Within the EU project ALP-IMP a high-resolution regional simulation driven by the ERA40 reanalysis has been performed for the Greater Alpine Region (GAR) for the period 1958 to the present. A comparison of the high-resolution simulation and the ERA40 reanalysis regridded to 1 deg resolution with four different monthly mean temperature datasets for the GAR shows for both very high correlations of around 0.9, and in general slightly higher correlations for the regional simulation. Correlations of the regional simulation and the reanalysis with observations increase with spacial scale. The separation of the GAR into six subregions identifies the Po plain as a region where the high-resolution simulation as well as ERA40 have problems in reproducing the instrumental measurements

Beate Müller

Julie Jones

Kerstin Prömmel

Martin Widmann

English

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COMPARISON OF A. HIGH.N"ESOI,UTIONREGIONAL SIMULATION AND THE ERA4O RE,A.NALYSISOVER THE ALPINE REGIONKerstin Prtimmel, Beate Miiller, Marlin Widmann, Julie JonesGKSS Research Centre, Max-Planck-Str. 1, 2150? Geesthacht, GermanyE-mail: kerstin.proemmel@gkss.deAbstract: Withia the EU project ALP-IMP a high-resolution regional simrlation driven by the ERA40reaaalysis has been performed for the Greater Alpine Regioa (GAR) for the period 1958 to the prese::t.A comparison of the high-resolution simulation a,:rd the ERA40 reanalysis regridded to 1 deg resolutionwith four di$erent mor:thly mean temperature datasets for the GAR shows for both very high correlationsof around 0.9, and in general slightly higher correlations for the:egional simulaiion. Correlations of theregional simulation and the reanalysis with observations increase with spacial scale. The separation of theGAR into six subregions identifes the Po plain as a region wbere the higl-resolution simulation as ruell asERA40 have problems in reproducing the i*strumental measurements.I(.eywords - ALP-IMP, high-rcsolution regional modelling, HfiMA, ER"A4A reanalysisI". INTRODUCTIONThe numerical simulation of weather and climate over ihe Alpine region is a challenge becauseof the complex topography. To include a realistic iopography in a climate model a very highresolution is needed. The comparison of the skill of a high-resolution simulalion driven by theERA40 reanalysis with the reanalysis itself allows to a,ralyze vrhether the higher resolution leadsto an added value. The high-resolution simulation described in this stady covers the period from1958 to the present with multi-variable hourly output.2. MODEI,The high-resolution regional simulation has been performed with R"EMO (R"Egional Model,Jacob and Podzun, 1997). The dynamical core of H"EMO is based on the numerical weatherprediction model EM of the German Weather Service (DWD) and the parameterisations are takenfrom the ECIIAMA climate model of the Max-Planck-Institule for Meteorology ia Hamburg.The REMO simulation discussed here has a very high horizontal resolution of.116 degree on20 vertical levels in the troposphere and lower stratosphere. It is driven by the approximately1.12 degree resolution ERA40 reanalysis through prescribing the values at the lateral bounda.riesa^nd through forcing the large-scale wind field within the model domain by the spectral nudgingtechnique (von Storch et aJ., 2000).3. COMPARISON WITH INSTIIUMENTAI, DATASETSSoth the REMO simulation and the ERA40 reanalysis (regridded to 1 degree resolution) arecompared with different monthly mean temperature daiasets for the GAR. One is the griddedtemperature dataset of the Climate Research Unit {CRU TS 2.0, Mitchell et ai., 2003) with aresolution of 112 degree. The others are the recently completed HISTALP datasets, which containone station dataset consisting of 131 long temperaiure series back to a maximum of 1760 and two369HISTALP 131 sta:ions0r !2Figure 1: Correlations of monthly mean temperatures between the REMO simulation a.nd theHISTALP statio! dataset and R"EMO a.nd the HISTALP gridded dataset from low-eleration sta-tions for January and July.Table 1: Definilion o{ the subregionsNo. I Definition1 West (Maritime)2 East (Continental)3 South (Adriatic)4 Po Plain5 Uentral Alpine Low Level6 Hieh Level (>1400 m a.s.lgridded temperature a,nomaly datasets of 1 degree resolution derived from the staticn series, onefrom low- (HIS?ALP low) and one from high-elevation stations (HISTAIP high). The comparisoabetween REMO (ERA40) and the three gridded datasets is done for the resolutions of 1/6 deg,If2 deg,1 deg, 2 deg and 3 deg (1/2 deg, 1 deg, 2 deg and 3 deg).The correlation between R^EMO and the datasets and ERA40 and the datasets is ca.lculatedmonthly for the period from 1958 to the present for each gridpoint. As an example for the cor-relation maps obtained, the correlations between REMO and the HISTALP station dataset andR'EMO and HISTALP low ale shown for Janua.ry and July in Fig. 1. To discuss the results thecorrelations are averaged over the whole Alpine area ranging from 3.5 deg to 18.5 deg west arrd42.5 deg to 49.5 deg north and additioaally over six subregions (Tatrle 1) defined by Bdhm et al.(2001).3.1. REMOThe seasonal cycle of the Alpine area mean correlations is nearly identical for all datasets andresolutions a,nd therefore represents a basic property of the R^EMO simulation. ?here are twopeaks in the seasonal cycle, one a,round March and one in September with lalues around 0.95.The two minima occur in August and November with values around 0.88. The comparisoa ofthe correlations of REMO with the diferent datasets shows that the lowest correlations occurbetween REMO a,nd the HISTALP station dataset almost during the whole year and the highestIJISTALP low I deg370tIISTALP highHISTALP lowFigure 2: Monthly mean temperature correlations between REMO and the three gridded datasetsand ERA40 and the datasets for each month for the five different resolutions and the HISTALPstations averaged over the Alpine area. The datasets are the HISTALP dataset from low-elevationstations (HISTAIP low), the HISTALP dataset from higb-elevatior stations (HISTAIP high) andthe CRU dataset (CRU), the resolutions are 1/6 deg, ll2 &eg,1 deg, 2 deg and 3 deg and thestations are those on which the HISTALP low and HISTALP high datasets a,re based, respectively.correlations occur betvreen REMO and HISTALP low. All correlations between R-EMO and thedataseis increase with resolution (Fig. 2).The Alpine area mean bias shows that the R"EMO simulation is too warm during the whole year(not shown). From May to October the bias amounts to more ihan 1 K with the highest values inJuly a:rd August. In winter the bias is a,round 0 K. For all resolutions the bias has similar values.The correlations averaged over the six diferent subregions are lowest for subregion 4, thePr: Plain (F'ig. 3). The highest correlations a,re found either for subregion 1, 2 or 6, i.e. West(Marltime), East (Continental) or High Level.3.2. ERA40For all resolutions and datasets except for the :IISTALP gridded datasel from high-elevationstations the seasonal cycles of the Alpine a,rea mear correlations are simila,r among each other butnot very pronounced. The highest correlations occur in September and from February to May(arouad 0.92), the lowest in August and from October tc January (a.round 0.88). In comparison tothe correlations between ERA40 and the gridded datasets the correlation between ERA40 and theHISTALP station dataset is lowest of all daiasets during the whole year a,nd, as for REMO, thecorrelations increase with resolution (Fig. 2). ?he correlations of the HISTALP gridded datasetsfrom high-elevation stations bave for all resolutions a very clear seasonal cycle with highest valuesfrom March to September und very low values from October to February.?he Alpine a,rea mean bias is negative from April io September vriih the most negative valuesa,round -1.5 K from May to July (not shown). )uring ihe renaiaing yeax the bias is around 0 K.The bias does not difer much for the diferent resolutions.The separation into the six subregions shows that the area mean correlations are lowest forsubregions 4 and 5, the Po Plain and the Central Alpine Low l,evel and from October to Februa,ryfor subregion 6, High Level. The highest correlations occur for the subregions 1 and 2, West3'71REMO1234567891011t20.9035Figure 3: Monthly mean temperature corelations betweea RDMO and the HISTALP stationdataset a,nd ERA40 and the HISTALP station dataset for each month for the six subregionsdefined in Table 1.(Mariiime) and East (Coniinental) (Fig. 3).4. CONC:,USIONSFor all four difierent datrasets (CRU, IIISTALP stations, HISTALP gridded from low- and fromhigh-elevation stations) and all resolutions the R"EMO simulation has slightly higher monthly meantemperature correlations than the ERA40 reanalysis nearly during the whole yea,r. For both RSMOand ERA40 it is evident that the Alpine area mean correlation increases continuously with spatialscale.By sepa,rating the GAR into six subregions the Po plain can be identified as a region of which thetemperature is difficult to reproduce, especially during summer and winter. Io summer, instabilitiesare very frequeat in this region which cause coavection a,nd showers which lead to a cooling. Inwinter, heavy fog occars frequeatly. Those weather phenomena a,re apparently difficult tro simulate,even with a regional model.As future work it is planned to calculate the correlations from daily values and to comparesimulated precipitation wlth observational daiasets in the GAR,REFEn,SNCtrSBdhmn R., I. Auer, M. Brunetti, M. Maugeri, ?. Nanai, w. sch6ner, 2001: Regional rempera-ture Variability in the Ouropean Alps: 1?6&1998 from llomogenized Instrumentat Time Series,Intemational Joumal of Cli,matology, 21, I77g-L801"Jacob, D. a^nd R. Podzun, 1997: Sensitivity Siudies with the Regional Climate Model REMO,Meteorologg anil Atmospheric Physics, 63, 119-129.Mitchell, T.D., T.R. Carter, P.D. Jones, M. Hulme, M. New, 2003: A Comprehensive Set of lligh-resolution Grids of Monthly Climate for Europe and tbe Globe: the Observed Record (1901-2000)arrd 16 Scenarios (2001-2100). Joumal of Climate: submitted.von Storch, H., H. Langenberg, F. Feser, 2000: A Spectral Nudging Technique for DynarnicalDow:rscaling Purposes, M onthly Weather Eea'iew, 128, 3664-3673.312

COMPARISON OF A HIGH-RESOLUTION REGIONAL SIMULATION AND THE ERA40 REANALYSIS OVER THE ALPINE REGION

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