Validation of present-day regional climate simulations over Europe: LAM simulations with observed...

Correspondence to: J. H. ChristensenE-mail: [email protected]

Present affiliation:*Centro Ricerche Energia Casaccia, Rome, Italy

ClimateDynamics© Springer-Verlag 1997

Climate Dynamics (1997) 13: 489—506

Validation of present-day regional climate simulations over Europe:LAM simulations with observed boundary conditionsJ. H. Christensen,1 B. Machenhauer,2 R.G. Jones,3 C. Schar,4 P.M. Ruti,5* M. Castro,6 G. Visconti7

1Danish Meteorological Institute, Copenhagen Ø, Denmark2Max Planck Institute for Meteorology, Hamburg, Germany3Hadley Centre, Bracknell, United Kingdom4Atmospheric Science ETH, Zurich, Switzerland5Regional Meteorological Service of the Emilia Romagna Region, Bologna, Italy6University of Complutense, Madrid, Spain7University of L’Aquila, Coppito, Italy

Received: 11 December 1996 / Accepted: 17 March 1997

Abstract. Nested limited-area modelling is one method ofdown-scaling general circulation model (GCM) climatechange simulations. To give credibility to this method thenested limited-area model (LAM) must be shown to simu-late local present-day climate conditions fairly accurately.Here seven different European limited-area models drivenby observed boundary conditions (operational weatherforecast analyses) are validated against observations, andinter-compared for summer and winter months. Relativelylarge biases are found. In summer large positive surface airtemperature biases are found over southeast Europe. Themain reason is deficiencies in the surface hydrologicalschemes causing an unrealistic drying of the soil. In atleast one of the models, most likely several of them, anadditional factor is an overestimation of incoming solarradiation. Apart from excessive precipitation in moun-tainous areas in some models they generally show a nega-tive bias due to the drying and decreased advection fromthe Atlantic. In winter most models have a positive pre-cipitation bias which seems to be caused by an enhance-ment of advection from the Atlantic and enhanced cycloneactivity. Surface air temperature biases are negative prob-ably due to an underestimation of the incoming longwaveradiation.

1 Introduction

Available computing resources impose strong restrictionson the horizontal and vertical scales resolvable in a gen-eral circulation model (GCM). Presently, coupled atmo-sphere-ocean GCM climate simulations use a rather

coarse grid, minimum horizontal grid lengths are 250 kmover Europe. These are insufficient to provide unambigu-ous prediction of climate variables on a regional scale (seee.g. Robinson and Finkelstein 1991). Recently attempts toovercome these problems have been made using a one-way nesting approach. A limited-area model (LAM) withmuch higher resolution (typically 25—50 km) is run withinitial and boundary conditions taken from a GCM (e.g.Dickinson et al. 1989; Giorgi 1990; Jones et al. 1995). Thisapproach is routinely employed for short-range numericalweather prediction (NWP), but its use for climate model-ling is comparatively new and, therefore, our knowledgeof its performance has so far been rather limited. With thehigher resolution more details of the surface fields (e.g.orography, land-sea distribution) and more detailed struc-tures of weather systems (e.g. sharper fronts) can be repre-sented, which according to experience from NWP do leadto more realistic simulations of the individual weathersystems and the effects of the surface characteristics, oro-graphic precipitation in particular. One way to validatethe nesting technique is to run the LAM in a forecast-likemode with initial and boundary conditions based on ob-servations of the real atmosphere, i.e. operational weatherforecast model analyses. These are referred to as perfectboundary condition experiments. In such experiments weavoid errors in the general circulation that are induced viathe boundary conditions, which should facilitate the de-tection and quantification of biases due to the systematicerrors in the model physics.

The models we describe use European Centre for Me-dium Range Weather Forecasts (ECMWF) analyses ex-cept for the UKMO/Hadley Centre model which usesUKMO analyses. We compare the integrated effect ofsimulated weather over a month with that observed. Werefer to this as actual time verification. The advantage ofthis approach is that it is relatively inexpensive in comput-ing time compared to a validation against climatologicaldata (requiring much longer integration periods) andtherefore allows an inexpensive direct validation of vari-ous LAMs. Simulations of July 1990 and January 1991from month— to year—long integrations (details in Sect. 3)of seven LAMs used in the EU Regionalization project are

Fig. 1. The various model domains utilized in this study and thecommon validation area. 1. HIRHAM3 and HIRHAM4 areas.2. ETH/DWD area. 3. Available part of CLAMBO area.4. PROMES area. 5. MM4 area. 6. RECSIII area. 7. CRU valida-tion area

Table 1. Model simulations

Period Model/Institution Resolution Boundary condition

January 90—August 91 HIRHAM3/DMI 57 km, 19 L ECMWF analysesJanuary 90—December 91 HIRHAM4 / DMI 57 km, 19 L ECMWF re-analysesOctober 89—April 91 CLAMBO / SMR-ER 26 km, 20 L ECMWF analysesAugust 90—August 91 MM4 / UNIVAQ 50 km, 17 L ECMWF analysesMarch 84—February 94 RECSIII / UKMO 50 km, 19 L UKMO analysesJuly 90, January 91 DWD / ETH 57 km, 20 L ECMWF analysesJuly 90, January 91 PROMES / UNICOM 20 km, 30 L ECMWF analyses

presented. The models are HIRHAM3 and HIRHAM4used at the Danish Meteorological Institute (DMI) andthe Max Planck Institute for Meteorology (MPI) in Ger-many, MM4 used at the University of L’Aquila(UNIVAQ) in Italy, CLAMBO from the Emilia-RomagnaRegional Meteorological Institute (SMR-ER), also Italy,PROMES from the University of Complutense (UNI-COM) of Madrid, Spain, UKMO-RCM/RECSIII fromthe Hadley Centre (UKMO), UK, and ETH/DWD usedat Atmospheric Science ETH in Switzerland.

The integration domain of the models are shown inFig. 1. They are different due to a variety of ‘‘domains ofinterests’’ and computational resources but they showa considerable overlap, within which their performancemay be compared. The purpose of this study is, however,not a ‘‘beauty contest’’ among the models. Rather,we want to validate alternative model configurations(including the domain size, the resolution, the lengthof simulation etc.) against observations with the purposeof identifying and quantifying possible systematicerrors. Despite the different configurations we shallsee that significant common systematic errors arerevealed.

A common validation domain is used to which model,analysis and observed data have been interpolated. This istermed the CRU-grid and defined as a 0.5° by 0.5° latit-ude—longitude grid covering the region 35 °N to 75 °N and15 °W to 35 °E (Fig. 1). Screen (2 m) temperature is alsointerpolated to a common orography, defined as the meangrid square orography for the CRU-grid (Fig. 2).

The study continues with a brief description of eachmodel, Sect. 3 contains a detailed description of the ex-periments, whilst Sects. 4 and 5 inter-compare and vali-date the models simulation of surface pressure and near-surface weather elements. The results are then sum-marized and discussed in Sect. 6.

2 Model descriptions

The seven LAMs were used over six regions indicated onFig. 1 with the resolutions listed in the descriptions later(for an overview see Table 1). They are all grid-pointprimitive equation models with orography represented asthe mean height over a grid square. The models differ inall aspects of the numerical solution of adiabatic processesand the parametrization of the physical processes. Theyalso differ in vertical and horizontal resolution. The fol-lowing briefly describes the main model characteristics(more detailed descriptions are provided in the references).

In this we use the following notation: u and v, horizontalwind components; ¹, temperature; q

c, specific humi-

dity; qw, liquid water content; p

s, surface pressure; ',

geopotential height; g the vertical co-ordinate, and gR verti-cal ‘‘velocity’’.

2.1 The HIRHAM3 and HIRHAM4 models

f Prognostic variables—u, v, ¹, qc, q

w, p

s. ' and gR diag-

nosed (Kallen 1996)f Horizontal grid—rotated latitude/longitude co-ordina-

tes: South pole at 25 °S and 0 °E (domain 1, Fig. 1).Resolution 0.51° by 0.51°

f Vertical grid—sigma-pressure hybrid (g), 19 levelsf Finite-difference scheme—centred second-order accu-

racy similar to the former ECMWF grid point model(Burridge and Haseler 1977)

f Time-stepping scheme—leapfrog, semi-implicit, withAsselin time filtering

f Boundary scheme—simplified Davies (1976) relaxationover 10 points. Inflow/outflow dependent scheme forspecific humidity and no relaxation on prognosticcloud water content (Christensen et al. 1996)

490 Christensen et al.: Validation of present-day regional climate simulations over Europe

Fig. 2. Upper left corner: surface orography used for the CRU grid.All other panels shows interpolated orographies for the involvedmodels

Christensen et al.: Validation of present-day regional climate simulations over Europe 491

f Boundary values—linearly interpolated in space andtime from ECMWF analysis with 6-h intervals, at3°]3° resolution in HIRHAM3. ECMWF re-analysisat 1.5°]1.5° resolution in HIRHAM4

f Horizontal diffusion—linear fourth order diffusionalong model surfaces for q

c, ¹, u, v. In HIRHAM4, no

diffusion at points where slope of model surface exceedsa critical value (Christensen et al. 1996)

f Surface—five soil layers with predicted temperature,one layer with predicted soil water content and a sur-face storage containing intercepted water and snow.Vegetation effects and run-off scheme (Dumenil andTodini 1992). In HIRHAM4 field variables for max-imum soil water holding capacity are introduced alongwith other soil characteristic fields, such as heat diffus-ivity and heat capacity of the soil (Claussen et al. 1994;Roeckner et al. 1996). The initial soil moisture is inter-polated from the ECMWF analyses for the first day ofthe integration

Physical parametrizationsHIRHAM3: first order local vertical diffusion, where tur-bulent diffusivity coefficients are formulated in terms oflocal gradients of the mean variables (Louis 1979; Louiset al. 1982)HIRHAM4: 1.5 order local vertical diffusion, with eddydiffusivity expressed in terms of the turbulent kinetic en-ergy (Brinkop and Roekner 1995)HIRHAM3/4: cloudiness and precipitation due to large-scale condensation utilizing liquid water as a prognosticvariable (Sundqvist 1978; Roeckner et al. 1991)HIRHAM3: moist convection parametrized with themass-flux scheme of Tiedtke (1989)HIRHAM4: as HIRHAM3 with modifications to penetra-tive convection from Nordeng (1994)HIRHAM3/4: gravity wave drag formulation (Palmeret al. 1986; Miller et al. 1989)HIRHAM3: radiative processes according to Hense et al.(1982), but with later updates (Rockel et al. 1986; Eicker-ling 1989)HIRHAM4: radiative processes according to Morcrette(1989) with extensions for greenhouse gases (van Dorlandand Morcrette 1997) and improved parametrizationof the water vapour continuum (Giorgetta and Wild1995)

2.2 The MM4 model

f Prognostic variables—u, v, ¹, qc, q

w, p

sf Horizontal grid—Lambert Conformal, resolution 50km by 50 km (domain 5, Fig. 1)

f Vertical grid—terrain following sigma (p) co-ordinate,17 levels

f Finite-difference scheme—centred second-order accu-racy (Anthes et al. 1987)

f Time-stepping scheme—explicit scheme (Brown andCampana 1978)

f Boundary scheme—nudging including a Newtonianand a diffusion term over four points (Anthes et al.1987). Inflow/outflow formulation for humidity (Giorgiet al. 1993)

f Boundary values—linearly interpolated in space andtime from ECMWF analysis with 12-h intervals, T42resolution

f Horizontal diffusion—fourth order diffusion, reduceddiffusion over steep orography (Giorgi et al. 1993)

f Surface—biosphere-atmosphere transfer scheme (BATS,Dickinson et al. 1986). The initial soil moisture is inter-polated from ECMWF analyses from the first day ofthe integration

f Physical parametrizationsBoundary layer scheme from Holtslag and Boville(1993) (non-local diffusion formulation with the inclu-sion of counter-gradient term)Grid-scale precipitation from super-saturated watervaporKuo scheme of Anthes (1977) for convectionFull radiation transfer package from the NationalCenter for Atmospheric Research (NCAR) climatecommunity model version 2 (CCM2)

2.3 The CLAMBO model

f Prognostic variables—u, v, ¹, qc, p

s, cloud liquid and

rain water mixing ratiosf Horizontal grid—rotated latitude/longitude co-ordina-

tes, resolution 0.25 ° by 0.25°. Integration domain Fig.1, domain 3, data domain for validation is somewhatsmaller


f Time-stepping scheme—split-explicit with the adjust-ment processes treated using a modified forward-back-ward time difference scheme. For horizontal advectionthe Janjic (1984) scheme is used with a modifiedEuler-backward time scheme

f Boundary scheme—Davies (1976) relaxation over8 points

f Boundary values—linearly interpolated in space andtime from ECMWF analysis with 6-h intervals, at T106resolution

f Horizontal diffusion—non-linear fourth order diffu-sion (Black 1988)

f Surface—biosphere-atmosphere transfer scheme (BATS,Dickinson et al. 1986). The initial soil moisture is inter-polated from ECMWF analyses from the first day ofthe integration

f Physical parametrizationsPBL and surface layer (II order turbulence closurescheme)

f Large-scale and convective precipitation parametriz-ation; Betts and Miller scheme (Betts 1986)

f Radiative processes according to Ritter and Geleyn(1992)

2.4 The PROMES model

f Prognostic variables—u, v, ¹, qc, p

sand cloud liquid

and rain water mixing ratiosf Horizontal grid—Lambert Conformal projection, 20

km by 20 km (domain 4 on Fig. 1)



f Finite-difference scheme—cubic spline upstream inter-polation for horizontal advection. Linear upstream in-terpolation for vertical advection

f Time-stepping scheme—forward for advection, for-ward-backward for gravity wave terms. Implicit forvertical diffusion, explicit for horizontal diffusion

f Boundary scheme—Davies-type (Davies 1976) overeight points for wind, temperature and pressure. In-flow/outflow dependent scheme for specific humidity(Giorgi et al. 1993)

f Boundary values—linearly interpolated in space andtime from ECMWF analysis with 12-h intervals, T106resolution horizontally interpolated to a 0.9°]0.9°resolution

f Horizontal diffusion—fourth-order with a non-dimen-sionalised background value plus a term proportional tothe flow deformation as proposed by Majewski (1991)

f Surface—force-restore method, in which the soil para-meters are held constant. 32 land-use types are con-sidered

f Physical parametrizationsBoundary layer formulation based on high-resolutionscheme as suggested by Blackadar (1962)Moist processes by implicit scheme for convective cloudparameterization (Fritsch and Chappell 1980). Explicitscheme for large-scale precipitation from Hsie et al.(1984), with a prognostic equation for cloud and rainwaterFull short and longwave radiation transfer parametriz-ation (Stephens 1978)

2.5 The ETH/D¼D model

f Prognostic variables—u, v, h, qv, p

s, where h"c

p¹#

¸qvand q

v"q

w#q

cwith c

pand ¸ are the heat capa-

city of dry air and the latent heat of water vapour,respectively. ¹, q

w, q

c, ', gR are diagnosed

f Horizontal grid—rotated latitude/longitude co-ordina-tes. South pole at 32.5 °S 10 °E. Resolution 0.5° by 0.5°.The results were further interpolated to a 1° by 1°regular latitude/longitude grid (domain 2 on Fig. 1)

f Vertical grid—sigma-pressure hybrid (g), 20 levelsf Finite-difference scheme—centred second-order accu-

racy similar to the former ECMWF grid point model(Burridge and Haseler 1977)

f Time-stepping scheme—leapfrog, semi-implicit, withan Asselin time filter

f Boundary scheme—simplified Davies (1976) relaxationover eight points

f Boundary values—linearly interpolated in space andtime from ECMWF analysis with 6-h intervals, and 1.5°by 1.5° resolution

f Horizontal diffusion—linear fourth order diffusion ofh, q

w, u, v on model surfaces (Majewski 1991)

f Surface—two soil layers with predicted temperatureand soil water content, and a surface storage containingintercepted water and snow (Jacobsen and Heise 1982).The initial soil moisture is based on the ECMWFanalyses from the first day of the integration

f Physical parametrizationsA boundary layer and turbulence formulation withsecond order closure scheme of hierarchy level 2 (Mellorand Yamada 1974; Muller 1982)Sub-grid-scale cloud micro-physics of Kessler-type withparametrized ice phase for the representation of theBergeron-Findeisen processParametrization of moist convection with the mass-fluxscheme of Tiedtke (1989)Radiative processes according to Ritter and Geleyn (1992)

2.6 The UKMO-RCM/RECSIII model

f Prognostic variables—u, v, h (potential temperature),qcand p

sf Horizontal grid—rotated lat/long, pole (30 °N,160 °E),resolution 0.44]0.44 (i.e. 50 km at rotated grid equa-tor) (domain 6 on Fig. 1)

f Vertical grid—sigma—pressure hybrid (g), 19 levelsf Finite difference scheme—split explicit with a

(two—step) Heun time—stepping scheme for advection.Spatial accuracy is fourth order except at high windspeeds (second order)

f Boundary scheme—linear relaxation over four pointsf Boundary values—linearly interpolated in space and

time from a climate resolution GCM assimilating tem-perature, wind and surface pressure from UKMO ana-lyses

f Horizontal diffusion—fourth order for u, v, qT

(watervapour plus cloud water and ice) and h

L(potential

temperature adjusted for latent heat of cloud water andice). q

Tdiffusion is switched off over steep orography

f Surface—four soil layers with predicted temperatureand soil moisture. Canopy interception and storage ofwater, surface run-off. Snow depth (modified by snow-fall, sublimation and melting) predicted and snow insu-lation represented. The initial soil moisture isinterpolated from an AMIP GCM simulation for therelevant day (in May 1983)

f Physical parametrizationBoundary layer scheme of Smith (1990) with surfacefluxes proportional to surface layer gradients, non-localmixing in unstable conditions and vertical transportsdependent on local gradients and stabilityLarge-scale cloud and precipitation are formed by con-densation, with a greater efficiency over sea than land.Ice precipitation falls by layerThe penetrative convection scheme of Gregory andRowntree (1990) is used modified to include an explicitdown draughtThe gravity wave drag formulation of Palmer et al.(1986), modified to include the anisotropy of the oro-graphy, is usedThe radiation calculations are based on Slingo (1985,1989) and Slingo and Wilderspin (1986)

3 Numerical experiments and observed data

The main purpose of this study is the detection of errorsin the LAMs. We focus on surface air (screen or 2 m)


cFig. 3. ºpper left corner: monthly mean of mean sea level pressure(hPa) from ECMWF analysis for July 1990. All other panels showdifference between simulations and analysis. Positive bias solid lines,negative bias dashed lines. Contours for every hPa

temperature and precipitation (given their importance forimpact studies) and are interested in the detection ofsystematic errors in the physical parametrization schemeswhich influence their simulation. We also analyze thesimulation of mean sea level pressure (MSLP) asa measure of the low—level flow which also influences thesimulation of temperature and precipitation.

In perfect boundary LAM simulations the error growthtendency of the large-scale flow seen in GCMs (renderingsolutions essentially random after 2—3 weeks) is counter-acted by the imposition of boundary conditions. The timescale for modelled processes to be influenced by the driv-ing fields in the interior for the domain 2 on Fig. 1 wasestimated by Sass and Christensen (1995) (by consideringthe average group velocity in the free atmosphere) to beapproximately four days for January 1991. The time scalewill decrease with decreasing model domain size but in-crease towards the surface, due to the stronger influencefrom the lower boundary. Also, in general it will dependon the strength of the flow and typically in summer will bemuch longer.

As surface air temperature and precipitation are heavilyinfluenced by the large-scale flow patterns (e.g. advectionand/or cyclone activity) then if the large-scale flow is closeto the observed our actual time verification is applicableand the errors detected are due to errors in the physicalparametrization schemes. However, if the boundary forc-ing is too weak, random errors in the large-scale flow willgrow and hence may dominate the simulation of thesurface variables. In this case it is more difficult to use theactual time verification to detect errors in the physicalparametrizations, though biases much larger than ob-served year-to-year variations can obviously be character-ized as systematic model errors.

Even with perfect boundary conditions the LAMs mayhave systematic errors in the simulation of the large-scaleflow. If it were possible, we would also be interested in thedetection of such errors. However, with the short integra-tion periods applied in the present study random errorswill not average out and, thus, the monthly MSLP biasescomputed here using the actual time verification cannot beinterpreted as systematic errors alone. A better approach,in which systematic errors may be determined, is to usemulti—annual simulations and then verify against clima-tology. This computationally more expensive approachhas been adopted by the UKMO/Hadley Centre groupwith a 10-y simulation (Noguer 1996).

The results presented here are for July 1990 and Jan-uary 1991. These months were selected as extremes in theannual cycle and from a recent contiguous period duringwhich the ECMWF data assimilation system was notsubstantially updated. The latter was considered impor-tant for the longer integrations undertaken with fourmodels, HIRHAM3 (January 1990 to August 1991), HIR-HAM4 (January 1990 to December 1991), MM4 (August1990 to August 1991) and CLAMBO (October 1989 toApril 1991). This allowed the models to spin-up theirsurface schemes (i.e. soil moisture and snow cover) but theperiod chosen for the MM4 integration only left January1991 for this inter-comparison. RECSIII used boundaryconditions from the UKMO GCM analysis system fromMarch 1984 and to February 1994. PROMES and

ETH/DWD were run for the individual months withoutany spin-up period. The simulations performed, the res-olutions and the boundary fields used are listed in Table 1.

For the inter-comparison the common grid (the CRUgrid, Sect. 1) has been used and the surface temperaturewas first interpolated to the common orography using thestandard lapse rate of 6.5 K/km. Simulated MSLP iscompared to the monthly mean of the driving analyses(6—hourly ECMWF or 12—hourly UKMO analyses). Thesurface parameters are compared to an analysis of 1500monthly averaged surface station data (obtained fromNCAR) using a direct interpolation procedure based onsuccessive correction (Bergthorsson and Doos 1955). Theanalysis procedure is thoroughly described in Christensenet al. (submitted 1997). The temperature analyses wereinterpolated in the vertical to the common orography(Figs. 5 and 9 upper left corners). The precipitation fieldshave not been corrected for gauge biases due to strongwind, wetting or evaporation. Also no account has beentaken of biases due to irregular horizontal and verticaldistributions (e.g. over sampling of valley stations inmountainous regions). Thus, specifically precipitation onmodel up—slopes and mountain tops due to orographiclifting will be underestimated by the analyses. Finally,sensitivity to local topography (a nearby mountain ora lake) which is too small-scale to be represented in themodels, gives rise to further uncertainty.

4 Errors in mean sea level pressure

Here we compare the simulated monthly mean distribu-tions of MSLP with the driving analyses (Figs. 3 and 4).We show the ECMWF analysis (ECMWF analyses andre-analyses and UKMO analyses of MSLP are all similar)and biases with respect to the driving analyses. The biasescan then be used to deduce errors in low-level advection ofheat and moisture. Furthermore, positive and negativebiases can be interpreted respectively (on the basis ofsynoptic experience) as areas with too much subsidenceand too much ascent of tropospheric air.

In July 1990 the monthly mean pressure pattern isnormal with a strong ridge of high pressure extendingfrom west to east over central Europe (Fig. 3). There islittle advection from the Atlantic to southern Europe andwarm dry air is supplied from above by subsidence in theridge. In the larger area models, except HIRHAM4, thebiases indicate a further reduction in the advection fromthe ocean due to negative errors to the south and positiveerrors to the north. As expected, the biases increase inmagnitude with increasing model domain size and distancefrom the western boundary (remember that only part of theintegration domains are included in the figures).

In January 1991 the monthly mean pressure is relativelyhigh in southeastern Europe with strong westerlies over


bFig. 4. As for Fig. 3 but for January 1991

the north (Fig. 4). The larger area models show similarbiases with too low pressure in a belt extending eastwestor diagonally from northwest to southeast. This indicatesoverdevelopment or too slow filling of cyclones. Also, thesame tendency for biases to increase with model domainsize is found but is less pronounced due to the strongerboundary forcing in this month.

The temperature and precipitation simulations (dis-cussed in the following section) will be influenced by thesebiases. However, as random errors in the flow cannot bedistinguished from systematic errors we must be carefulnot to deduce that monthly biases in the near surfaceelements come from the systematic errors alone. Also thelarger influence of random errors in larger domains and insummer should be considered when comparing the simu-lations.

5 Errors in surface parameters

5.1 July 1990

The temperature biases in all simulations are mainly po-sitive over southern and eastern Europe (Fig. 5). They arelargest and extend furthest to the north in HIRHAM3.Elsewhere biases are small except for negative biases in thenorth east in RECSIII and HIRHAM4.

The main reason for the positive biases are deficienciesin the surface schemes. These produce an unrealistic dry-ing-out of the soil (so soil water available for evaporationbecomes insufficient) which is enhanced by positive feed-backs leading to the excessive heating. With the ridge ofhigh pressure over central Europe there is little advectionof cool moist air from the Atlantic and dry warm air issupplied from above by subsidence. The drying is causedby run-off and a net flux of atmospheric humidity out ofthe region which seems to happen too quickly in themodels. When soil water available for evaporation be-comes insufficient the evaporation decreases and the airdries thus reducing cloud cover and precipitation. De-creased precipitation implies less recharge of the soilmoisture, and reduced cloud cover and evaporation implyincreased temperature (though this is partly counteractedby increasing upward long-wave radiation and sensibleheat flux losses). The increased temperature tends to in-crease the evaporation which leads to excessive drying ofthe soil. Finally, negative pressure biases (heat lows) formover these areas (Fig. 3) further reducing advection of coolmoist Atlantic air from northwest into the region andadding a further feedback.

That such processes are taking place is supported by thedeficit of precipitation in southern and central Europe(Fig. 6). Furthermore, the soil moisture decrease through-out July in both HIRHAM simulations and the RECSIIIsimulation as well (Fig. 7). In RECSIII the treatment isdifferent, the maximum available soil moisture is higher,and consequently the relative soil moisture can be ratherlow whilst some water is still available for evapotranspira-

tion. This is partly reflected by the low values seen almosteverywhere. However, in both the HIRHAM3 and RECSIII simulations the soil moisture content is low at thebeginning of the month due to previous additional dryingout, whereas in the HIRHAM4 simulation the soil moist-ure content is relatively high. Here the soil water holdingcapacity (the bucket depth) has been increased via theintroduction of a horizontally varying field of 35 cm onaverage replacing the constant 20 cm field in HIRHAM3.Also the evaporation from the bare soil part in a grid cellis modified in HIRHAM4 with respect to HIRHAM3, inHIRHAM4 only the upper 10 cm of the bucket is avail-able. These changes have contributed to the increased soilmoisture (Fig. 7) with more water available for evapotran-spiration and consequently to the improvement of thetemperature bias (Fig. 5). The role of initial soil moisturevalues is also demonstrated in the one month ETH/DWDsimulation which is initialized with ECMWF analyses.This simulation is not as dry as the HIRHAM3 and theUKMO simulations, and consequently the temperaturebiases remain more reasonable.

For HIRHAM3, and possibly the other models, thewarming could also result from an overestimation of theincoming shortwave fluxes. Wild et al. (1995b) estimatedthis in the ECHAM3 (a GCM model from MPI with thesame physical parametrization scheme as used in HIR-HAM3) radiation scheme to be up to 50 W/m2 for dailymaximum values under cloud free summer conditions inSwitzerland (about 10 W/m2 over the diurnal cycle).These and other authors have presented evidence thata similar overestimation is a problem also in several otherGCMs, but not in ECHAM4 (similar physical parametriz-ation as in HIRHAM4). Furthermore, Wild et al.(1995c)have shown that under clear-sky conditions the radiationscheme of ECHAM3 underestimates the incoming lon-gwave radiation at the surface by 10 W/m2 on average.Similar errors (resulting from inaccuracies in the par-ametrization of the water vapour continuum (Giorgettaand Wild 1995)) are seen in other GCMs, including that ofthe UKMO, but again not in ECHAM4. Hence, on aver-age the solar and downward longwave radiation errorstend to cancel. However, in summer, especially at lowlatitudes, the positive solar radiation error will dominateas is seen in the comparison of HIRHAM3 and HIR-HAM4 over the area of warming Table 2.

This large excess of solar radiation in HIRHAM3 re-sults from errors in the radiation scheme and fewer clouds(25% compared with 29% in HIRHAM4). This is onlypartially compensated for by reduced downward long-wave, further balancing fluxes are provided by increases inupward longwave and sensible heat fluxes. These latterresult from an increase in the surface temperature (surfaceair temperature is about 2K higher). However, the latentheat flux does not show a corresponding increase due tothe limitation on evaporation from reduced soil moisture.Hence part of the surface temperature increase is in orderto compensate for the reduced evaporation which alsocauses the reduction in cloud cover. This demonstratesone of the positive feedbacks described earlier. However, itis also clear that errors in the radiation scheme (contribu-ting to the excess in solar radiation) also contribute to thesurface temperature error.


bFig. 5. ºpper left corner: analyzed monthly mean 2-m temperature(°C) for July 1990. All other panels show difference between simula-tions and analysis. Positive bias highlighted with solid lines, negativebias dashed lines

In contrast to our conclusions, Wild et al. (1995a) at-tributed a similar reduced drying and heating in experi-ments with ECHAM3 and ECHAM4 over Europe atT106 resolution to the more accurate ECHAM4 radiationscheme (rather than changes in the bare soil evaporationand the soil water holding capacity). However, sensitivityexperiments performed with the HIRHAM3 model(Windelband et al. 1995), show that the effect alone ofa doubling of the soil water holding capacity delayed thedrying out for several months and resulted in a significantreduction of the large positive July temperature bias.A similar result has been obtained with the HIRHAM4model in an experiment where the July 1990 monthstarted from more moist conditions than in the presentexperiment. The temperature bias was eliminated almosteverywhere and also a significant reduction in the precipi-tation bias was obtained.

This indicates that the higher sensitivity of the HIR-HAM models to changes in the soil water holding capa-city is due to increased resolution. The resolution inHIRHAM3 is double that of the T106 ECHAM3/4 mod-els, in which case the mountain chains in southern Europeare better resolved, and hence their sheltering effect frommoist oceanic air masses is increased. Also, work byNoguer (1996) and Jones et al. (1997) has shown that theincreased resolution in the RECSIII model, compared tothe UKMO GCM, causes more extreme precipitationevents and more dry days which again leads to largeincreases in run-off and in evaporation. This results ina faster drying of the soil for the same mean precipitation.

The negative temperature errors in the northeast inHIRHAM4 and to a lesser extent in RECSIII are due tocold advection by the anomalous southwesterly (HIR-HAM) or northwesterly (RECSIII) low-level flow intonorthern Scandinavia and Finland/Russia. The anomal-ous cold air advection probably acts to counteracta warming in this region.

Positive precipitation biases are generally seen in areasof orographic precipitation, i.e. in PROMES, UKMO andHIRHAM3 (Fig. 6). This is not explained by an increase inhorizontal flow and thus orographic lifting. For HIR-HAM3 a contributing factor could be erroneous up-slopediffusion of moisture along terrain following co-ordinatesurfaces. In HIRHAM4 this diffusion is switched off nearsteep topography, which causes a substantial reduction ofthe positive orographic biases. If, however, we accept thatthe observed orographic precipitation is underestimatedthen both the HIRHAM4 and ETH/DWD are too dry inmountainous areas. For PROMES the observed patternof orographic precipitation over the Iberian peninsulacompares very well with a higher resolution analysis(not shown), but the amounts are much too high. Thisis due to an unfortunate usage of erroneous and excessive-ly high SSTs in this experiment, promoting excessevaporation.

5.2 January 1991

The precipitation is very low in the high pressure region tothe south, except for the areas of significant orographyover the Alps and parts of former Yugoslavia (Fig. 8).Elsewhere precipitation is normal for the season withmaximum values along western coastal areas. The behav-iour is well simulated in the smaller region models with anoverestimation over orography compared with the (prob-ably underestimated) observations. The other, larger areamodels, all generally have positive precipitation biases,except for MM4 in the north. Contributing to the centralbelt of biases in the HIRHAM models is the enhancedadvection (Fig. 4) of warm moist air into this region. Thiswould also explain the positive biases in MM4. The areasof enhanced advection are south and west of negativepressure biases which are caused by enhanced cycloneactivity, i.e. deeper and/or longer lasting lows. Also thediagonal bands of negative pressure biases in the RECSIIIand ETH/DWD models are probably due to enhancedcyclone activity which, in turn, may explain the positiveprecipitation biases in these models. For the ETH/DWDmodel Luthi et al. (1996) have noted a tendency for overes-timation of synoptic-scale variability, an indication ofenhanced cyclone activity. Jones et al (1995) showed thata general tendency of RECSIII to over predict large—scaleprecipitation is due to an enhancement of verticalmotions (compared to those in an equivalent GCMusing the same physics) which could be linked withthe increased cyclone activity. This may also be a contri-buting factor to the errors in the HIRHAM models in thesoutheast (note negative pressure errors in this region,Fig. 4).

Except for HIRHAM4 and locally in the other simula-tions surface air temperature biases are generally negative(Fig. 9). The other exceptions are PROMES which is toowarm due to its use of excessive SSTs as is MM4 on the leeside of the Scandinavian mountains which may be due toenhanced Fohn effects. Contributing to the generallynegative errors are the systematic errors in the radiationschemes mentioned already. In winter time, in HIRHAM3(and possibly other of the models) the cooling effect of theunderestimated incoming longwave radiation dominatesthe heating effect of the overestimated solar radiation(Table 2). This implies a heating of the surface in HIR-HAM4 relative to HIRHAM3 consistent with the highertemperatures and the increased upward longwave radi-ation in HIRHAM4. The sensible and latent heat fluxdifferences nearly balance. The small residual of!3.64 W/m2 implies a heating during the month of thesoil in HIRHAM4 relative to HIRHAM3.

The very cold biases over Russia and Finland seen inmost of the simulations (Fig. 9) are partly due to excesscold air advection from the north (ETH/DWD) or fromthe east (RECSIII, HIRHAM3/4) (Fig. 4). However, errorsin the simulated snow cover might also be an importantcontributing factor though this is difficult to verify againstobservations.


Fig. 7. Soil moisture content relative to saturation at the beginning and end of July 1991. In HIRHAM3 the saturation value is a constantvalue. It is a varying field in HIRHAM4 (values about twice that of HIRHAM3) and RECSIII (with even higher values)

bFig. 6. As for Fig. 5 but for precipitation (mm/day)

6 Discussion and conclusion

Our primary goal is regionalization of climate changesimulations using time-dependent boundary conditionstaken from coarse-mesh global model (GCM) simulations.

In such simulations the LAM large-scale flow has similarsystematic errors to those of the global model (Machen-hauer et al. 1996). Also, the smaller the LAM domain themore similar the errors but even with a very large LAMarea centred in the North Atlantic, covering 1/8 of theEarth, the flow errors are very similar. For Europe theselarge-scale errors are generally not local in origin. Fur-thermore, they cause relatively large errors in the funda-mental near-surface climate parameters, surface air


Table 2. Difference in surface fluxes, HIRHAM3—HIRHAM4 values(positive downward), averaged over south east Europe for July andEuropean land points for January

Fluxes/month July 1990 January 1991W/m2 W/m2

Longwave upward [10.11 7.14Longwave downward [12.35 [16.73Shortwave net 24.33 5.22Sensible heat [6.46 [12.45Latent heat 4.61 13.20Snow melt 0.00 [0.02

Residual 0.02 [3.64

cFig. 8. As for Fig. 6 but for January 1991

temperature and precipitation. For example, anover—strong zonal flow over Europe in winter in one of themodels analyzed in Machenhauer et al. (1996) producedlarge positive temperature and precipitation biases.

With the present perfect boundary experiments wewanted to eliminate the influence of the large-scale sys-tematic errors imposed by a driving GCM and so isolatecontributions from internal LAM errors which mightotherwise be difficult to detect. Ideally these experimentsshould be multi-year simulations verified against climat-ological data (as has been done with the RECSIII experi-ments, Noguer 1996, two months of which are presentedhere). In the present study, however, we have chosen a lesscomputationally expensive approach, simulating a sum-mer and a winter month, July 1990 and January 1991,with 6 different LAM models, all of which could not berun for multi-year periods. They have been validatedagainst observations from these months, MSLP againstthe driving analyses and monthly mean surface air tem-perature and precipitation against analyses of monthlymean station values.

In spite of the perfect boundary conditions errors in theflow will develop. Some of these internal errors will berandom and would average out in a long term mean buthere cannot be separated from the systematic LAM errors,if any. As expected, we found that these errors in the flowgenerally increase with increasing domain size. The otherinternal source of errors, and of particular interest here,are defects in the physical parametrization schemes.Again, their impact cannot be completely separated fromthose of internal flow errors but we have tried to identifysituations where they appear to be the main factor. Twosuch areas are surface processes in summer and radiationin both summer and winter.

Most models show a warming in the south and east insummer (over most of the Balkan generally 2—6 K, inHIRHAM3 4—8 K) which is connected with the soil moist-ure drying out too rapidly. This leads to positive feed-backs of reduced evaporation (and evaporative cooling)and subsequent reduced precipitation (and cloud shading)and of heat low formation which enhances the decouplingof the region from the westerly advection of moist/coolAtlantic air. As shown by surface heat budget calculationswith the HIRHAM models excessive incoming solar radi-ation in HIRHAM3, largely absent in HIRHAM4, con-tributes to this process. As many models have radiationscheme errors similar to those in HIRHAM3 we suggest

a similar enhancement of the error is happening in theother simulations. That reduced warming in HIRHAM4is the result not only of an improved radiation schemefollows from a HIRHAM3 sensitivity test with soil waterstorage capacity of the same magnitude (i.e. doubled) as inHIRHAM4 which substantially reduced the problem anda similar result obtained in a HIRHAM4 experimentstarted from more moist conditions than the present one.This indicates that the warming and drying problem isenhanced by the increased resolution due to better shelter-ing effects of the mountain chains and increased run-offand evaporation which were supported by analysis ofprecipitation and drying in UKMO simulations.

Similar warm and dry biases for the summer seasonwere found by Machenhauer et al. (1996) in ensembleaverages of multi-year climate simulations, performedwith three regional climate models. The biases were shownto be significantly larger than the most extreme decadalmean anomalies observed in the 1961-1990 period, whichshows that the biases discussed here are indeed systematicmodel errors.

Another common feature amongst many of the modelsis a cooling in winter (1—3 K over large areas). We believethis results from the underestimation of incoming long-wave radiation at the surface seen in many radiationschemes (believed to be caused by inaccuracies in theparametrization of the water vapour continuum). That itis a major factor here is supported by surface heat budgetcalculations and the fact that the correction of this error inthe HIRHAM4 physics leads to improved (and sometimespositive) biases.

Another area for further investigation is the simulationand validation of summer orographic precipitation. Ouranalyses of precipitation in mountainous regions areprobably an underestimate, though we do not know howmuch and thus we need to improve these estimates. Thechange in horizontal diffusion between HIRHAM3 andHIRHAM4 seems to eliminate the apparent precipitationbiases in the former, though if the orographic precipita-tion in the analyses are under-estimated as we think theyare, this is a deterioration. Also RECSIII has positiveorographic precipitation biases but with a horizontal dif-fusion treatment similar to that in HIRHAM4. Further-more, ETH/DWD and CLAMBO show almost no suchbiases but have a treatment similar to HIRHAM3. Thus,other factors than just horizontal diffusion are importanthere and further work is required to identify these.

Most of the other major biases are explained by the(internal) errors in the large-scale flow. Both positive andnegative temperature biases (about 1—3 K) and positiveprecipitation biases (over large areas 1—5 mm/day) insome models result from anomalous advection. Anotherrelated factor, which is possibly important and may bea systematic error, is excessive cyclone activity promotingenhanced precipitation, though we have no directevidence of this. Given the size of the biases associatedwith these errors this is an important area for furtherinvestigation.


bFig. 9. As for Fig. 5 but for January 1991

Acknowledgements. This study was supported by the EuropeanCommission under contracts EV5V-CT92-0126 and EV5V-CT94-0505. The research of the ETH group was funded by the SwissNational Science Foundation (contract SPP-U 5001-035166).The authors would like to thank the partners in these contractsand the colleagues at their home institutions for their supportand many valuable discussions. The ETH group would liketo acknowledge the support of the German Weather Serviceand the Swiss Meteorological Institute in providing access to thenumerical model.

References

Anthes RA (1977) A cumulus parametrization scheme utilizinga one-dimensional cloud model. Mon Weather Rev 105 : 270—286

Anthes RA, Hsie E-Y, Kuo Y-H (1987) Description of the PennState/NCAR mesoscale model version 4 (MM4). NCAR TechniNote 282#STR

Bergthorsson P, Doos B (1955) Numerical weather map analysis.Tellus 7 : 329—340

Betts AK (1986) A new convective adjustment scheme Part I: obser-vational and theoretical basis. Q J R Meteorol Soc 112 : 667—691

Black TL (1988) The steep mountain ETA co-ordinate regionalmodel: a documentation. NOAA/NWS/NMC

Blackadar AK (1962) The vertical distribution of wind and turbulentexchange in a neutral atmosphere. J Geophys Res 67 : 3095—3102

Brinkop S, Roeckner E (1995) Sensitivity of a general circulationmodel to parametrizations of cloud-turbulence interactions inthe atmospheric boundary layer. Tellus 47A : 197—220

Brown J, Campana K (1978) An economical time-differencing sys-tem for numerical weather prediction. Mon Weather Rev106 : 1125—1136

Burridge DM, Haseler J (1977) A model for medium range weatherforecasting, adiabatic formulation. ECMWF Tech Rep 4 : 46pp

Christensen JH, Christensen OB, Lopez P, van Meijgaard E, BotzetM (1996) The HIRHAM4 regional atmospheric climate model.DMI Sci Rep 96-4, 51pp (Available from DMI, Lyngbyvej 100,Copenhagen Ø, Denmark)

Christensen OB, Christensen JH, Botzet M (1997) Very-high-resolu-tion regional climate simulations over Scandinavia. Controlexperiment. J Clim (submitted)

Claussen M, Lohmann U, Roeckner E, Schulzweida U (1994) A glo-bal data set of land surface parameters. MPIM Rep No. 135

Davies HC (1976) A lateral boundary formulation for multilevelprediction models. Q J R Meteorol Soc 102 : 405—418

Dumenil L, Todini E (1992) A rainfall-runoff scheme for use in theHamburg climate model In: O’Kane J (ed) Advances in theoret-ical hydrological hydrology, A tribute to James Dooge vol. 1,European Geophysical Society Series of Hydrological Sciences,129—157. Elsevier, Amsterdam

Dickinson RE, Henderson-Sellers A, Kennedy PJ, Wilson MF(1986) Biosphere-atmosphere transfer scheme (BATS) for theNCAR community climate model. NCAR Techn Note, Decem-ber 1986

Dickinson RE, Errico RM, Giorgi F, Bates GT (1989) A regionalclimate model for the western United States. Climatic change,15 : 383—422

Dorland R van, Morcrette J-J (1997) A comparison of spectroscopiccompilations using long-wave radiation models. J Geophys Res(submitted)

Eickerling H (1989) Parametrisierung des infraroten Strahlungstran-sports fur Kohlendiozid, Wasserdampf und Ozon in einem breit-bandigen Stralungsmodell, Diplomarbeit Institut furMeteorologie und Geophysik, Universitat Koln, Germany

Fritsch JM, Chappell CF (1980) Numerical prediction of convective-ly driven meso-scale pressure systems. Part I: convective para-metrization. J Atmos Sci, 37 : 1722—1733

Giorgetta M, Wild M (1996) The water vapour continuum and itsrepresentation in ECHAM4. MPIM Rep 162

Giorgi F (1990) Simulation of regional climate using a limited areamodel nested in a general circulation model. J Clim 3 : 941—963

Giorgi F, Marinucci MR, and Bates GT (1993) Development ofa second generation regional climate model. II Convective pro-cesses and assimilation of lateral boundary conditions. MonWeather Rev 120 : 2814—2832

Gregory D, Rowntree PR (1990) A mass flux convection schemewith representation of cloud ensemble characteristics and stabil-ity dependent closure. Mon Weather Rev 118 : 1483—1506

Hense A, Kerschgens M, Raschke E (1982) An economical methodfor computing radiative transfer in circulation models. QJRMeteorol Soc 108 : 231—252

Holtslag AAM, Boville BA (1993) Local versus nonlocal boundary-layer diffusion in a global climate model. J Clim 6 : 1825—1842

Hsie E-Y et al. (1984) Numerical simulation of frontogenesis ina moist atmosphere. J Atmos Sci 41 : 2581—2594

Jacobsen I, Heise E (1982) A new economic method for computationof the surface temperature in numerical models. Beitr PhysAtmos 55 : 128—141

Janjic J (1984) Non linear advection schemes and energy cascade onsame staggered grid. Mon Weather Rev 112 : 1234—1245

Jones RG, Murphy JM, Noguer M (1995) Simulation of climatechange over Europe using a nested regional climate model. PartI. Assessment of control climate including sensitivity to locationof lateral boundaries. QJR Meteorol Soc 121 : 1413—1449

Jones RG, Murphy JM, Noguer M, Keen AB (1997) Simulation ofclimate change over Europe using a nested regional climatemodel. Part II. Comparison of driving and regional model re-sponses to a doubling of carbon dioxide concentration. QJRMeteorol Soc 123 : 265—292

Kallen E (ed) (1996) HIRLAM documentation manual, system 2.5;(Available from SMHI, S-60176 Norrkoping, Sweden)

Louis JF (1979) A parametric model of vertical eddy fluxes in theatmosphere. Bound-Layer Meteoro 17 : 187—202

Louis JF, Tiedtke M, Geleyn JF (1982) A short history of the PBLparametrizations at ECMWF. Proc ECMWF Workshop onBoundary-Layer Parametrization, p 59-79, ECMWF, Reading

Luthi D, Cress A, Davies HC, Frei C, Schar C (1996) Interannualvariability and regional climate simulations. Theor Appl Clima-tol 53 : 185—209

Machenhauer B, Windelband M, Botzet M, Jones RG, DequeM (1996) Validation of present-day regional climate simulationsover Europe: nested LAM and variable resolution global modelsimulations with observed mixed layer ocean boundary condi-tions. MPIM Rep 191

Majewski D (1991) The EUROPA-model of the Deutscher Wetter-dienst. Proc ECMWF Seminar on Numerical Methods in Atmo-spheric Models. ECMWF, Reading UK

Mellor GL, Yamada T (1974) A hierarchy of turbulence closuremodels for planetary boundary layers. J Atmos Sci 31 : 1791—1806

Miller MJ, Palmer TN, Swinbank R (1989) Parametrization andinfluence of sub-grid scale orography in general circulation andnumerical weather prediction models. Meteorol Atmos Phys40 : 84—109

Morcrette J-J (1989) Description of the radiation scheme in theECMWF model. Techn Memo 165 : ECMWF, Reading

Muller E (1982) Turbulent flux parametrization in a regional-scalemodel. ECMWF Workshop on planetary boundary layer para-metrization pp 193—220

Noguer M (1996) Application of a regional climate model overEurope: analysis of the effect of the systematic errors in theboundary conditions (in Spanish). Phd Thesis, University Com-plutense, Madrid, Spain

Nordeng TE (1994) Extended versions of the convective parametriz-ation scheme at ECMWF and their impact on the mean andtransient activity of the model in the tropics. Tech Memo 206


Palmer TN, Shutts GJ, Swinbank R (1986) Alleviation of a system-atic westerly bias in general circulation and numerical weatherprediction models through an orographic gravity wave dragparametrization. Q J R Meteorol Soc, 112 : 1001—1039

Ritter B, Geleyn JF (1992) A comprehensive radiation scheme fornumerical weather prediction with potential applications in clim-ate simulations. Mon Weather Rev 109 : 758—766

Robinson PJ, Finkelstein PL (1991) The development of impactoriented climate scenarios. Bull Am Meterial Soc 72 : 481—490

Rockel B, Zhao B, Raschke E (1986) A flexible radiative transferroutine for GCMs: infrared part. In: G.J. Boer (ed) ResearchActivities in atmospheric and oceanic modelling. World Me-teorological Organization; CAS/JSC WGNE 9 : 4.62—4.65

Roeckner E, Rieland M, Keup E (1991) Modelling of cloud andradiation in the ECHAM model. In: ECMWF/WCRP Work-shop on clouds, radiative transfer and the hydrological cycle,12-15 November 1990, pp. 199—222, ECMWF, Reading

Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M,Dumenil L, Esch M, Giorgetta M, Schlese U, SchulzweidaU (1996) The atmospheric general circulation model ECHAM4:model description and simulation of present-day climate: MPIMRep no. 218

Sass BH, Christensen JH (1995) A simple framework for testing thequality of atmospheric limited-area models. Mon Weather Rev123 : 444—459

Slingo A (ed) (1985) Handbook of the Meteorological Office 11-layeratmospheric general circulation model. Volume 1: model descrip-tion, Meteorological Office 20 DCTN 29

Slingo A (1989) A GCM parametrization for the short wave radi-ative properties of water clouds: J Atmos Sci 46 : 1419—27

Slingo A, Wilderspin RC (1986) Development of a revised long-waveradiation scheme for an atmospheric general circulation model.Q J R Meteorol Soc 112 : 371—386

Smith RNB (1990) A scheme for predicting layer clouds and theirwater content in a general circulation model. Q J R Meteorol Soc116 : 435—460

Stephens GL (1978) Radiation profiles in extended water clouds. II:parametrization schemes. J Atmos Sci 35 : 2123—2132

Sundqvist H (1978) A parameterization scheme for non-convectivecondensation including prediction of cloud water content.Q J R Meteorol Soc 104 : 677—690

Tiedtke M (1989) A comprehensive mass flux scheme for cumulusparametrization in large-scale model. Mon Weather Rev117 : 1779—1800

Wild M, Dumenil L, Schultz J-P (1995a) High resolution GCMsimulations over Europe: surface processes. MPIM Rep 176

Wild M, Ohmura A, Gilgen H (1995b) Validation of general circula-tion model radiative fluxes using surface observations. J Clim8 : 1309—1324

Wild M, Ohmura A, Gilgen H, Roeckner E (1995c) Regional climatesimulations with a high resolution GCM: surface radiative fluxes.Clim Dyn 11 : 469—486

Windelband M, Machenhauer B, Botzet M (1995) Investigation ofa warm and dry summer in a simulation with a regional climatemodel. Final Report of the EC Regionalization Project. Annex1 to the MPIM contribution, 7 pp. (Available from MPIM)

.


Validation of present-day regional climate simulations over Europe: LAM simulations with observed...

Documents

Transcript of Validation of present-day regional climate simulations over Europe: LAM simulations with observed...