Future weather types and their inﬂuence on mean and ...

BMeteorologische Zeitschrift, Vol. 23, No. 3, 231–252 (published online April 4, 2014) Open Access Article© 2014 The authors

Future weather types and their influence on mean andextreme climate indices for precipitation and temperature inCentral Europe

Ulf Riediger1∗ and Annegret Gratzki1

1Deutscher Wetterdienst, Germany

(Manuscript received July 3, 2013; in revised form November 8, 2013; accepted November 13, 2013)

AbstractIn Central Europe, the spatial and temporal distributions of precipitation and temperature are determined bythe occurrence of major weather types. In this paper, we examine climate indices (i.e. mean values or hot, cold,wet and dry days) for different weather types in a recent (1971–2000) and future climate (2070–2099). Theweather types are classified objectively for the control run and for the A1B scenario with an ensemble of eightglobal climate simulations (GCM) to be compared with different reanalyses. To derive climate indices, thehigh-resolution, regionalized reference dataset HYRAS and an ensemble of nine regional climate simulations(RCM) are used. Firstly, the reliability of simulated weather patterns and their climate indices are tested in thecontrol period. The reanalyses circulation climatology can be reproduced well by the GCM ensemble mean.For temperature and precipitation, each climate index is characterized and evaluated in terms of definedweather patterns. The comparison of HYRAS and RCM data show reliable mean temperature values withdifferences between weather classes by +2 to −6 °C during winter (13 to 19 °C in summer). The analysis ofobserved and simulated precipitation reveal that mean winter precipitation is significantly influenced by thedirection of air flow, while in summer, mesoscale atmospheric patterns of cyclonic rotation play a larger role.Secondly, the analysis of potential future changes simulated by the RCM ensemble were able to demonstratethat weather type changes, superior climate trends (such as mean warming) and their interaction lead tomajor changes for precipitation and temperature in Central Europe. While temperature differences betweencold and warm weather types are nearly stable over time, the ensemble temperature changes (with a rangeof +2 to +4 °C) reinforce warm/hot conditions in the future winter and summer. Milder, wetter winterscan be explained by an increased occurrence of warm south-westerlies and a decrease in cold easterlies.Thereby, an increase of extensive areal rainfall events is simulated for specific weather types. Otherwise,warmer and drier summers are projected by the RCM ensemble. Here, a few weather patterns are relevantfor very hot conditions with the total number of very hot days where the mean daily temperature greater than25 °C increases. Thereby, anticyclonic weather patterns are most relevant for non precipitation events andparticulary, the number of days with anticyclonic westerlies is expected to double in the future.

Keywords: Regional Climate Model, Global Climate Model, Weather Types

1 IntroductionA change in average precipitation, temperature and inthe number of extreme events are relevant for derivingadaptation strategies to reduce socio-economic costs ina world with a changing climate. Changes in the atmo-spheric circulation and their link to particular weatherevents are thus an essential issue in the debate about cli-mate change (Solomon et al., 2007).

In Central Europe, climate and weather are mainlycharacterized by transitions of high and low pressuresystems. Temperature differences exist between theequatorial and polar regions resulting in the transportof energy, in the form of heat and humidity, in merid-ional and zonal directions via atmospheric circulation.Depending on the geographical position of the partici-pating pressure centres, daily weather types can be cor-related with particular weather patterns.

∗Corresponding author: Ulf Riediger, Deutscher Wetterdienst, FrankfurterStr. 135, 63067 Offenbach, Germany, e-mail: [email protected]

In fact, it is the circulation dynamics that mainly in-fluence the spatial distribution and temporal variabilityof precipitation (Hurrell, 1995; Haylock and Good-ess, 2004; Pauling et al., 2006; Bárdossy, 2010), tem-perature (Domonkos et al., 2003; Yiou et al., 2008;Plavcová and Kyselý, 2011) and their extremes (Wibigand Glowicki, 2002; Ionita, 2009; Jacobeit et al.,2009). In the last decades, Central Europe has witnessedvarious circulation patterns induced phenomena likeheat waves (1997, 2003, 2006; see Schär et al. 2004),cold spells (2010, 2012; see Cattiaux et al. 2010), ma-jor floods (1997, 2002, 2005; see Kundzewicz et al.2005) and a large-scale drought (2003; see Garcia-Her-rera et al. 2010) which place great stress on economics,environmental and social systems.

With respect to a more profound understanding ofextreme events, an interpretation of the triggering atmo-spheric structure could be given by classifying weatherpatterns. For extreme events, van den Besselaar et al.(2010) found dependencies between the 10th (90th) per-

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232 U. Riediger & A. Gratzki: Future weather types and their influence on precipitation and temperature Meteorol. Z., 23, 2014

centiles of temperature and circulation type frequencies.Temperature extremes can thus be estimated (explainedvariance), in particular, hot events (DJF: 75 % and JJA50 %) can be better estimated than cold events (DJF:53 % and JJA 30 %). In terms of potential future cli-mate change, circulation dependent extreme tempera-ture episodes, such as cold or heat waves, are of highsocio-economic interest (Blenkinsop et al., 2009). Ja-cobeit et al. (2009) conclude that in large parts of Cen-tral Europe, only a few weather types are conduciveto extreme events. In summer, extended high pressureridges favour hot extremes, while strong cyclonic pat-terns or north-westerly cold fronts often lead to exten-sive areal precipitation. An enhanced zonal circulationmainly induces these events in winter. With the expla-nation of the initial atmospheric condition, some hydro-logical phenomena such as high and low water eventscan be estimated (Stahl, 2001; Jacobeit et al., 2006;Samaniego and Bárdossy, 2007; Petrow et al., 2009;Fleig et al., 2010). In the field of synoptic climatology,circulation patterns are used for a wide range of applica-tions (Huth et al., 2008).

Early studies have analyzed circulation and weatherpatterns to help interpret differences between presentand future climates (Bárdossy and Caspary, 1990;Slonosky et al., 2000; Kyselý and Huth, 2006; De-muzere et al., 2009). The analysis of reconstructedpressure patterns reveals, for the period 1850 to 2003,that changes in circulation patterns contribute to the ob-served warming in Central Europe (Philipp et al., 2007).Especially in winter, increasing zonal patterns have ledto warmer conditions, while meridional patterns, whichare associated with cold conditions, have decreased. InEurope, more changes have occurred in the frequencyof circulation patterns in winter than in summer (Ky-selý and Huth, 2006). During summer, the Azores highhas strengthened and the majority of Europe is affectedby an increased sea level pressure on average (Della–Marta et al., 2007).

Possible occurrences of future climate events can beestimated with climate model simulations, which de-scribe the physical relationships of current and futureclimate in the form of transient simulations. For the de-tected circulation trends described in the previous para-graph, climate simulations show progressive changesin the same directions (Demuzere et al., 2009; Donatet al., 2010; Hanafin et al., 2011). Most of the com-mon global climate models project poleward shifts inthe north-hemispheric storm tracks that influence theeffects of fronts and atmospheric flow over CentralEurope (Yin, 2005; Bengtsson et al., 2006; Ulbrichet al., 2009). With an ensemble of global climate mod-els, van Ulden and van Oldenborgh (2006) identi-fied that changes in future precipitation are primarily in-duced by circulation changes and thereby explains thetendencies to drier summers and wetter winters. In prin-ciple, the large-scale pressure systems are much bet-ter represented than rare extremes. Nevertheless, sta-tionarity of the relationship between circulation and cli-

mate is marked by within-type variations (Beck et al.,2007). Thereby subgrid-scale processes (i.e. orographicinduced convection) or climate boundary conditions (i.e.sea surface temperature of the North Atlantic) influ-ence the effectiveness of weather types. For future as-pects, van Ulden and van Oldenborgh (2006) showthat the changes in continental temperature distributionare bounded by changes in land surface processes, soilmoisture, cloudiness and radiation.

An essential premise is that models can represent allrelevant climate processes and thus permit sound state-ments about a possible future development of the cli-mate system. Confidence in climate projections is oftensubjectively heightened if climate model results show agood agreement with observations in the control periodand if they can reproduce the main circulation statisticsof the past climate (Kreienkamp et al., 2009). It is likelythat biased circulation simulations negatively affect thetemperature and precipitation results of the regional cli-mate models (van Ulden et al., 2007). This has led torecent assessments of the circulation dynamics in the cli-mate system (Raible et al., 2005; Tolika et al., 2006;van Ulden and van Oldenborgh, 2006; Demuzereet al., 2009; Sanchez-Gomez et al., 2009; Rust et al.,2010; Woollings, 2010).

In this study, a combinated analysis is performedwhich takes a number of these mentioned aspects intoaccount. Primarily, weather types and their impacts onCentral European precipitation and temperature regimesare evaluated for past and future climates with respectto their uncertainties which depend on ensemble con-figuration, model deficiencies and the particular param-eter considered. In comparison with previous studies,the data presented here is based on the connection ofcirculation statistics (simulated by global climate mod-els) with precipitation and temperature statistics (sim-ulated by nested regional climate models). With theseparation of the derived climate indices into weathertypes, circulation-specific model trends and biases aredetectable. Fig. 1 shows a flow chart of analysis stepsand relationships between the various data.

In the following section, this paper describes the dataused (reanalysis, climate simulations and gridded ob-servational data) and the method for objective weathertyping. Aspects of the derived weather type climatolo-gies modelled by the reanalysis data sets and the climatecontrol runs are carried out in Section 3. An interpre-tation of weather type trends in a changing climate isgiven in Section 4. Additionally, the changes in precip-itation, temperature and extreme indices are linked withchanges in circulation weather type regimes. The paperis concluded in Section 5.

2 Data and methodology

2.1 Reanalyses and global climate models

The atmospheric parameters for weather typing arebased on daily fields from various reanalysis and global

Meteorol. Z., 23, 2014 U. Riediger & A. Gratzki: Future weather types and their influence on precipitation and temperature 233

Figure 1: Flow chart of analysis steps and the relationships between the various data sets.

Table 1: IPCC global climate model ensemble included in this study.

GCM Abbreviation / Runs Institute

BCCR-BCM2 BCM2r1 Bjerkness Centre for Climate ResearchCNRM-CM3 CNCM3r1 Centre National de Recherches Météorologiques InstituteMPI-ECHAM5 ECHAM5r1,ECHAM5r2,ECHAM5r3 Max Planck Institute for MeteorologyFUB-EGMAM EGMAMr3 Freie Universität Berlin, Institute für MeteorologieDMI-ECHAM5 ECHAM5r4 Danish Meteorological InstituteMETO-HC-HADCM3C HADCM3Cr1 UK Met Office, Hadley Centre

climate simulation data sets. The reanalysis data setsare NCEP/NCAR (Kistler et al., 2001) and ERA-40(Uppala et al., 2005). A state-of-the-art assessment ofuncertainties is given by these numerically dissimilarreanalysis products in Santer et al. (2003). Reanaly-sis models estimate the state of climate, each with aninternally consistent model physics scheme and opera-tional analysis of the atmosphere. Nevertheless, somewell-known inconsistencies (e.g. the quality of assimi-lated data) can affect the model reliability. For exam-ple, changes in the observation configuration can also beseen after the pre-satellite era (Trenberth et al., 2001;Brönnimann et al., 2009). Therefore, in climate changestudies, reanalysis data should be used critically (San-ter et al., 2004). With increasing observational cover-age, the weighting of the reanalysis model is reduced(Bengtsson et al., 2004). To avoid these inconsisten-cies, the objective weather types were derived for a morerecent time period (1971–2000) (see Section 2.2).

Global climate models are an essential tool for an-alyzing the present and future climate in a physicalframework. To estimate the sources of uncertainties andhence the robustness of the climate signal, the multi-model approach is an advantageous tool for handlingdeficiencies in each single model output. When it comesto the process of decision making regarding long last-ing environmental or political issues, the single-modelapproach cannot address the uncertainties associatedwith future climate change. In this study, global sim-ulations (see Table 1) with the SRES emission sce-nario A1B (Solomon et al., 2007) were used. The globalclimate simulation data sets used in this study areBCM2 (Ottera et al., 2009), CNCM3 (Salas-Meliaet al., 2005), ECHAM5 (Röckner et al., 2003, 2006a,b),EGMAM (Legutke and Voss, 1999; Min et al., 2005;Hübener et al., 2007) and HADCM3C (Johns et al.,2003, 2011). These global climate simulations are tran-sient for the climate control period (1971–2000) to the


future projection period (2070–2099). BCM2, the sec-ond version of the Bergen Climate Model is derivedfrom the ARPEGE-CLIMAT atmospheric general cir-culation model and the MICOM 2.8 Ocean Model cou-pled with the GELATO sea-ice model (Ottera et al.,2009). CNCM3, the third version of the Centre Nationalde Recherches Météorologiques Coupled Global Cli-mate Model consists of the ARPEGE-CLIMAT atmo-spheric general circulation model, OPA8.1 ocean model,the GELATO2 sea-ice model and TRIP river routingscheme (Salas-Melia et al., 2005). ECHAM5, the fifthversion of this atmospheric general circulation model,has been developed from the ECMWF forecast modeland a parametrization package contributed by the MPIHamburg and is coupled with the MPI-OM ocean model(Röckner et al., 2003, 2006a,b). The ECHO-G middleatmosphere model (EGMAM) has been constructed bythe coupled atmospheric ocean model from ECHAM4and HOPE-G (Legutke and Voss, 1999; Min et al.,2005). A special feature of this model is a fully resolvedstratosphere which improves the simulation of the cli-mate in the upper troposphere (Hübener et al., 2007).The HADCM3C simulation is based on a configurationof the HadCM3 model with a flux adjustment, an in-teractive terrestrial vegetation, and an ocean carbon cy-cle (Johns et al., 2011). An interactive sulphur chem-istry cycle and sulphate aerosol scheme is also included,which means that two forcing effects are modelled: ab-sorption of incoming solar radiation and the indirectcloud albedo increase by aerosol particles.

2.2 Objective weather type schemeWeather type classifications are often used for a syn-optic description of the large-scale atmospheric cir-culation (Yarnal, 1993; Huth, 1996; James, 2007;Philipp et al., 2007; Anagnostopoulou et al., 2008;Huth et al., 2008; Philipp et al., 2010). The spatial pat-terns of atmospheric elements like pressure, tempera-ture, wind and humidity can be described by a number ofclasses defined by the chosen classification techniques(Philipp et al., 2010). Each one of these algorithms canbe sorted into one of five main techniques - subjective,threshold, principal component analysis, leader or op-timization algorithms. Fixed, predefined rules to deter-mine the daily atmospheric state in a weather type aregiven by the threshold based methods. This procedureallows an automatic classification under consistent, clearand reproducible conditions (objectively classified), butthe declaration of the threshold follows some subjectivechoice.

Dittmann et al. (1995) developed such a thresholdbased classification method for the Central European re-gion which has been successfully applied since 1979by the Deutscher Wetterdienst for different applica-tions, e.g. climate monitoring and forecasting (Bissolliand Dittmann, 2001; Bissolli and Müller-West-ermeier, 2005; Bissolli et al., 2007). The method isdesigned to utilize the circulation analysis on a syn-optic scale for different European regions. Within the

Figure 2: Classification domain with coronary weighting fields(grey shaded) (Bissolli and Dittmann, 2001).

COST733 Action (Harmonisation and Applications ofWeather Type Classifications for European Regions),this classification is used for a wide range of clima-tological comparisons, evaluations and application is-sues (Huth et al., 2008; Beck and Philipp, 2010; Fleiget al., 2010; Philipp et al., 2010; Schiemann and Frei,2010; Tveito, 2010). One major outcome of these eval-uation and comparison studies is that there is currentlyno universal best classification method for any domain,any season or any parameter.

The adaptation of the weather type scheme for cli-mate model output is a comprehensive, transparent wayto evaluate the control run period (Hulme et al., 1993;van Ulden and van Oldenborgh, 2006; Demuzereet al., 2009). In this study, the meteorological input dataare based on the output from global circulation mod-els (see Section 2.1 and Table 1). The objective weathertype scheme is applied to reanalyses and global climatesimulations to obtain a comparison of atmospheric dataat nearly the same resolution. Also, circulation statisticscan be derived from nested regional climate simulations(van Ulden et al., 2007), but the mean circulation staysvery close to their driving global models in winter butless so in summer. Van Ulden et al. (2007) have sum-marized that the divergences between different globalmodels are larger than the differences between regionaland driving global models.

The classification domain covers Germany and partsof bordering countries in western Central Europe (withbox corners 55 °N 2 °E, 55 °N 18 °E, 45 °N 4 °E, 45 °N16 °E). The grid spacing is not equidistant, having an av-erage grid cell size of 55 km in both zonal and meridonaldirections. The following classification criteria are aver-aged with a coronary weighting scheme (see Fig. 2). Toavoid an inclusion of gridpoints which are located faraway from Germany, zero weights are chosen for these


Table 2: Objective weather types.

Shortcut Advection cyclonality Centres of action in domain

XXA undefined anticyclonic (950 hPa + 500 hPa) high pressure centralNEA north-east anticyclonic (950 hPa + 500 hPa) high pressure north-westSEA south-east anticyclonic (950 hPa + 500 hPa) high pressure north-eastSWA south-west anticyclonic (950 hPa + 500 hPa) high pressure south-eastNWA north-west anticyclonic (950 hPa + 500 hPa) high pressure south-westXXAC undefined anticyclonic (950 hPa) and cyclonic (500 hPa) XXA with upper air troughNEAC north-east anticyclonic (950 hPa) and cyclonic (500 hPa) NEA with upper air troughSEAC south-east anticyclonic (950 hPa) and cyclonic (500 hPa) SEA with upper air troughSWAC south-west anticyclonic (950 hPa) and cyclonic (500 hPa) SWA with upper air troughNWAC north-west anticyclonic (950 hPa) and cyclonic (500 hPa) NWA with upper air troughXXC undefined cyclonic (950 hPa) low pressure centralNEC north-east cyclonic (950 hPa) low pressure south-eastSEC south-east cyclonic (950 hPa) low pressure south-westSWC south-west cyclonic (950 hPa) low pressure north-westNWC north-west cyclonic (950 hPa) low pressure north-east

gridpoints. Central gridpoints have a weighting factorof three (black), adjacent gridpoints are two (light greyshaded) and the surrounding gridpoints are one (darkgrey shaded). With this weighting scheme, the inten-tion is an improved classification of derived synopticweather patterns specifically for Germany and border-ing areas. Bissolli and Dittmann (2001) underline thesynoptic scale of the classification and separate it fromplanetary classifications like Hess/Brezowsky’s ’Gross-wetterlagen’ (Werner and Gerstengarbe, 2011).

The classification method used in this study assignsthe daily atmospheric circulation to generic criteria.Three criteria are combined into 15 weather types (basedon a grouping of 40 originally defined types in Bissolliand Dittmann 2001). The first classification criterionis the large-scale flow direction (zonal and meridionalwind components at the 700 hPa pressure level) andthus identifies the origin and advection of air masses.The spatial distribution of climate variables is depen-dent on the sources of advected air masses and ontheir warm/cold and dry/moist thermodynamic proper-ties. For the second and third criteria, the vorticity iscalculated at the lower and middle troposphere (950 and500 hPa geopotential respectively) to provide an indica-tor of mesoscale air motions. The vorticity is derivedfrom the second spatial derivative of geopotential. If thesum of gridpoints is negative, anticyclonic conditionsare given over the classification domain, otherwise cy-clonic structures are given by positive values. The gen-eral circulation is denoted by a spatial extent of high andlow pressure systems. Dynamical processes like liftingand subsidence are induced by mesoscale air motion incyclonic or anticyclonic rotation.

The original scheme includes a humidity parameterwhich is formed by the weighted areal mean of the pre-cipitable water. This index differs between ’wet’ and’dry’ tropopheric conditions in comparison to the ref-erence period 1979–1996. The parameter is stronglytemperature related and in terms of global temperature

changes can be deceptive for interpreting weather typealterations, so the parameter is excluded.

A combination of five advection types (NE - north-east, SE - southeast, SW - southwest, NW - northwestand XX - undefined flow direction [more than 1/3 of gridpoints show different sectors]), three vorticity types (an-ticyclonic [A] and cyclonic [C] near surface, as well asanticyclonic with upper air troughs [AC]) represent the15 weather types (see Table 2). The main flow directionand the basis of anti- or cyclonic mesoscale flow sub-sequently determine the spatial location and distributionof the pressure centres. Anticyclonic structures are oftenassociated with high pressure systems and cyclonic flowwith low pressure influences.

2.3 Temperature and precipitation data sets

In this section, a brief description of the employed datasets is given by the explanation of the regionalisedobservation data set HYRAS (Rauthe et al., 2013)and the downscaled simulation data of the ENSEM-BLES Regional Climate Models (van der Linden andMitchell, 2009). Furthermore, derived climate indicesand their uncertainty measurements are explained.

2.3.1 HYRAS gridded reference data

The reference data set HYRAS is based on daily, 5×5 km2 gridded precipitation and temperature fields. Thespatial extent of the domain (see Fig. 3) contains all Ger-man river basins (i.e. Rhine, Danube, Elbe, except Oder)including parts of foreign countries (Netherlands, Bel-gium, Luxembourg, France, Switzerland, Austria, CzechRepublic) and hence the area of investigation covers thewestern part of Central Europe. The HYRAS data en-compass the period from 1951 to 2006 and are based ona network of 6200 precipitation gauges and about 1000temperature stations with the highest station density oc-curring between 1971–2000. A validation is given forprecipitation with procedures like cross-validation and


Figure 3: Sub-catchment basins of the rivers Rhine, Danube, Elbe,Weser, Oder, North- and Baltic Sea.

sampling error estimation to explain seasonal and spatialinterpolation errors and thereby quality and uncertaintymeasurements. The daily temperature data set uses amodified kriging interpolation. In the first step, the lo-cal trend is estimated using multiple linear regression(topographical parameter). During the second step, theresiduals are interpolated with a correlation function totake into account the spatial network configuration andalso the different altitude levels between the stations asis especially important for the Alpine Region.

2.3.2 ENSEMBLES regional climate simulations

For climate impact modelling, meteorological variablesin high spatial resolution are regularly utilized, butthe common global climate simulations (GCM) provideonly coarse resolution and low variability of spatial pre-cipitation statistics (Solomon et al., 2007). Therefore,in this study, nine nested regional climate model simula-tions (RCM) combined with post-processing (i.e. down-scaling from 25× 25 km2 to 5× 5 km2 reference reso-lution) are applied. These are indispensable implemen-tations needed to bridge the gap between global cli-mate simulation output and the small-scale informa-tion required for the analysis of extremes. These cli-mate simulations are split into two 30-year time frames:the control period 1971–2000 and projection period2070–2099. The far-future period is used to minimizemodel-dependent uncertainties and to increase the rel-evance of the greenhouse gas emission scenario A1B(Hawkins and Sutton, 2009). Here, a non-weightedmulti-model mean and the inter-model standard devia-tion were calculated to estimate model uncertainty androbustness of the climate signal (see Section 2.3.3).

The common A1B-scenario was used with variousGCM/RCM combinations and different GCM runs toprovide uncertainty information in our results (see Ta-ble 3). The uncertainty due to the GCM boundary con-

ditions is generally larger than that due to the nestedRCM (Déqué et al., 2007). Our choice of pairings isbased on the available simulations which can providethe parameters needed to use the weather type approachand the RCM precipitation and temperature data. TheseGCM/RCM combinations define our sampling strategyand the design of the climate model ensemble.

2.3.3 Climate Indices

The observation data set HYRAS and the output fromnine regional climate simulations are used to derivecommon climate indices for precipitation and temper-ature which are summarized in Table 4. Climate indicesare based on statistical (e.g. mean or quantiles) and non-parametric elements (e.g. threshold exceedance). Here,these climate indices are investigated with respect toweather types. This allows for the evaluation of the in-fluence and stationarity of each weather type on extremeevents in the past and future climates. For the observa-tion data HYRAS, time series of weather types are ob-tained from two reanalysis data sets. Analogous for eachregional climate simulation, derived weather types arebased on the respective driving global climate simula-tion (see Table 3).

As a first step, the data was aggregated into 25river basin catchments with areas of 10,000 to about45,000 km2 (see Fig. 3). This upscaling to catchmentbasins allows an enhanced spatial representation of pre-cipitation parameters and reduces misinterpretation ofsmall-scale statistics. This also partitiones the entire in-vestigation area into more climatological regions (i.e.Alps, high- and lowlands). For each catchment basin,precipitation and temperature time series are derivedon a daily basis from observations and regional climatesimulations and analysed separately in the winter (DJF)and summer (JJA) seasons.

Accordingly, to evaluate the control period and to ex-amine future climate changes, these indices are opento interpretation. Values for control run biases, futureclimate changes and intra-ensemble standard deviationsare added to each climate index (see Table 4). Next,this evaluation is divided into control (con) and scenarioperiods (sce) and the resulting values are summarizedacross all weather types. To provide uncertainty infor-mation of the climate change signal (change_sce) givenby the ensembles of global and regional climate models,all indices are marked with the mean bias (bias_con)and the mean absolute error (mae_con) between ensem-ble mean and the observation/reanalyses mean. The en-semble uncertainty is represented by the intra-ensemblestandard deviation within the control run (sdev_con) andthe future scenario period (sdev_sce), thereby separatingthe winter and summer seasons. These values are contin-uously emphasized during the following analysis.

3 Weather types in recent climate

The climatic features of weather types can be assessedby the analysis of the past climate (1971–2000). In the


Table 3: ENSEMBLES regional climate simulations included in this study.

RCM Institute Driving GCM

RCA3 Swedish Meteorological and Hydrological Institute (SMHI) BCM2r1, ECHAM5r3HIRHAM5 Danish Meteorological Institute (DMI) BCM2r1, ECHAM5r3CLM2.4.11 ETH Zürich ECHAM5r1, ECHAM5r2RegCM3 International Centre for Theoretical Physics (ITCP) ECHAM5r3RACMO2 Royal Dutch Meteorological Institute (KNMI) ECHAM5r3REMO5.7 Max Planck Institute for Meteorology (MPI-M) ECHAM5r3

Table 4: Future climate signals, control run evaluation and uncertainty measures for the ensemble GCM circulation occurrence and theensemble RCM climate indices for winter (left) and summer (right); listed are the climate change signals (change_sce), the intra-ensemblestandard deviation (sdev_sce) for the scenario period 2070–2099 and the bias (bias_con), the mean absolute error (mae_con) and the intra-ensemble standard deviation (sdev_con) for the control period 1971–2000.

scenario period control period

index change_sce sdev_sce bias_con mae_con sdev_con

frequency of weather type occurrence [%] 1.0 / 0.4 1.3 / 1.6 1.1 / 0.4mean temperature [ °C] +3.0 / +2.5 0.7 / 1.0 +0.7 / -0.2 0.8 / 0.6 0.8 / 1.0mean precipitation [mm] +0.3 / -0.0 0.5 / 0.7 +0.8 / +0.3 0.9 / 0.7 0.5 / 0.6contribution of weather type to hottest days (90th quantile) [%] 2.3 / 2.5 2.1 / 2.8 2.2 / 2.3days with at least +10 °C (DJF) / +25 °C (JJA) basin temperature +3.9 / +8.2 2.6 / 6.9 -0.5 / +1.2 0.7 / 1.2 0.5 / 2.5contribution of weather type to coldest days (10th quantile) [%] 2.4 / 2.0 2.6 / 2.2 2.4 / 1.9days with at most -5 °C (DJF) / +10 °C (JJA) basin temperature -14.0 / -6.3 1.6 / 0.9 -6.8 / -3.8 7.3 / 4.4 6.4 / 4.1contribution of weather type to wettest days (90th quantile) [%] 1.5 / 1.4 1.1 / 1.3 1.6 / 1.6days with at least 10 mm (DJF) / 20 mm (JJA) mean daily basin precipitation +3.9 / +0.5 3.8 / 1.7 +3.4 / +0.1 3.8 / 0.9 3.3 / 1.8contribution of weather type to driest days (10th quantile) [%] 2.7 / 2.2 3.2 / 3.0 2.7 / 2.2days with no basin precipitation -1.1 / +8.2 4.6 / 12.3 +1.1 / -1.9 3.9 / 6.0 4.6 / 7.9

following section, the detected weather types are char-acterized by their occurrence frequency and their rela-tionship to temperature and precipitation (Section 3.1).Climate simulations are then validated for the same timeperiod (control run). GCM-simulated weather type oc-currence frequencies are compared with reanalyses inSection 3.2. Both temperature and precipitation valuessimulated by the regional models are evaluated depen-dent on weather type with the HYRAS observations inSection 3.3.

3.1 Weather types and their precipitation andtemperature patterns

For each weather type, mean values of precipitationand temperature were derived for winter and summerseasons on the basis of the NCEP/NCAR reanalysisdata and HYRAS observations (see Figs 4 to 7). Bothreanalyses are merged into a 30-year climatologicalmean (1971–2000), so natural variability in the Euro-pean weather patterns can be reduced (van Ulden andvan Oldenborgh, 2006). Different synoptic structuresand their precipitation and temperature distributions arepresented to show spatial and quantitative properties. Toget an impression of mean flow characteristics, meansea level isobars are included in the figures. The rela-tive frequencies of the weather types for the two reanal-ysis products are presented in Fig. 8. In the following in-terpretation, the focus lies on frequent weather patternsbased on NCEP/NCAR reanalysis data.

From 1971–2000, prevailing westerlies carried mostlywet, maritime air masses from the North Atlantic toCentral Europe. Mild, rainy winters and relatively cool,rainy summers are the result of frequent seasonal west-erlies. In Central Europe, westerlies occured with a rel-ative frequency of 68 % in winter (29 % SW and 39 %NW) and 72 % in summer (35 % SW and 37 % NW).Among these westerly types, anticyclonic flow (SWAand NWA) occurs about 30 % of the time both in sum-mer and in winter. High and low pressure influences arecharacterized by anticyclonic (DJF: 46 % JJA: 35 %) andcyclonic rotation (DJF: 43 % JJA: 31 %), respectively.

Continentally influenced easterlies lead to cold, drywinters and hot summers. The frequency of easterlies islower than westerlies with an occurrence of about 15 %in winter and 10 % in summer. North-easterlies (DJF:9 % JJA: 7 %) occur more often than south-easterlies(DJF: 7 % JJA: 3 %).

In winter, westerlies are primarily responsible for wetconditions in Central Europe with much higher precip-itation amounts than the other types. In particular witha cyclonic character near the surface (C) or in the mid-troposphere (AC), an intensified daily precipitation rateis distinctly visible. The westerly precipitation regimeis governed by moisture transport from the North At-lantic and is additionally induced by geographical barri-ers (i.e. coastal or orographic effects). The resulting pre-cipitation patterns depend mostly on the directional airflow (i.e. windward rain). Also associated with strength-


Figure 4: Mean daily precipitation amount (HYRAS) and mean sea level pressure field (NCEP/NCAR) for weather types in winter (1971–2000); Anticyclonic types [A] (upper row), upper air trough [AC] (middle row) and cyclonic types [C] (bottom row) with undefined flow[XX] (left), north-east [NE] (second left), south-east [SE] (middle), south-west [SW] (second right) and north-west [NW] (right).

ened westerly flow are positive temperature anomaliesin the form of mild winter conditions, most notably thesouth-westerly types which are accompanied by warmair masses advected from parts of southern Europe.

Easterlies und undefined flows align with dry andcold conditions during the winter months. With their as-sociated high pressure systems, these types are charac-terized by meridional flow which cause the advectionof polar air masses, lower cloudiness and night cool-ing (i.e. undefined anticyclonic XXA, 8 %; mostly as-sociated with central high pressure systems). Southernand eastern parts of Central Europe are most affected bynorth-east flow with very low temperatures. In general,troughs in the upper air convey extra cold conditions.

Mean temperature differences between easterly andwesterly flows reach 4 to 6 °C (Fig. 9). For precipita-tion, pronounced differences can be seen between east-erly and westerly flows in Figs 4 and 10: easterlieshave a mean value of about 0.5 to 1.0 mm, whereas thecyclonic-shaped westerlies (like AC and C) show meanvalues of 3 to 4 mm over the investigation area. Evenhigher values of 6 to 12 mm are accompained by wester-lies in southern mountain ranges (i.e. Black Forest, Vo-gesen) and in the Alpine Region. Most frequent types arecyclonic south-westerly (SWC, 15 %; mostly coupled

with low pressure systems over the North-Sea or BritishIsles) and cyclonic north-westerly (NWC, 4–9 %; Balticlow pressure systems).

In summer, anticyclonic structures and all south-easterly types are associated with warm/hot mean dailytemperatures, while AC formations (a trough in the mid-troposphere at 500 hPa) steadily shift cool air massesto western Central Europe (Fig. 7, i.e. NWAC, 14 %and NEAC, 2 %). In general, westerlies with an upperair trough occur more often in summer than in winter.The difference between mean daily temperature in hotand cold circulation lies during the summer months in arange of 19 °C for the SEA type (1 %) with a blockingsituation and 13 °C for the north-westerly with an upperair trough (NWAC) with a strong cold air advection.

For summer precipitation, high (low) amounts canbe found for (anti-) cyclonic types (Fig. 5). Weathertypes which correspond to large-area dryness are XXA(8 %), NEA (4 %), SEA (1 %) and NWA (13 %). Ex-tensive spatial precipitation is promoted by all south-westerlies, undefined cyclonic (XXC, 7 %; mostly as-sociated with central low pressure systems), SEC (3 %)and NWC (5 %). In general, the formation of summerprecipitation is stronger/less influenced by small/largescale processes (e.g. cooling within local convection or


Figure 5: Same as Fig. 4 but in summer (1971–2000).

the moist saturation of air). Nevertheless, atmosphericmesoscale structures (herein cyclonality) are an indica-tor for the meteorological conditions for reduced (or en-hanced) precipitation development in summer.

3.2 Circulation climatology in reanalyses andglobal climate models

General circulation models reproduce large-scale weatherpatterns with varied quality (van Ulden and van Old-enborgh, 2006; Demuzere et al., 2009). These anal-yses reveal some models with seasonal, spatial andquantitative deficiencies in reproducing the climatol-ogy of typical atmospheric fields. The long-term com-parison of the occurrence of weather types betweenthe NCEP/NCAR and ERA-40 reanalyses reveals amean absolute error (MAE) of 0.8 % in winter and0.9 % in summer. The same evaluation between ERA-40 (NCEP/NCAR) and the ensemble mean of global cli-mate models shows a higher MAE of 1.5 % (1.2 %) inwinter and 1.9 % (1.4 %) in summer. This means un-certainties between the two reanalysis products have aslightly lower magnitude than the comparison betweenthe ensemble mean and the mean of both reanalyses.

In general, circulation models represent the atmo-spheric state differently and the model grid size/resolution

can influence the weather types (Jacob et al., 2007; De-muzere et al., 2009). To summarize systematic differ-ences, the ERA-40 reanalysis produces more undefinedtypes (XX), less anticyclonic and more cyclonic types(C and AC) than NCEP/NCAR. The climate model en-semble mean appears with less anticyclonic and moreAC types. Cyclonic types are particular overestimatedin winter and underestimated in summer.

An overestimated pressure gradient between northand south Western Europe was shown for some globalclimate models in preceeding studies (Demuzere et al.,2009; Donat et al., 2010), while the positions of theleading high and low pressure systems match quite well(van Ulden and van Oldenborgh, 2006). Here, theensemble mean systematically under- and overestimateseasterlies and westerlies, respectively (see Fig. 8). How-ever, the comparison between reanalyses and the en-semble mean shows a good agreement in representingthe primary circulation background in Central Europewith an overestimation of westerlies (especially in sum-mer with less easterlies) and cyclonic structures (see ACand C).

Considering these circulation biases, the global cli-mate models can influence the precipitation and tem-perature statistics which are simulated by the nested re-gional climate models. More insight into the origin ofprecipitation and temperature biases can be obtained by


Figure 6: Same as Fig. 4 but for mean daily temperature.

examining the weather type dependent analysis of re-gional simulations in the next section.

3.3 Regional climate control run biases

Next to the circulation biases in global climate models,it was investigated how temperature and precipitationin regional climate models compare to the referencedata. In Table 4, mean bias (bias_con), mean absoluteerror (mae_con) and intra-ensemble standard deviations(sdev_con) are displayed for each climate index (controlrun period, 1971–2000). Additionally, these errors areinterpreted as weather type dependent for winter andsummer (Figs 9 and 10).

During winter, all weather types apart from NWAshow warm temperature biases, in particular the coldeasterlies (i.e. NEC +1.9 °C, SEC +1.4 °C). The en-semble uncertainty is reflected by the intra-ensemblestandard deviation (sdev_con ±0.8 °C) and a mean tem-perature bias (+0.7 °C). Small biases (up to +0.5 °C)are revealed for frequent types like all anticyclonic pat-terns and westerlies. Exceptions are central low pres-sure systems (XXC +2.1 °C) and other undefined flows(XXAC +1.5 °C). The occurrences of high/low basintemperatures (above 10 °C/below −5◦C) are both un-derestimated by the regional model ensemble (Figs 11and 13). Interestingly, no clear tendencies of the temper-ature biases are detectable during the summer months.

Here, temperatures can be both slightly under- andoverestimated and higher differences are found for in-frequent types. Compared to winter, the mean inter-model standard deviation has a slightly higher magni-tude (±1.0 °C). The dominant westerly circulation typesshow only a mean bias of +0.1 °C, so the simulatedmean summer temperatures agree quite well with thereference. But hot conditions (days with a mean tem-perature of any basin above 25 °C) are overestimated,while cold days (lower than 10 °C) are strongly under-estimated for each weather type (Fig. 11 and 13).

The weather type dependent precipitation statisticcreated by the nine regional climate models shows largediscrepancies between the observed values (Fig. 10).Mean precipitation rates are mostly overestimated, inwinter (doubled amounts) and in some types in sum-mer. Almost all easterlies show large wet biases (+0.5to +1.6 mm) compared to the drier reference data. Nev-ertheless, the occurrence of no basin precipitation eventsare simulated accurately in both seasons (Fig. 18). Dayswith intense basin precipitation (seasonal thresholdsof 10 mm/20 mm) are overestimated in winter and re-produced quite well in summer (Fig. 15). For non-precipitation as well as for extreme precipitation events,it is remarked that the reference data are based on inter-polated station values and not every precipitation eventis detectable due to the location of the precipitationgauges.


Figure 7: Same as Fig. 6 but in summer (1971–2000).

These results show that regional model biases dependon the specific circulation, differ between summer andwinter seasons, and play an important role for an inter-pretation of climate change as shown in the followingsection.

4 Weather types in a changing climate

In this sections, changes in weather types are derivedfrom the global climate model ensemble to detect futureshifts in the Central European circulation regime. Theresulting temperature and precipitation statistics are thenshown for each weather type to interpret their potentialmodifications as simulated by nine regional climate sim-ulations. Finally, the simulated occurrence of future ex-treme events is related to the physical atmospheric con-ditions in the form of objective weather types.

4.1 Changes in the weather type occurrence

In terms of climate change, displacements in the circu-lation system can have a large influence on the resultingweather and climate. For the control period (1971–2000)and the far future period (2070–2099), the relative win-ter and summer occurrences of each objective weathertype are shown in Fig. 8 for the ensemble mean of theglobal climate simulations.

For the climate change scenario A1B, the ensemblemean exhibits an increased frequency of westerly typesat the end of the 21st century. In winter, an increas-ing tendency can be found for all warm south-westerlytypes, while easterlies and undefined flows show aslight decreasing trend (relative change of −18 % NE,−10 % SE and −15 % XX). Warmer temperatures andincreasing precipitation are expected for the wintermonths due to enhanced south-west advection and re-duced cold easterlies. The GCM induced uncertainties(Table 4) in future weather type occurrences are about1.0 % in the mean. Frequent weather types have largerspreads (see NWA 1.3 %, SWC 2.9 %). These spreadsbetween different ensemble members show, in manycases, higher magnitudes as the detected winter changesfrom the past to the future climate.

In summer, a similar behaviour can be recognized:increasing anticyclonic westerlies and decreasing south-easterlies and undefined flows (XX). Remarkably, mostof the summer wet cyclonic patterns (i.e. SWC, NWC,XXC) and upper air troughs (i.e. SWAC) show a reducedfrequency (except NEC and NWAC). These circulationtrends imply decreasing precipitation amounts. The cli-mate signal is more robust in summer than in winteras can be seen from the inter-model standard deviation(sdev_sce), which is 0.4 % in summer and 1 % in winter(Table 4).


Figure 8: Occurrence of weather types in winter (top) and summer (bottom) derived from NCEP/NCAR (black) and ERA-40 (grey)reanalysis data for the time period (1971–2000) and global climate model ensemble mean for the control period (1971–2000, cyan) andfor the future period (2070-2099, red). Inter-model standard deviation between the members are shown by uncertainty bars.

Figure 9: Mean daily temperature for each weather types in winter (top) and summer (bottom) derived from NCEP/NCAR (black) andERA-40 (grey) reanalysis data in combination with the observation data set HYRAS for the time period (1971–2000) and regional climatemodel ensemble mean for the control period (1971–2000, cyan) and for the future period (2070–2099, red). Inter-model standard deviationbetween the members are shown by uncertainty bars.


Figure 10: Same as Fig. 9 but for mean daily precipitation for each weather types.

Figure 11: Same as Fig. 9 but for days of at least +10 °C (DJF) and +25 °C (JJA) mean daily basin temperature.


Figure 12: Same as Fig. 9 but for the contribution to the warmest (DJF) / hottest (JJA) days [90th quantile].

Figure 13: Same as Fig. 9 but for days lower than -5 °C (DJF) and +10 °C (JJA) mean daily basin temperature.


Figure 14: Same as Fig. 9 but for the contribution to the coldest days [10th quantile].

Figure 15: Same as Fig. 9 but for days with at least 10mm (DJF) and 20mm (JJA) mean basin precipitation.


Figure 16: Same as Fig. 9 but for the contribution to the wettest days [90th quantile].

Figure 17: Same as Fig. 9 but for the contribution to the driest days [10th quantile].


Figure 18: Same as Fig. 9 but for days with no basin precipitation.

Additionally to the changes in the atmospheric circu-lation, climate indices are assessed for future weathertypes in the next section to summarize the resultingchanges in precipitation and temperature.

4.2 Weather Types and Future MeanTemperature and Precipitation

Mean daily temperature and precipitation for differentweather types are calculated for the control period (re-analyses and ensemble mean) and the far-future period(ensemble mean only) (Figs 9 and 10).

For the future winter period (2070–2099), the en-semble mean temperature change is about +3 °C andall weather types change to warmer conditions with arange of +2.4 (NEC) to +4.3 °C (SEAC). Much lowerthan these climate signals is the mean inter-model stan-dard deviation which is ±0.7 °C in the control as wellas in the future scenario period. Only two weather types(NEC and NEAC) still show mean daily temperaturesbelow 0 °C compared with ten weather types during thecontrol run. Large positive temperatures can be foundfor south-westerly types (SWA 5.4 °C, SWAC 4.7 °C,SWC 5.0 °C) which show increasing occurrences for thefar-future (+5% of all days). As a result, the future oc-currences of frost and ice days are less certain because ofthe increase in simulated probability for mild, wet win-ter days.

In the future summer period, the mean change is+2.5 °C and nearly all colder weather types (XXAC,NEAC or NWAC) are subject to the same temperature

increase. During the summer season, the control runtemperature bias is lower than in winter (with −0.2 °Cin mean), but no clear tendency is detectable as weathertype temperatures are partly over- and underestimatedby the ensemble mean. A mean daily temperature of20 °C is exceeded during four types (XXA, SEA, SWA,SEC). The probability of hot days with tropical nights isusually very high under these circulation conditions.

In the control period 1971–2000, the mean winterprecipitation shows a strong overestimation in nearlyall weather types. The normally dry easterly types arenotably affected. The precipitation bias in the controlperiod is as high as the precipitation change betweenthe future and the control period. Nevertheless, a state-ment can be made for the future period: in 13 of 15weather types, the mean precipitation amounts increaseby 3–25 %. The inter-model standard deviation has amean magnitude of 12 % comparable to the precipitationchange. Precipitation changes are about +25 % for allsouth-westerly types. The regional climate models showincreasing winter precipitation amounts in Central Eu-rope which could be due to potential circulation changesas well as to attributed within-type changes.

Mean summer precipitation can be characterized bysmaller changes that are mostly lower than the ensembleuncertainties. Simulated future mean precipitation de-creases in most frequent weather types (NWA −21%,SWA −15%, NWAC −10%, SWAC −6%). Simulateddecreasing summer precipitation amounts can also beexplained by variations in the weather types with lesscyclonic (−5% in relation to all days) and more anti-


cyclonic structures (+5%). The anticyclonic flow pro-motes more subsidence of the air masses combinedwith less development of clouds and precipitation. Thejoint effect of the increasing occurrence of anticyclonicweather types with decreasing mean precipitation canlead to drier summer conditions.

4.3 Weather types and associated extremeevents

Threshold- and quantile-based extreme indices were de-rived from the HYRAS observation and ENSEMBLESregional climate simulation data sets. This analysis re-veals which weather type contributes to extreme temper-ature and precipitation events and how these relationschange in the far future period. Particularly for sim-ulated extremes, the uncertainty information (climatemodel ensemble standard deviation) in Table 4 shouldbe borne in mind.

Changes in high temperatures The number of dayswith mean basin temperatures of at least 10 °C in win-ter and above 25 °C in summer are shown in Fig. 11. Inboth seasons, major increases are noticeable with about+60 mild days in winter and +125 hot days in sum-mer (in 30 seasons). Main weather type contributorsduring the winter months are SWA (+21 ± 11 days),NWA (+13± 7 days) and SWC (+13± 10 days). Mildwinter days are underestimated by −0.5 days duringthe control period. The far future can be characterizedwith a ensemble mean change of +4 days and an un-certainty of ±2.6 days. In summer, mostly the sameweather types are responsible: SWA (+50± 32 days),NWA (+27±26 days), SWC (+22±15 days) and XXA(+10±10 days). An overestimation of hot days in sum-mer (+1.2 days, control run) and a model spread of±6.9 days are the ensemble uncertainty compared to theensemble mean climate signal of +8.2 days between thefuture and control climates. Most of the models showan increase in both seasons, but some members suggestsome combination of minor and major increases of verywarm/hot days.

The warmest winter days (above the 90th quan-tile, Fig. 12) are mainly associated with anticyclonicwesterlies (52 %) and cyclonic south-westerlies (25 %).Changes in the far future (2070–2099) are mostly re-lated to all south-westerly types which show an increas-ing contribution (+10 %) to the highest daily tempera-tures (with a decreasing influence of anticyclonic north-westerlies −8%). As described in Section 4.1, an in-creasing frequency of the south-westerly types is sim-ulated by the global climate model ensemble. In futuresummers, anticyclonic south- and north-westerlies (withchanges of SWA +9%, NWA +1%), Central Europeanhigh pressure systems (XXA,−4%) and cyclonic south-westerlies (SWC, −1%) will still be major circulationdrivers of warm events. Except for SWA and NWA, thecontribution of the other weather types decreases. As

well as in winter, the increasing occurrence of SWA pre-sumably constitutes more hot days.

Changes in low temperatures All weather typeswhich show cold temperature conditions can also leadto extreme cold days. The number of extreme cold days(Fig. 13) is defined by a mean basin temperature lowerthan −5 °C (in winter) and +10 °C (in summer). Bydefinition, extreme cold days occur about 50 times inthe future winter and 20 times in the future summerinstead of 260 times and 115 times, respectively, underpast climate conditions (in 30 winter/summer seasons).The regional climate simulations tend to underestimatevery low temperature occurrences, particularly in winterwith a mean bias of −6.8 days per weather type. Weatherpatterns which are often associated with cold conditionsshow the largest decreases: NWAC (−29 days), NWA(−26 days), NEAC (−23 days) in winter and NWAC(−33 days), NWC (−15 days), NWA (−10 days) insummer.

The coldest days (below the 10th quantile; Fig. 14)seem to be randomly distributed within the differentweather classes in winter. Large amounts of the cold-est days are captured by weather types with an upperair trough and easterly flow while significantly fewerare bounded by southern types. In the future period,westerlies are marked by a decreasing influence. More-over, an increased occurrence of undefined flows is de-tectable. During summer, a dominance of about 40 % ofall coldest days are associated with the NWAC type, fol-lowed by NWC (15 %), SWC and SWAC (both 10 %). Inthe simulated far-future period, the contribution of theNWAC rises slightly and cyclonic structures have lessinfluence on the coldest days. In both seasons, the intra-ensemble spread is larger than most of the future climatesignals, so the circulation induced influence on cold sit-uations is rather difficult to interpret and global temper-ature changes may have larger effects.

Changes in high precipitation events High precip-itation events are defined by a threshold exceedance of10 mm (winter) and 20 mm (summer) daily basin meanprecipitation (Fig. 15). For the past climate, the fre-quencies of these rare events are simulated with about150 days in winter and 80 days in summer (in 30 sea-sons). The simulated future shows large changes in win-ter with +60 days (+40 %) but no significant changein summer. In winter, increasing south-westerlies ini-tiate about 65 % of these events (SWC about 30 %).Cyclonic-shaped north-westerly (+11 days) and cen-tral low pressure systems (+6 days) are also impor-tant contributors. These high precipitation events in-crease remarkably in most of all westerly flows (exceptof NWA). Regional climate models overestimate theseevents with +50 days in winter (control run). Instead,summer events are simulated accurately.

The 90th quantile wettest days (Fig. 16) are chieflynorth-westerlies (54 %) and the cyclonic south-westerlies(22 %) in winter. The RCM representation of the wettest


days is quite good in comparison to the other quan-tile indices (see mae_con in Table 4). Under climatechange, south-westerlies become increasingly responsi-ble for the wettest days (an increase from 31 to 48 %,while NW types show a decrease from 54 to 45 %).Thereby, the inter-model standard deviations for thesevery wet types are about ±4% in the control and fu-ture period and even higher than in other types. Duringthe summer months, cyclonic south-westerly flow is theprimary contributor to the wettest days (30 %). On com-paring future and control periods, no significant changesin effective weather types are detectable in summer.

Changes in low and non precipitation events Thedriest days per river catchment (Fig. 17) are the dayswith a mean basin precipitation lower than the 10thquantile of each time series. About three-quarters ofthese days are generated by strong anticyclonic highpressure systems in both seasons in both the recent andthe future climates. For the simulated future, the pre-dominance of anticyclonic westerlies (SWA and NWA)increases both in winter (+7%) and in summer (+5%).Similar weather patterns are responsible for days withno basin precipitation which are presented in Fig. 18.While no obvious winter changes are visible, notableincreases are detected for all concerned dry types inthe summer months. Anticyclonic westerlies contribute+90 days and double their number of no precipitationdays in the future period. Although, the regional modelsdiffer in their simulation of no precipitation events: theintra-ensemble standard deviation of ±12 days is higherthan the climate signal of +8 days (ensemble mean).Maybe this is due to higher simulated frequencies ofdrizzle in some RCMs. Non basin precipitation eventsincrease in 11 of 15 weather types.

5 Summary and conclusion

In this study, we analysed objective weather types andtheir temperature and precipitation regimes for the re-cent (1971–2000) and a future climate (2070–2099)from a multi-model ensemble of global and regionalclimate simulations for the emission scenario A1B.In comparison with two reanalysis products (ERA-40,NCEP/NCAR), the global climate models are capableof reproducing the 30-year circulation climatology quitewell. The differences between the two reanalysis prod-ucts are slightly lower than the differences between theensemble mean of the GCMs and the reanalyses. Theobjective weather type scheme is an appropriate methodfor an automated, reproducible and transparent evalu-ation to detect changes and differences in circulationstatistics.

Weather pattern dependent precipitation and temper-ature indices are derived from the gridded observationdata set HYRAS, as well as from nine ENSEMBLESregional climate runs for the control period 1971–2000.The evaluation results often show a good coincidence of

the simulated climate indices. For mean winter temper-ature, the RCM ensemble mean and HYRAS are gener-ally in good agreement (Fig. 9), but with a mean warmbias (+0.7 °C) which is stronger during cold easterlyflows. In summer, the temperature bias is comparativelysmall (−0.2 °C) and all weather types with an upperair trough show a warm bias. In the control period, themean precipitation amounts are overestimated in nearlyall weather types in both seasons. In winter, dry weatherpatterns (easterlies and undefined flows) often show aclear overestimation of the mean precipitation (for a fewtypes more than twice the HYRAS amounts). Althoughthe representation of summer precipitation processes issimplified in most regional climate models, the here-presented bias values of areal precipitation are lowerthan in the winter season.

With respect to these evaluation scores, weather typedependent changes of climate indices were analysed.Future weather type occurrence and their temperatureand precipitation regimes induce large potential changesprojected by the climate model ensemble. An increasingwarm air advection during winter is given by increasedfrequencies of all south-west weather types, whereascolder easterly circulations decrease in frequency. To-gether with the temperature increase in all types, verymild winters are expected for the future. Very cold days(mean daily temperature of a catchment basin is lowerthan −5 °C) are greatly reduced in the future winter sea-son, while warm days (greater than 10 °C) during west-erly flows increases significantly (+55 days in the 30-year future period). Winter weather types are affectedby large modifications of mean and extreme precipita-tion parameters. In nearly all weather types, mean dailyprecipitation amounts considerably increase by 3–25 %.In most cases, the wettest days are a contribution of allnorth-westerlies and cyclonic south-westerlies. With aview on the future period, days with at least 10 mm dailybasin precipitation become more frequent (+60 days),mostly during south-westerly flows (+38 days).

Major circulation changes in the future summermonths are an increasing frequency of dry anticyclonicnorth-westerlies and a decrease in mostly wet, strong cy-clonic weather types. Hence, the influence of low pres-sure systems will be reduced, while high pressure sys-tems will become more common. The occurrence ofsummer days with no basin precipitation seem to under-lie large changes with +122 days (+50 %) in the futureperiod. A key contributor is anticyclonic westerly circu-lations with an additional 90 days. Given this displace-ment of the pressure centres, the coincident result mod-elled by the regional climate ensemble is a slightly re-duced mean precipitation. In the future period, the meanprecipitation of each weather type is slightly reduced inall patterns, except during cyclonic flow with marginallyhigher mean amounts. Noteworthy is the wide-spreadinter-model standard deviation which implies greateruncertainties than simulated climate signals in summer.Simulated precipitation extremes (here with a minimumof 20 mm daily basin precipitation) seem to be unaf-


fected, too. In the control as well as the future period,only a few weather types (like cyclonic south-westerlywith approximately 30 %) are responsible for the high-est basin precipitation.

All these results imply that trends in weather typesindicate major changes in western Central Europe dur-ing winter and summer such as increasing temperature.In winter, the possible temperature changes are mainlyan interaction of circulation trends and mean warming.Greater circulation induced changes are presented formean precipitation and extensive rainfall events in fu-ture winters. In summer, changes in non precipitationevents in combination with more frequent temperatureextremes are of substantial importance in western Cen-tral Europe.

Acknowledgements

This study was made possible by the KLIWAS researchprogramme, which is funded by the German FederalMinistry of Transport, Building and Urban Develop-ment (BMVBS). The generation of the HYRAS data setand the calculation of the weather type data set werefunded by the Federal Institute of Hydrology (BfG). Re-gional Climate Model data were provided by the EUProject ENSEMBLES. The authors are also grateful tothe three anonymous reviewers for their comments andsuggestions that helped to improve the quality of themanuscript.

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