from General Circulation Models (GCM). Christensen and Christensen [2007], Schmidli et al. [2007], Jacob et al. [2007], and Frei et al. [2006, 2003] presented intercomparisons and evaluations of dynamic and statistical downscaling performed in various international projects, such as STARDEX (Statistical and Regional dynamical Downscaling of Extremes for European regions [Goodess, 2003]), MERCURE (Modelling European Regional Climate: Understanding and Reducing Errors [Busch and Heimann, 2001]), MICE (Modelling the Impact of Climate Extremes [Hanson et al., 2007]) or PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects [Christensen and Christensen, 2007]). This article broadens these investigations, as it also covers data obtained from transient model runs and time slice experiments that yielded high-resolution regional climate change data of less than 20 km grid resolution. Such data are urgently required in subsequent hydrological and hydrological climate change impact analysis. The study has been performed in the framework of Interreg III B Alpine Space project ClimChAlp (Climate change, impacts, and adaptation strategies in the Alpine Space) (http://www.climchalp.org/). ClimChAlp aimed at supporting the political decisions regarding protection and natural disaster prevention related to climate change in the European Alps.



Model and Institution


ARPEGE GCM Global and spectral general circulation of CNRM (Centre National de Recherches Météorologiques) Déqué et al. [1994]
CCSM3 GCM NCAR Community Climate System Model 3.0 Collins et al. [2006]
CGCM3 GCM The Third Generation Coupled Global Climate Model, Canadian Centre for Climate Modeling and Analysis (CCCma) Flato and Boer [2001]
CHRM RCM Swiss Federal Institute of Technology (ETH), climate version of ‘‘Europamodell’’ of German and Swiss weather services Lüthi et al. [1996]
COSMO-CLM RCM Climate version of the Lokal Modell of German weather service, until version 4 CLM Böhm et al. [2006]
CNCM3 GCM CNRM-CM3 GCM, Météo-France, France Salas-Mélia et al. [2005]
ECHAM5 GCM 5th generation of the ECHAM GCM, Max Planck Institute for Meteorology (MPI), Hamburg, Germany Roeckner et al. [2006]
HadCM3 GCM Hadley Centre, UK Meteorological Office global climate model Gordon et al. [2000]
HadRM RCM Hadley Centre, UK Meteorological Office regional climate model Pope et al. [2000]
HIRHAM RCM Danish Meteorological Institute (DMI) Christensen et al. [1996]
CSIRO Mk3 GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO) coupled climate model Mk3.0 Gordon et al. [2002]
MM5 RCM NCAR/Penn State University mesoscale model Dudhia [1993]
RegCM RCM International Centre for Theoretical Physics (ICTP), Trieste, Italy Regional Climate Model Giorgi et al. [1993b] and Gao et al. [2006]
REMO RCM Regional Model, MPI, Hamburg Jacob et al. [2001]

Table 1. List of Models Mentioned in This Study

In general, all regional climate models show some shortcomings when applied in order to reconstruct the observed present-day climate. Within the QUIRCS (Quantification of Uncertainties In Regional Climate and climate change Simulations) project [Kotlarski et al., 2005; Keuler, 2006] climate model simulations for the area of Germany were evaluated. The regional models REMO (The acronym explanations are given in Table 1.), COSMO-CLM, and MM5 were driven by ECMWF (European Centre for Medium-Range Weather Forecasts) ERA15 reanalysis data for the period of 1979–1993. The bias versus observational reference in the 2m temperature over Germany was quantified to be –1.1 K to +0.9 K and annual bias in precipitation varied between –31 mm/a and +108 mm/a. The differences between the models were in the same range as the differences between the reference data sets. Frei et al. [2003] evaluated daily precipitation statistics from four regional climate models (CHRM, HadRM3, HIRHAM, REMO) and ARPEGE GCM, all with a horizontal resolution of 50 km in the European Alps (2.25°–17.25°E and 42.25°–48.75°N). 15-year integration (1979–1993) RCM runs were forced by ERA15 reanalysis data and compared with observational data based on 6400 rain gauge records. The results showed that the models reproduce the mean characteristics of the annual cycle and spatial distribution. The domain mean bias varied between –23% and +3% in the winter season and between –27% and –5% in summer. Large errors, however, were found in other statistics. The models underestimated the precipitation intensity in summer, with the frequency of heavy precipitation events being far too low. Moreover, the evaluation did not identify a single best model. Each model showed substantial deviations in some statistics describing the distribution function of daily precipitation. Application of data from RCMs and SDMs in hydrological models requires, however, reasonable accuracy in all precipitation statistics.

Biases in runs with reanalysis data generally are smaller compared to model runs with GCM boundary forcings. Déqué et al. [2007] investigated uncertainties in RCM downscaling experiments. The uncertainty introduced by the choice of the GCM generally was the largest. In PRUDENCE, Jacob et al. [2007] calculated bias ranges of –19% to +26% compared to the climatological value of 3.3 mm/d in winter(DJF) and –29% to +15% compared to the climatological value 4 mm/d in summer (JJA). Kunstmann et al. [2004] investigated the impact of climate change for an Alpine catchment using the distributed hydrological model WaSiM-ETH (Water Balance Simulation Model-ETH [Jasper et al., 2002]) driven with dynamically downscaled ECHAM4 data. Due to the substantial underestimation of precipitation in the summer season of up to 100% and an overestimation of winter precipitation of up to 50%, a bias correction had to be applied.

The Alps represent a challenging test ground for the downscaling performance of RCMs and SDMs. The density of observational reference data is relatively high. Hence a wide range of evaluation experiments can be carried out on a daily or monthly mean level at least. It must be taken into account, however, that long-term station records are often affected by various changes in the local environment, instrumentation, and other factors [Schmidli et al., 2002] and, thus are subject to some uncertainty. Frei et al. [2003] estimated the precipitation bias (underestimate) in the Alpine area due to measurement and network bias to amount to 10% in summer and 16% in the winter season.

(5) - Syntèses et préconisations

Références citées :

Auer, I., et al. (2005), A new instrumental precipitation dataset for the greater alpine region for the period 1800 –2002, Int. J. Climatol., 25, 139–166, doi:10.1002/joc.1135.

Böhm, R., I. Auer, M. Brunetti, M. Maugeri, T. Nanni, and W. Schöner (2001), Regional temperature variability in the European Alps: 1760– 1998 from homogenized instrumental time series, Int. J. Climatol., 21, 1779–1801, doi:10.1002/joc.689. - [Fiche biblio]

Böhm, U., M. Kücken, W. Ahrens, A. Block, D. Hauffe, K. Keuler, B. Rockel, and A. Will (2006), CLM — the climate version of LM. Brief description and long-term applications, in COSMO Newsletter No. 6, COSMO, Deutscher Wetterdienst, Offenbach am Main.

Busch, U., and D. Heimann (2001), Statistical-dynamical extrapolation of a nested regional climate simulation, Clim. Res., 19, 1 – 13, doi:10.3354/ cr019001.

Christensen, J. H., and O. B. Christensen (2007), A summary of the PRUDENCE model projections of changes in European climate by the end of this century, Clim. Change, 81, 7 –30, doi:10.1007/s10584-006-9210-7.

Christensen, J., O. Christensen, P. Lopez, E. V. Meljgaard, and M. Botzet (1996), The HIRHAM4 atmospheric climate model, Tech. Rep., DMI Rep. 96-4, Danish Meteorol. Inst., Copenhagen.

Collins, W., et al. (2006), The community climate system model version 3 (CCSM3), J. Clim., 19, 2122– 2143.

Déqué, M., D. P. Rowell, D. Lüthi, F. Giorgi, J. H. Christensen, B. Rockel, D. Jacob, E. Kjellström, M. de Castro, and B. van den Hurk (1994), The ARPEGE-IFS atmosphere model: A contribution to the French community climate modelling, Clim. Dyn., 10, 249– 266.

Déqué, M., et al. (2007), An intercomparison of regional climate simulations for Europe: Assessing uncertainties in model projections, Clim. Change, 81, 53–70, doi:10.1007/s10584-006-9228-x.

Dudhia, J. (1993), A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation tests and simulation of an atlantic cyclone and cold front, Mon. Weather Rev., 121, 1493– 1513.

Efthymiadis, D., P. D. Jones, K. R. Briffa, I. Auer, R. Böhm, W. Schöner, C. Frei, and J. Schmidli (2006), Construction of a 10-min-gridded precipitation data set for the Greater Alpine Region for 1800–2003, J. Geophys. Res., 110, D01105, doi:10.1029/2005JD006120.

Flato, G., and G. Boer (2001), Warming asymmetry in climate change simulations, Geophys. Res. Lett., 28, 195–198. Frei, C., and C. Scha¨r (1998), A precipitation climatology of the Alps from high-resolution rain-gauge observations, Int. J. Climatol., 18, 873– 900.

Frei, C., J. H. Christensen, M. Déqué, D. Jacob, R. G. Jones, and P. L. Vidale (2003), Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps, J. Geophys. Res., 108(D3), 4124, doi:10.1029/2002JD002287.

Frei, C., R. Schöll, S. Fukutome, J. Schmidli, and P. L. Vidale (2006), Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models, J. Geophys. Res., 111, D06105, doi:10.1029/2005JD005965.

Gao, X., J. S. Pal, and F. Giorgi (2006), Projected changes in mean and extreme precipitation over the Mediterranean region from a high resolution double nested RCM simulation, Geophys. Res. Lett., 33, L03706, doi:10.1029/2005GL024954.

Giorgi, F., M. R. Marinucci, and G. T. Bates (1993b), Development of a second-generation regional climate model (RegCM2): Part I. Boundarylayer and radiative transfer processes, Mon. Weather Rev., 121, 2794– 2813.

Goodess, C. (2003), Statistical and regional dynamical downscaling of extremes for European regions: STARDEX, Eggs, 6, 25– 29.

Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood (2000), The simulation of sst, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dyn., 16, 147 – 168, doi:10.1007/s003820050010.

Gordon, H., et al. (2002), The CSIRO Mk3 climate system model, Tech. Rep., CSIRO Atmos. Res. Tech. Pap. 60, 130 pp., CSIRO, Victoria, Aust.

Hanson, C., et al. (2007), Modelling the impact of climate extremes: An overview of the MICE project, Clim. Change, 81, 163–177, doi:10.1007/ s10584-006-9230-3.

Haylock, M., N. Hofstra, A. K. Tank, E. Klok, P. Jones, and M. New (2008), A European daily high-resolution gridded dataset of surface temperature and precipitation for 1950 – 2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

IPCC (2007), Summary for policymakers, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K. Jacob, D., et al. (2001), A comprehensive model intercomparison study investigating the water budget during the BALTEX-PIDCAP period., Meteorol. Atmos. Phys., 77, 19– 43.

Jacob, D., et al. (2007), An inter-comparison of regional climate models for Europe: Model performance in present-day climate, Clim. Change, doi:10.1007/s10584-006-9213-4.

Jasper, K., J. Gurtz, and H. Lang (2002), Advanced flood forecasting in alpine watersheds by coupling meteorological observations and forecasts with distributed hydrological model., J. Hydrol., 267, 40 – 52, doi:10.1016/S0022-1694(02)00138-5.

Jones, P. D., M. New, D. E. Parker, S. Martin, and I. G. Rigor (1999), Surface air temperature and its changes over the past 150 years, Rev. Geophys., 37, 73–199.

Keuler, K. (2006), Quantifizierung von Ungenauigkeiten regionaler Klimaund Klimaa¨nderungssimulationen. Abschussbericht, technical report, BTU Cottbus.

Kleinn, J., C. Frei, J. Gurtz, D. Lüthi, P. L. Vidale, and C. Schär (2005), Hydrologic simulations in the Rhine basin driven by a regional climate model, J. Geophys. Res., 110, D04102, doi:10.1029/2004JD005143.

Kotlarski, S., A. Block, U. Böhm, D. Jacob, K. Keuler, R. Knoche, D. Rechid, and A. Walter (2005), Regional climate model simulations as input for hydrological applications: Evaluation of uncertainties, Adv. Geosci., 5, 119–125.

Kunstmann, H., and C. Stadler (2005), High resolution distributed atmospheric-hydrological modelling for alpine catchments, J. Hydrol., 314, 105– 124.

Kunstmann, H., K. Schneider, R. Forkel, and R. Knoche (2004), Impact analysis of climate change for an alpine catchment using high resolution dynamic downscaling of echam4 time slices, Hydrol. Earth Syst. Sci., 8, 1031–1045.

Leander, R., and A. Buishand (2007), Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487– 496, doi:10.1016/j.jhydrol.2006.08.006.

Legates, D. R., and C. J. Willmott (1990a), Mean seasonal and spatial variability global surface air temperature, Theor. Appl. Climatol., 41, 11 – 21.

Legates, D. R., and C. J. Willmott (1990b), Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111 –127.

Lüthi, D., A. Cress, H. C. Davies, C. Frei, and C. Schär (1996), Interannual variability and regional climate simulations, Theor. Appl. Climatol., 53, 185–209.

Mitchell, T. D., and P. D. Jones (2005), An improved method of constructing a database of monthly climate observations and associated highresolution grids, Int. J. Climatol., 26, 693– 712, doi:10.1002/joc.1181.

Mitchell, T. D., T. R. Carter, P. D. Jones, M. Hulme, and M. New (2004), A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901 – 2000) and 16 scenarios (2001 – 2100), Tech. Rep. Tyndall Centre Working Paper 55, Tyndall Centre, Norwich, U. K.

Nakicenovic, N. (Ed.) (2000), IPCC Special Report on Emissions Scenarios, 599 pp., Univ. Press Cambridge, Cambridge, U. K.

New, M., M. Hulme, and P. D. Jones (2000), Representing twentieth century space-time climate variability: Part 2. Development of 1901 – 96 monthly grids of terrestrial surface climate, J. Clim., 13, 2217–2238.

Pope, V. D., M. L. Gallani, P. R. Rowntree, and R. A. Stratton (2000), The impact of new physical parameterizations in the Hadley Centre climate model : HadAM3, Clim. Dyn., 16, 123 – 146, doi:10.1007/ s003820050009.

Roeckner, E., R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, L. Kornblueh, E.Manzini, U. Schlese, andU. Schulzweida (2006), Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model, J. Clim., 19, 3771–3791, doi:10.1175/JCLI3824.

Salas-Mélia, D., F. Chauvin, M. Dqu, H. Douville, J. Gueremy, P. Marquet, S. Planton, J. Royer, and S. Tyteca (2005), Description and validation of the CNRM-CM3 global coupled model, Tech. Rep., CNRM Working Note 103.

Schmidli, J., C. Schmutz, C. Frei, H. Wanner, and C. Schär (2002), Mesoscale precipitation variability in the region of the European Alps during the 20th century, Int. J. Climatol., 22, 1049– 1074, doi:10.1002/ joc.769.

Schmidli, J., C. M. Goodess, C. Frei, M. R. Haylock, Y. Hundecha, J. Ribalaygua, and T. Schmith (2007), Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps, J. Geophys. Res., 112, D04105, doi:10.1029/2005JD007026. - [Fiche biblio]

Smiatek, G., H. Knoche, A. Marx, and H. Kunstmann (2007), Evaluation of regional high resolution climate change data for the Alpine region (ClimChAlp), Final report to Bayerisches Landesamt für Umwelt (LfU), 77 pp.