Current and perturbed climate as simulated by the second-generationCanadian Regional Climate Model (CRCM-II) over northwestern NorthAmerica

Received: 8 July 2002 / Accepted: 25 April 2003 / Published online: 23 August 2003� Springer-Verlag 2003

Abstract An updated version of the Canadian RegionalClimate Model (CRCM-II) has been used to performtime-slice simulations over northwestern North Amer-ica, nested in the coupled Canadian General CirculationModel (CGCM2). Both driving and driven models areintegrated in a scenario of transient greenhouse gases(GHG) and aerosols. The time slices span three decadesthat were chosen to correspond roughly to single, doubleand triple current GHG concentration levels. Severalenhancements have been implemented in CRCM-IIsince the CRCM-I climate-change simulations reportedupon earlier. The larger computational domain,extending further to the west, north and south, allowsfor a better spin-up of weather systems as they enter theregional domain. The increased length of the simula-tions, from 5 to 10 years, strengthens the statisticalrobustness of the results. The improvements to thephysical parameterisation, notably the moist convectionscheme and the diagnostic cloud formulation, cure theexcessive cloud cover problem present in CRCM-I, re-duce the warm surface bias and prevent the occurrenceof grid-point precipitation storms that occurred withCRCM-I in summer. The dynamical ocean and sea-icecomponents of CGCM2 that is used to provide atmo-spheric lateral and surface boundary conditions toCRCM-II, as well as the use of transient rather thanequilibrium conditions of GHG and the inclusion ofdirect aerosols forcing, in both CGCM2 and CRCM-II,increase the realism of the CRCM-II climate-changesimulation.

1 Introduction

General circulation models (GCMs), including land-surface processes and coupled with dynamical ocean andsea-ice models (hereinafter termed CGCMs), currentlyprovide the most sophisticated, physically based ap-proach to simulate the large-scale response of the cli-mate system to projected scenarios of increasinggreenhouse gases (GHG) and aerosols concentrations.Because of the computational load of integrating com-plex CGCMs over simulated periods of several centu-ries, the horizontal resolution has to be limited to gridmeshes of the order of a few hundred kilometres. Suchresolution is inadequate to resolve atmospheric pro-cesses operating at mesoscale, such as frontal zones, andsmall-scale surface forcing due to local orography or in-land water bodies (IPCC 1995). Surface hydrology andatmospheric moist processes in particular are known toexhibit high variability at small (below the CGCMs’resolved) scales. Furthermore, climate-change impactand adaptation studies deal with processes operating onmuch finer scales than currently affordable withCGCMs.

Nested limited-area regional climate models (RCMs)represent an appealing approach to achieve finer spatialresolution climate and climate-change simulations at anaffordable computational cost. Nested with atmosphericdata simulated by CGCMs and integrated for longperiods of time, RCMs are potentially useful tools foridentifying effects of anthropogenic forcing at regionalscale (e.g. Pan et al. 1999). These RCMs can be run atfairly high resolutions (with grid meshes of a few tens ofkilometres) over an area of interest covering typicallyseveral millions of square kilometres. Such RCM cli-mate-change simulations have been made for variousparts of Europe (e.g. Jones et al. 1997; Machenhaueret al. 1998; Jones and Reid 2001; Durman et al. 2001;Christensen et al. 2001; Frei et al. submitted 2002), ofNorth America (e.g. Giorgi et al. 1998; Laprise et al.

Climate Dynamics (2003) 21: 405–421DOI 10.1007/s00382-003-0342-4

R. Laprise Æ D. Caya Æ A. Frigon Æ D. Paquin

R. Laprise (&) Æ D. Caya Æ A. Frigon Æ D. PaquinCRCM/UQAM – Ouranos, 550 West Sherbrooke St,19th floor, Montreal (Quebec), Canada H3A 1B9E-mail: laprise.rene@uqam.ca

1998) and Australia (Whetton et al. 2001); see also theearlier references cited in the reviews by Mearns et al.(1995) and McGregor (1997). A noteworthy alternativeapproach to nested limited-area RCMs is that of stret-ched-grid global models (e.g. Deque et al. 1998; Fox-Rabinovitz et al. 2001).

First-generation RCMs could be classified into twogroups, depending upon whether their subgrid-scalephysical parameterisation was issued from (1) a high-resolution numerical weather prediction (NWP) ormesoscale research model (e.g. Juang and Kanamitsu1994; Takle et al. 1999), or (2) a low-resolution globalclimate model (e.g. Deque et al. 1998; Laprise et al. 1998;Christensen et al. 2001). In the former case, the pa-rameterisations were developed for weather forecastmodels with resolutions similar to that of RCMs, butthey may not have been designed with consideration oflong-term balances required by climate integrations. Inthe latter case, the parameterisation package is shared bythe RCM and its nesting GCM, thus facilitating cou-pling and the interpretation of the simulated results;there is usually a need however for re-tuning of someparts of the parameterisation, specially those related tothe treatment of moist processes, to account for the in-creased resolution of RCM compared to GCM. Recentstudies clearly document the need for parameterisationsthat are suitable for long-term integration and appro-priate for the resolution of the RCMs (e.g. Jacob andPodzun 1997; Christensen et al. 1998; Laprise et al.1998).

Early GCMs were coupled with rather simple mixed-layer ocean and thermodynamic sea-ice models, and‘‘equilibrium’’ climate-change simulations with thesemodels were performed with greenhouse gases (GHG)concentrations held fixed throughout the integration.More recent GCMs include coupling with dynamicalocean and sea-ice models (henceforth called CGCMs)that allow to perform more realistic simulations,accounting for the time evolution of observed past andanticipated future concentrations of GHG and aerosols(GHG & A) and the strong feedback of the oceans.Owing to the characteristic time scale of the deep oceans,CGCMs are spun-up for several centuries beforelaunching a ‘‘transient’’ climate experiment that spanstypically a couple of centuries. In order to increase thestatistical significance of the results, an ensemble of thesesimulations may be performed with identical modelconfiguration and scenario of GHG & A. Most RCMclimate-change simulations to-date have been performedusing, as ocean surface boundary condition, coarse-re-solution CGCM-simulated ocean temperature and sea-ice amounts, simply interpolated on the RCM finer grid.Because of this simplified treatment of the ocean con-ditions, it is possible to run an RCM for selected timeslices within a long CGCM simulation, in order to lessencomputation costs.

In this work, we will present results of climate si-mulations performed with the second-generation ofthe Canadian Regional Climate Model (CRCM-II)

integrated over a computational domain covering anorthwestern sector of North America and adjacentparts of Hudson Bay and Pacific and Arctic Oceans. TheCRCM-II is nested with simulated data of the coupledCanadian General Circulation Model (CGCM2; Flatoand Boer 2001) in a scenario of transient GHG & A. TheCRCM-II simulation covers three time slices corre-sponding roughly to single, double and triple currentGHG concentration levels. The next section describesthe CRCM-II and CGCM2 models and their experi-mental configurations. Section 3 presents the analysis ofsimulated results where the first sub-section looks intothe 1975–1984 decade (referred to as the 1 · CO2 cli-mate), by comparing the simulation of the CRCM-IIwith that of the CGCM2 and with available recent-pastclimate observations for basic variables such as tem-perature, precipitation, snow depth, soil water and cloudcover. Afterwards, sub-section 3.2 discusses the resultsof the simulated 2040–2049 decade (referred to as the 2 ·CO2 climate), comparing the CRCM-II and CGCM2-simulated climate-change results. Sub-section 3.3 pre-sents a hydrological budget of the simulated 1 · CO2

and 2 · CO2 climates, carried out over four sub-regionsof the CRCM-II domain. Finally, Section 4 will sum-marise the highlights of the results of this study. It isnoteworthy to mention that a subset of the CRCM-II-simulated data for the 1·, 2· and 3 · CO2 time slices isavailable for applications of interested users via theinternet at http://www.cccma.bc.ec.gc.ca/data/rcm/rcm.shtml.

2 Description of models and experimental configuration

2.1 The CRCM-II

The regional model used for this study is an updated version of thefirst-generation Canadian Regional Climate Model (CRCM-I;Caya and Laprise 1999; Laprise et al. 1998). This limited-areanested model, developed at the Universite du Quebec a Montreal,uses a dynamical kernel (Laprise et al. 1997) that is based on thefully elastic non-hydrostatic equations solved by a non-centredsemi-implicit semi-Lagrangian three-time-level marching schemewith a weak running time filter. The lateral boundary conditionsare provided through the one-way nesting method of Davies (1976);the regional model receives atmospheric nesting information in theform of a dataset, but the regional model simulation does notinfluence the driving data in return. Right at the lateral boundaries,the time evolution of vertical profiles of winds, air temperature,water vapour and pressure are imposed as nesting data to the re-gional model. Over a ribbon covering some 10 grid points from theedges of the lateral boundaries (called the sponge zone), the sim-ulated horizontal winds are relaxed toward the values of the drivingdata, with a strength varying as a cosine square from the distanceto the boundary.

The regional model shares most of the subgrid-scale physicalparameterisation package of the global model that provides itsnesting data for this study: the second-generation Canadian Cou-pled General Circulation Model (CGCM2; Flato and Boer 2001).The CGCM2 combines a dynamical ocean and sea-ice model(Flato et al. 2000) with the atmospheric and land-surface processescomponents of an earlier uncoupled version of the model (GCMii;McFarlane et al. 1992; Boer et al. 1992). The reader is referred tothe former paper for an extensive description of the physical

406 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

parameterisation and to Caya and Laprise (1999) for a summary ofits implementation in CRCM-I.

Compared to the earlier version of the Canadian RegionalClimate Model (CRCM-I), some noteworthy modifications havebeen made to the physical parameterisation and numerical com-ponents in the second-generation version (CRCM-II) used for thisstudy. The contributions of the physical parameterisation to thetime tendencies are now incorporated with the nonlinear dynamicalexplicit part in the semi-implicit marching algorithm, thus reducingsubstantially the time truncation associated with the time-splittingprocedure used in the earlier version (Caya et al. 1998), and dis-pensing from the ad hoc ‘‘hydrostatic adjustment’’ used by Lapriseet al. (1998). The nesting of water vapour is now achieved byperforming the multiple interpolations required during the pre-processing on the dew-point depression variable rather than specifichumidity, thus preventing the occurrence of fictitious supersatu-ration events resulting from interpolating temperature and specifichumidity separately. The strength of the bi-harmonic lateral dif-fusion has also been reduced by a factor of 2.6 (now a = 0.2219;see Appendix of Laprise et al. 1998); this implies a damping factorof 5% per time step for the shortest resolved scales. Three changeshave been made in the physical parameterisation package, com-pared to the earlier CRCM-I version. The moist convectiveadjustment scheme has been replaced by the deep convectionscheme of Kain and Fritsch (1990), better suited to the horizontalresolution of the regional model (Paquin and Caya 2000). Strati-form precipitation however is still parameterised as a simplesupersaturation-based condensation scheme as in GCMii andCGCM2. Cloud cover is still parameterised in terms of a simplefunction of local relative humidity, and assuming maximum (orrandom) overlap, depending upon whether cloud presence isdiagnosed in adjacent layers (or not), as in GCMii and CGCM2.The cloud onset function however has been altered in CRCM-II toreduce the excessive cloudiness noted in the simulations of CRCM-I; the modified vertical profile of critical relative humidity proposedin Sect. 4b of Laprise et al. (1998) is now employed in CRCM-II.Finally, the prognostic calculation of land-surface temperature isimplemented with a backward-implicit scheme to suppress theoccasional numerical instabilities associated with the original for-ward-in-time scheme, as documented in Giguere et al. (2000).

2.2 The CGCM2

For the experiments reported here, the data serving to nest theCRCM-II came from the last of a three-member ensemble ofsimulations performed with CGCM2 (Flato and Boer 2001) in ascenario of transient GHG & A for the period extending from1850 to 2100. The atmospheric component of CGCM2 is un-changed from earlier versions of GCMii in non-coupled mode(McFarlane et al. 1992) and from the coupled version with simple50-m mixed-layer ocean (Boer et al. 1992) that was used to pro-duce the CRCM-I nesting data as reported by Laprise et al.(1998). The atmospheric component of CGCM2 is a spectralmodel with T32 horizontal truncation and 10 unequally spacedhybrid sigma-pressure levels in the vertical. The ocean and icecomponents of CGCM2 evolved from an earlier version referredto as CGCM1 (Flato et al. 2000). In both cases, the oceancomponent is a three-dimensional grid-point model based on theGFDL MOM1.1 code (Pacanowski et al. 1993), using a 1.875�longitude–latitude horizontal resolution and 29 levels. CGCM2differs from CGCM1 by its use of isopycnal eddy-stirring mixingparameterisation of Gent and McWilliams (1990). In addition, thesea-ice component of CGCM2 is based on the dynamical cavi-tating-fluid scheme of Flato and Hibler (1992), rather than thethermodynamic sea-ice of CGCM1. The atmosphere, ocean andice components of CGCM2 exchange water, heat and momentumonce per day. A monthly mean flux-adjustment procedure forheat and fresh water at the ocean’s surface ensures that thecoupled model reproduces the observed climatology under currentforcing conditions; no adjustment is applied to momentum fluxeshowever (Flato and Boer 2001). The ocean model was spun-up

for a few thousand simulated years before initiating the period1850 to 2100 with transient GHG & A.

2.3 Experimental configuration

The CRCM-II simulations reported were performed with a 45-km(true at 60�N) grid-size mesh, on a 120 by 120 grid-point compu-tational domain covering the north-western North America andadjacent ocean bodies. As can be seen on Fig. 1, this domain isconsiderably larger than the 100 by 70 grid-point domain used forCRCM-I by Laprise et al. (1998). In the vertical, 18 unequallyspaced Gal-Chen scaled-height levels were used; the lowest ther-modynamic level is about 170 m above the surface, and the com-putational rigid lid is located near 29 km. A time step of 15 minwas used, with a decentring coefficient of 0.01 in the time-averagingof the semi-implicit algorithm and a running time filter of 0.05.

For nesting of the CRCM-II, the CGCM2-simulated atmo-spheric fields, archived at 6-hourly intervals, were vertically inter-polated from hybrid sigma-pressure coordinate to pressure levels,and horizontally interpolated from Gaussian latitudes and longi-tudes onto the CRCM-II polar-stereographic grid, and then verti-cally interpolated to Gal-Chen scaled-height levels and staggered inthe vertical and horizontal as required; these atmospheric data werethen linearly interpolated in time to nest the CRCM-II at its 15-mintime steps. Geophysical fields over land points, such as liquid andfrozen soil water content, snow amount and ground temperature,were initialised with the monthly mean values from the CGCM2simulation. Once-monthly data of sea surface temperatures andsea-ice cover simulated by CGCM2 were interpolated in space andtime to serve as time-dependent lower boundary conditions forCRCM-II over oceans. As shall be seen below, there are no lakes inCRCM-II. The reason is that North American lakes are not re-solved by the CGCM2 at its resolution, hence no lake temperaturesand ice cover were available as lower boundary condition to the

Fig. 1 Computational domains used for CRCM-I (Solid line, 100by 70 grid points) and CRCM-II (entire figure 120 by 120 gridpoints). The dotted line identifies the edge of the 10-grid-pointsponge zone and 80 by 50 grid-point diagnostic domain forCRCM-I in Laprise et al. (1998). The dashed line delineates a 20-grid-point ribbon surrounding the 80 by 80-grid-point diagnosticdomain for this work. The isolines correspond to the topographicheight field in CRCM-II

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 407

CRCM-II. The next version, CRCM-III, will incorporate aninteractive mixed-layer lake model developed by Goyette et al.(2000), thus permitting a representation of lakes in CRCM.

The GHG & A evolution follows that of Mitchell et al. (1995)and is a modified version of the IPCC (1995) IS92a scenario. TheGHG concentration (Fig. 2) corresponds to the observed ‘‘effectiveCO2 concentration’’ till 1990, after which time it increases at a rateof 1% per year until year 2100. The direct effect of sulfate aerosolsis included in CGCM2 and CRCM-II by increasing the surfacealbedo in a geographically varying fashion, as done in Reader andBoer (1998); the annual average aerosol loading patterns are basedon the slow-oxidation version of the chemistry model of Langnerand Rodhe (1991), as described by Boer et al. (2000).

The CRCM-II simulations were performed for three 10-yeartime slices of transient GHG & A (the red segments on Fig. 2); thisdiffers from the two 5-year periods of constant 1· and 2 · CO2

simulated with CRCM-I in Laprise et al. (1998). Prior to each 10-year time slice, a two-year spin-up was performed to allow time forthe modelled climate system to adjust. Hence the CRCM-II wastherefore run for three 12-year periods starting on January 1 1973,on January 1 2038 and on January 1 2078. The 10-year averageequivalent-CO2 concentrations for the 1975–1984, 2040–2049 and2080–2089 time slices are respectively 437 ppmv, 827 ppmv and1255 ppmv. These three time slices will henceforth be referred to as1 · CO2 (or recent past climate), 2 · CO2 and 3 · CO2 periods. The

10-year statistics were computed for all four ‘‘seasons’’ (defined as3-month periods such as December–January–February for ‘‘win-ter’’, etc.), although the analysis presented in this paper will focuson the winter and summer seasons of the 1 · CO2 and 2 · CO2

periods. For diagnostic purposes, CRCM-II simulated fields werearchived at 6-hourly intervals, with surface fluxes and precipitationcumulated as time integrals between archival times. A 20-grid-pointribbon was removed at the perimeter of the computational domain,corresponding to the 10-grid-point sponge zone plus an additional10 grid points to allow for the spin-up of fine-scale features; theresulting diagnostic domain hence covers 80 by 80 grid points. Thesimulated atmospheric data of CRCM-II and CGCM2 were allinterpolated onto a common set of 14 pressure levels, and CGCM2data were interpolated from Gaussian latitude and longitude ontothe CRCM-II polar-stereographic grid for ease of comparison.

Figure 3 presents the CRCM-II ‘‘ground cover’’ mask, identi-fying the regions of land, open water and sea-ice, and the topo-graphic height field, on the 80 by 80 points diagnostic domain; forcomparison, the corresponding CGCM2 fields (interpolated ontothe CRCM-II grid) are also shown. It can be noted that the RockyMountains and Continental Divide are much more detailed in theCRCM-II with even some partial representation of the OkanaganValley in southern British-Columbia (BC) and some coastalmountains near the Vancouver Island, all absent at the CGCM2resolution. In southern Yukon, the CRCM-II better resolves theMount Logan, with a peak elevation of 2400 m compared to1200 m in the CGCM2. The topography field in CRCM has beensmoothed in order to reduce the numerical truncation error asso-ciated with the use of long time steps that the semi-implicit semi-Lagrangian marching scheme permits (e.g. Hereil and Laprise1996). A drawback of this procedure however is a somewhat de-graded representation of mountains, which has some impact on thesimulation of snow in regions of complex orography, as shall beseen below. Forthcoming versions of the CRCM will try toimprove on the treatment of orography.

3 Results and discussion

Gridded surface climate data from various sources wereused to evaluate the simulated seasonal mean variablesunder current conditions: screen temperature from theClimatic Research Unit (CRU; New et al. 2000), pre-cipitation from Willmott and Matsuura (WM; 1995,2000), snow depths from Brown et al. (2003), and sur-face runoff from Cogley (1998). Simulated seasonalmean screen temperatures, diurnal screen temperaturerange and total clouds were compared with the CRUdatabase; the CRU dataset takes account of station

Fig. 2 Time evolution of the equivalent CO2 concentration inCGCM2 and CRCM-II. The three red segments identify the threetime slices of CRCM-II: the 1975–1984 decade (referred to as the1 · CO2 period), the 2040–2049 decade (2 · CO2 period) and the2080–2089 decade (3 · CO2 period)

Fig. 3 Topographic height andground cover mask of CRCM-II and CGCM2, displayed overthe CRCM-II diagnosticdomain. The following colourcodes are used for the groundcover mask: brown for land,light blue for open ocean andwhite for sea ice of one of theJanuary periods. The CGCM2Gaussian latitude–longitudefields were interpolated onto theCRCM-II polar-stereographicgrid for ease of comparison

408 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

elevation in processing screen temperatures. Simulatedseasonal mean precipitation rates were compared withthe WM database; the WM dataset however did notcorrect for under-catch of precipitation. The observa-tional datasets were interpolated from their originallatitude–longitude grid (1� for the Cogley (1998) dataset,0.5� for the CRU and WM datasets, and 0.3� for theBrown et al. (2003) dataset) onto the CRCM-II 45-kmpolar-stereographic grid. The 1975 to 1984 datasets wereused to compute the 10-year means, to compare thecorresponding recent past climate (1 · CO2) CRCM-IIand CGCM2 simulations in the next sub-section.

3.1 The recent past climate: the 1975–1984 decade

3.1.1 Screen temperature

Figures 4 and 5 present seasonal average screen tem-peratures simulated by the CRCM-II and the CGCM2,

as well as the gridded analyses of surface observationsby the Climatic Research Unit (CRU; New et al. 2000),for winter and summer, respectively. The winter climateover the domain of interest is characterised by a verylarge thermal gradient between the temperate maritimeclimate of eastern Pacific Ocean, where temperaturesrange from 5� to 15 �C, and the continental Arctic cli-mate of the Canadian North-Western Territories(NWT), where temperatures reach below –30 �C (seeCRU; right panel of Fig. 4). In summer, the continentaltemperatures exhibit a gradual variation with latitudeand altitude, ranging from 25 �C in the prairies in thesouthern part of the domain to near freezing at the edgeof the Arctic Ocean (see CRU: right panel of Fig. 5).Not too surprisingly, both models are successful insimulating the major characteristics of spatial distribu-tion and seasonality contrasts. Also expectedly, due totheir higher resolution, the CRCM-II fields display finerscale variations, especially over steep topographicalfeatures such as the Rocky Mountains.

Fig. 4 Winter-mean screen-level temperatures simulated by CRCM-II (left), CGCM2 (centre) and analysed by the Climatic Research Unit(CRU; New et al. 2000) (right). The contour interval is 5 �C

Fig. 5 Summer-mean screen-level temperatures simulated by CRCM-II (left), CGCM2 (centre) and analysed by CRU (right). The contourinterval is 5 �C

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 409

In winter, the CRCM-II simulation suffers from ageneralised warm bias of about 5 �C over the westernpart of the domain (between 2–6 �C over the CanadianPrairies) reaching 10� over southern Yukon. There arehowever some regions where the CRCM-II is somewhattoo cold, such as Wyoming (–6 �C) and the northernsector of the NWT. A comparison of CRCM-II simu-lated minimum and maximum temperatures againstCRU data reveals that the warm bias is mostly the resultof too warm simulated minimum temperatures. As anexample, the minima are too warm over central BC by asmuch as 16 �C, compared to the maxima that are toowarm by only about 2 �C there. CGCM2 also has awarm bias over Canada; compared to CRCM-II, thisbias is less pronounced over BC but more pronouncedover the Canadian Prairies and NWT.

In summer, the CRCM-II is substantially warmerthan the observations at the northern edge of the NWT,but overall the warm bias of the CRCM-II is generallysmall; there are even generalised regions of negligible orcold biases west of the Continental Divide. This smallbias however is somewhat coincidental, resulting fromcompensating errors in the minimum and maximumtemperatures. In the CRCM-II, maxima are generallytoo cold and minima too warm over most of the regionwest of the Continental Divide; as shall be seen later, this

results in part from the excessive cloud cover. In sum-mer, the CGCM2 temperatures are generally somewhatbetter than those of the CRCM-II, except over thenorthwestern USA where the cold bias is nearly dou-bled, reaching almost 10 �C.

Figure 6 presents the seasonal average diurnal tem-perature range (DTR) simulated by the CRCM-II, aswell as the analyses of observations by the ClimaticResearch Unit (CRU), for winter and summer. TheDTR is simply the difference between the monthlyaverages of daily maximum and minimum temperaturesfurther averaged for the appropriate season. The DTRintends to measure the amplitude of the diurnal cycle butis also affected by the temperature variations associatedwith the passage of synoptic weather systems. Over theregion of interest, the spatial pattern of DTR exhibitsthe combined effect of two influences: a northward de-crease associated with the smaller solar zenith angle(understandably more pronounced in summer than inwinter), and a decrease towards the West Coast due tothe important cloud cover there (see CRU; bottompanels of Fig. 6). In the CRCM-II, the DTR values arenoticeably too small almost everywhere, but the large-scale spatial pattern does reproduce the overall charac-teristic of the observations. A part of the simulated DTRdeficit may be an artefact of the definition of screen

Fig. 6 Diurnal temperaturerange (DTR) at screen level assimulated by CRCM-II (top)and as analysed by CRU(bottom), for winter (left) andsummer (right). The contourinterval is 2 �C

410 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

temperature in CRCM-II as discussed below. The overlylarge simulated soil water amounts, that result in largeground thermal inertia, thus contribute to reduce theDTR in CRCM-II. It can be noted that the model’sDTR values become extremely small in regions such asthe West Coast, with thick cloud cover; this negativecorrelation between small DTR and large cloud coverhas been well documented at both the regional andglobal scales (Karl et al. 1993).

Before concluding, we note that the CRCM-II errorin average screen temperature is less important over theWest Coast, precisely where observation stations aremore numerous; part of the CRCM-II’s thermal biasesin the NWT and in the Rocky Mountains can beattributed to the sparse station network. Since theobserving stations in the NWT are mainly located alongthe coast (see: Fig. 2 in New et al. 2000), the coolsummertime marine temperatures may spread inside thecontinent in the gridding operation, i.e. coastal stationsmay have large influence radii because of the lack ofcontinental stations in the area. Such station configu-ration may produce gridded data with too cool summertemperatures in the NWT and make the CRCM-II warmbias appear worse than it really is.

The errors in the CRCM-II simulated average screentemperature result from the combined effects of severalsources of modelling and diagnostics errors. Errors inthe simulated cloud cover will affect the solar and ter-restrial radiative fluxes entering the surface energybudget. Errors in the simulated ground water will resultin an incorrect partitioning of sensible and latent surfacefluxes, producing an erroneous surface temperature.Such errors may compound those due directly to thesimplified treatment of land-surface energy budget, andto the absence of lakes that contribute to moderatetemperatures. In addition to these are the errors in thecirculation of the nesting CGCM2. Furthermore, screentemperature is not a prognostic variable in CRCM-IIand CGCM2: it is calculated diagnostically from theland–ocean-ice surface temperature and the lowest

thermodynamic atmospheric prognostic level, as inGCMii (McFarlane et al. 1992). It has been noted thatthis diagnostic estimate of screen temperature is poorunder conditions of strong inversion, which are frequentin continental climates over night and in winter. Finally,the definition of average screen temperature differs be-tween the observations and the model results. Inobservations, it is defined as the mean of daily minimumand maximum temperatures; in the model it is obtainedas the average of six-hourly archived temperatures.Cursory analysis of this aspect has revealed that the twomethods may produce results differing by as much as2 �C, with either signs. The next version of the CRCMwill produce a diagnostic of average screen temperaturein accordance with that of the observations.

3.1.2 Precipitation

Figures 7 and 8 present the seasonal average precipita-tion rates simulated by the CRCM-II and the CGCM2,as well as the gridded analyses of observations byWillmott and Matsuura (WM; 1995, 2000), for winterand summer, respectively. From the point of view ofprecipitation, the region of interest is characterised byfour vastly distinct climate zones: the western coast ofNorth America (including the Coastal Range), thewestern slopes of the Continental Divide, a valley sep-arated by these two mountain systems, and the plains tothe east of the Continental Divide (see WM; right panelsof Figs. 7, 8). The western coast of North America andthe western slopes of the Continental Divide are char-acterized by winter seasons that are very wet and wet,respectively. The middle valley is fairly dry with littleseasonal variation of precipitation. The plains to the eastof the Continental Divide are dry and receive theirmaximum precipitation in summer.

Overall, both models capture well these seasonal andgeographical contrasts of precipitation. Owing to itshigher resolution topography, the CRCM-II is able to

Fig. 7 Wintertime average precipitation rates simulated by CRCM-II (left), CGCM2 (centre) and analysed by Willmott and Matsuura(WM; 1995, 2000) (right). The contour intervals are irregular, starting at 1 mm da–1 for small values

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 411

reproduce with fidelity the narrowness of the precipita-tion band on the edge of the west Coast and on thewindward side of the Coastal Range and ContinentalDivide, and its correct seasonal variation. Over theprairies CRCM-II precipitation amounts are excellent inwinter; in summer however CRCM-II precipitationamounts are too large but their distribution is bettersimulated than in CGCM2. In Yukon in summer and inthe inter-mountain valley system in general, CRCM-IIprecipitations are too abundant, probably due to the useof smoothed topography that reduces the rain shadow ofthe mountains. The contrast of precipitation between theWest Coast and the Prairies is well simulated byCGCM2, albeit at low resolution. Hence, the narrowband of precipitation over the west coast is smeared inCGCM2, and its maximum intensity is under-estimated.The minimum precipitation in the inter-mountain valleysystem cannot be resolved by CGCM2. The CGCM2precipitation is excessive over the Canadian Prairies andNWT in summer, probably as a result of the under-estimated height of the Continental Divide as repre-sented with the coarse resolution of CGCM2, resultingin insufficient drying in the downslope part of the flow.

3.1.3 Snow depth

The CRCM-simulated current-climate snow depthswere verified against North American gridded dataanalyses made by Brown et al. (2003). Following acritical rescue of Canadian in situ snow-depth mea-surements (Brown and Braaten 1998) and with access tothe important USA measurement network, Brown et al.(2003) proceeded to produce objective analyses of dailysnow depths over North America. Their approach alsomade use of daily precipitation and screen temperaturedata produced as part of the ECMWF (EuropeanCentre for Medium-Range Weather Forecasts) reanal-ysis procedure. Gridded snow depths analyses areavailable as monthly climatology from the 1979–1997

period. The Brown et al. (2003) data were interpolatedfrom their original 0.3� latitude–longitude grid onto theCRCM-II 45-km polar-stereographic grid. The prog-nostic variable for surface snow in the CRCM-II andCGCM2 models is snow amount in units of kg m–2,which is roughly equivalent to millimetres of equivalentwater thickness. The models’ snow amounts were con-verted to approximate snow depths in order to becompatible with the surface measurements and griddedanalyses of Brown et al. (2003). A snow density value of160 kg m–3 was used; this value corresponds to averagesnow density for taiga and maritime regions in the snowclassification of Sturm et al. (1995), and this value israther on the low end of the range of values of snowdensities (120 to 500 kg m–3) estimated by Brown et al.(2003).

Figures 9 and 10 show snow depths simulated by theCRCM-II and the CGCM2, as well as the griddedobservational dataset of Brown et al. (2003), for winterand summer, respectively. The middle- and high-latitudeclimate of the region in this study is characterised by alarge seasonal variation in snow cover. Only few areaskeep snow in summer, such as the most elevated parts ofthe Continental Divide; snow depths of a few millimetresare also found in the extreme northeastern areas of theNWT (see Brown et al. 2003; right panel of Fig. 10).Conversely, few regions in the domain of interest do nothave some snow in winter; southwestern BC and thewesternmost states of the USA remain snow free (seeBrown et al. 2003; right panel of Fig. 9).

Both models simulate the seasonal variation of snowamounts with some fidelity. In winter, simulated maxi-mum snow depths over high terrain correspond to therespective models’ representation of the ContinentalDivide, and a broad region with moderate snow depthsin the NWT extends southward in the Canadian Prairieswest of Hudson Bay. Small-scale details in the snowsimulated by the CRCM-II over the Rocky Mountainsreflect the model’s finer resolution and higher localtopographic height, but values are still somewhat

Fig. 8 Summertime average precipitation rates simulated by CRCM-II (left), CGCM2 (centre) and analysed by WM (right). The contourintervals are irregular, starting at 1 mm da–1 for small values

412 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

smaller than the Brown et al. (2003) analyses. The majorweakness of the CRCM-II simulated snow depth is thelack of snow cover along the coastal mountains of BCand northwestern USA; this defect is likely due to thedegraded mountain features resulting from topographicsmoothing in the CRCM as explained earlier. In theCanadian Prairies and NWT, the snow depth is sub-stantially less in the CRCM-II than in the CGCM2. Bycomparison with the Brown et al. (2003) analyses, theCGCM2 snow depths seem better than the CRCM-II inthe Prairies, but become too important in the NWT. Thelight-snow line just east of the Continental Divide is wellsimulated by the CRCM-II.

In summer, substantial snow remains in the CRCM-II over the Mount Logan area (in southern Yukon) as aresult of the model’s higher elevation (2400 m inCRCM-II Vs 1200 m in CGCM2), in accord with theBrown et al. (2003) analyses. In the CGCM2, more than50 cm of snow persists west of Hudson Bay, which ap-pears to be excessive according to the Brown et al. (2003)

analyses, although these analyses are known to under-estimate snow depths in the area.

3.1.4 Summary of the simulation of the 1975–1984decade

Both the CRCM-II and the CGCM2 successfullyreproduced several features of the geographical distri-bution and seasonal variation of the observed climate.Several terrain-induced features of the climate that aremissed by the CGCM2 due to its coarse spatial resolu-tion are captured by the CRCM-II. The systematicbiases of CRCM-II appear to have been reduced com-pared to the results obtained with the earlier version(CRCM-I) by Laprise et al. (1998), although severalaspects still leave a lot to be desired. The errors in theCRCM-II simulation result from the combination ofseveral contributions: modelling, nesting and diagnos-tics. Modelling errors origin from approximations,

Fig. 9 Wintertime average snow depth simulated by CRCM-II (left), CGCM2 (centre) and analysed by Brown et al. (2003) (right). Thecontour intervals are irregular, starting at 10 cm for small values

Fig. 10 Summertime average snow depth simulated by CRCM-II (left), CGCM2 (centre) and analysed by Brown et al. (2003) (right). Thecontour intervals are irregular, starting at 10 cm for small values

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 413

especially in the physical parameterisation package.Moreover, when considering nesting errors, one mustnot forget that the RCM is affected by biases in thenesting GCM (Rummukainen et al. 2001; Pan et al.1999; Giorgi et al. 1995); these biases are not presentwhen nesting with objective analyses (e.g. Biner et al.2000; Frigon et al. 2002). Finally, diagnostics errors re-sult from comparing somewhat different quantities (asalluded to already for cloud cover, screen temperatureand the definition of daily average screen temperature)and from the limited knowledge of the real climateafforded by the gridded analysed climatologies.

Soil moisture content (not shown) is markedly dif-ferent between CRCM-II and CGCM2, despite the factthat both models share the same surface scheme. De-tailed observations lack to assess the relative meritsof each model in this respect. The drier soils in theCRCM-II simulation are consistent with warmer sum-mer screen temperatures (Fig. 5) and reduced cloudcover (not shown) in this model compared to CGCM2.The drier values in the CRCM-II are also partly theresult of an incomplete transmission of the precipitationto the surface hydrology scheme in the CRCM-II com-puter code; this error was only uncovered after thecompletion of these extensive simulations through adetailed examination of the water budget. As seen laterin Section 3.3, this coding error has luckily only limitedimpact on the overall hydrological cycle budget.

3.2 The 2 · CO2 climate: the 2040–2049 decade

In this section, we focus on the climate change ‘‘delta’’between the two following 10-year periods: the 1975–1984, i.e. the 1 · CO2 period, and the 2040–2049, so-called 2 · CO2 period. The climate change ‘‘delta’’simulated by the CRCM-II will be compared to thatsimulated by the CGCM2 for the winter and summerseasons.

We want to emphasise clearly that we do not pre-tend (at this stage) that the following simulated cli-mate changes have any predictive skills at the regionalscale. We are mostly interested in understanding thedifferences between two climates simulated under dif-ferent forcing by two models with different resolutionsbut a common parameterisation of several physicalprocesses. An important effort of climate-modellingresearch is devoted to trying to understand themechanisms responsible for the changes and theirdifferences.

3.2.1 Screen temperature, snow depthand albedo feedback

Figure 11 presents maps of screen temperature deltas forwinter and summer as simulated by the CRCM-II andthe CGCM2. Both models agree in simulating an in-creased warming from the south-west corner of the

domain, with less than 2 �C over the Pacific Ocean, to-wards the north-east corner, with about 4 �C in summer,and up to 7 �C in winter over the NWT. The CRCM-IIwarming is noticeably weaker than that of the CGCM2over central Alberta and Saskatchewan where the dif-ferences exceed 2 �C. The average computed over thedisplayed domain of the screen temperature deltas sim-ulated by the CRCM-II and the CGCM2 indicate quitesimilar values of warming however: 2.6 and 2.7 �Cannually, 3.3 and 3.6 �C in winter, and 2.1 and 2.3 �C insummer, for the CRCM-II and the CGCM2, respec-tively.

The CRCM-II delta is modulated by topographicalheight, especially in winter: for example the warming isless than 1 �C in the low-elevation BC interior and morethan 2 �C over high-elevation Rocky Mountains of BC.The same effect is also noted in southern Idaho, withmore pronounced amplitude. As we shall see later, thechange of snow, and its albedo feedback, is largelyresponsible for this elevation amplification. Altitudeamplification was also noted over the Rocky Mountainsin a climate-change simulation made with CGCM1 byFyfe and Flato (1999). Such an altitude amplification ofclimate trends over the Alps had also been noted earlierby Beniston and Rebetez (1996) in the records of ob-served surface temperatures, and by Giorgi et al. (1997)in the simulated results of the regional climate modelRegCM.

Next, we turn our attention to the delta in wintersnow depth as simulated by the CRCM-II and theCGCM2. Because the delta of snow depth appears to behighly correlated with the snow depth itself, the relativechange of snow depth (relative to 1 · CO2 climate val-ues) appears to be best suited to display the informationin Fig. 12. The overall patterns of relative change aresimilarly simulated by the CRCM-II and the CGCM2.A vast region with relative reduction between 50 and100% covering the southwestern part of the domainbecomes virtually snow-free in the 2 · CO2 climate, andonly the highest parts of the Rocky Mountains retainany snow. The relative reduction in snow depth de-creases from its maximum in the southwestern part ofthe domain to reach zero in some parts of the NWT,Alaska and Yukon. It is interesting to note that, despitea widespread decrease of winter snow in the warmerclimate, some regions receive an increase. This is the casefor the region at the northern edge of the NWT, Yukonand Alaska. This increase of snow depth is caused by anincrease of winter precipitation, as seen below in Sect.3.2.2 (Fig. 13).

Summertime snow-depth deltas are not very inter-esting and are not shown. Most of the domain of interestis snow-free even in today’s climate, save for the highground around Mount Logan in southern Yukon and,in the CGCM2 where it is rather excessive, over theeastern part of the NWT, west of Hudson Bay. Overthese two areas, snow decreases under 2 · CO2 forcingand vanishes over the high ground around MountLogan in the CGCM2. In the 1 · CO2 climate simulated

414 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

by the CGCM2, the excessive amount of snow west ofHudson Bay (more than 60 cm in winter) is so large thatmore than 30 cm remain in summer (Fig. 10). In the 2 ·CO2 projection, almost all this summer snow disappears,resulting in a large change of snow depth. A concomi-tant decrease in surface albedo is responsible for thelarge climate-change warming found west of HudsonBay in the CGCM2: up to 5 �C compared to about 3 �Cin the CRCM-II. This is an example of a case where a

poor current-climate simulation can result in an unlikelyclimate-change signal, thus the paramount importanceof a faithful reproduction of current conditions by cli-mate models.

Another area of difference between the two modelsimulations in winter screen temperature delta is notableat the northern edge of Alaska (Fig. 11); the CGCM2simulates an area of minimum warming reaching valuesless than 2 �C, whereas the CRCM-II simulated

Fig. 12 The climate-changerelative delta of winter snowdepth (in percent of the 1 · CO2

period values) as simulated byCRCM-II (left) and CGCM2(right). The contour intervalsare 0, +/–10, +/–25, +/–50and +/–100%

Fig. 11 The climate-changedelta of screen-leveltemperature as simulated byCRCM-II (top) and CGCM2(bottom), for winter (left) andsummer (right). The contourintervals are irregular, startingat +/–1 �C for small values

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 415

warming is 3 to 5 �C. Figure 9 showed that the CGCM2simulates winter snow depths between 60 and 120 cm inthat region for the 1 · CO2 climate, compared toCRCM-II values of about 50 cm. Under a scenario ofincreased GHG, the decreased snow depth for bothmodels (not shown) is about 10 cm on average. Thisdecrease in snow is sufficient to change the winter sur-face albedo in the CRCM-II but insufficient to appre-ciably change the CGCM2 albedo, hence the smallertemperature delta in the CGCM2 simulation. The sametype of snow-albedo feedback argument applies to ex-plain the larger winter climate-change warming simu-lated by the CRCM-II in southern Idaho. A similarsituation is found in winter over the Canadian Prairieswhere the CGCM2 has a climate-change warming of5 �C compared to around 3 �C in the CRCM-II; this isrelated to the snow depth reduction that is larger in theCGCM2 than in the CRCM-II.

3.2.2 Clouds, precipitation and ground water

Figure 13 presents the relative changes of winter andsummer precipitation (relative to 1 · CO2 climate val-ues) simulated by the CRCM-II and the CGCM2. Theserelative changes, ranging between –30% and +40% in

both models and both seasons, correspond to precipi-tation changes of less than +/–1 mm da–1. Over land,the signal is weak and poorly organised, although thereis a tendency for a small decrease of precipitation inboth models and for both seasons over two regions:southern BC and an area around or to the southwest ofthe Athabasca, Great Slave and Great Bear lakes. Theseresults contrast sharply with previous simulations ob-tained with CRCM-I nested within GCMii (Laprise et al.1998) in which winter precipitation changes over Van-couver (BC) reached values of +3 and +5 mm da–1 inthe GCMii and the CRCM-I, respectively. The factorexplaining the GCM differences is that the GCMii sim-ulation corresponded to equilibrium conditions and wascoupled with mixed-layer ocean and thermodynamicsea-ice models, while the CGCM2 simulation was per-formed for a transient GHG & A scenario and wascoupled with dynamical ocean and ice models. TheseGCM differences were then passed onto the regionalmodel through the nesting of atmospheric variables.There is a noticeable increase of precipitation over thenorthern part of the domain, especially in winter and inthe CRCM-II simulation. This increase is associated tothe decreased sea ice off the north coast of NorthAmerica. The open waters can lead to decreasedboundary layer stability and increased evaporation,

Fig. 13 The climate-changerelative delta of precipitation(in percent of the 1 · CO2

period values) as simulated byCRCM-II (top) and CGCM2(bottom), for winter (left) andsummer (right). The contourintervals are 0, +/–10, +/–20,+/–40, +/–60, +/–80 and+/–100%

416 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

leading to increased convective precipitation near thecoastal waters. Perhaps not too surprisingly, cloud coverchanges (not shown) are fairly small, less than +/–10%everywhere in both models and for both seasons, andthese changes relate only weakly to the changes in pre-cipitation. The overall pattern of total soil water fractionvaries little between the 1 · CO2 and the 2 · CO2 sim-ulations (not shown); the amplitude of changes canreach values of –40% and +30% locally, but the pat-terns of change are not well defined.

3.3 The hydrological cycle and its climate change

In this section, we turn our attention to the hydrologicalcycle and its climate change as simulated by theCRCM-II and the CGCM2. The hydrological budgetwill be averaged over four selected regions locatedwithin the diagnostic domain (80 by 80 grid points). Thisintegrative approach has the merit of providing morerobust statistics than point-wise calculations. The do-main of interest comprises several drainage basins thatmay be grouped on the basis of the ocean in to whichtheir rivers drain. For the case at hand, some basinsdrain into the Pacific Ocean (e.g. Columbia, Fraser,Yukon and several smaller West Coast basins), into theArctic Ocean either directly or via Hudson Bay (e.g.Mackenzie, Churchill, Nelson and several others in theNWT), and into the Atlantic Ocean via the Gulf ofMexico (e.g. Mississippi). For our purposes, the first setof land basins west of the Continental Divide draininginto the Pacific Ocean will be called the ‘‘West Land’’,and the remaining sets located east of the ContinentalDivide will be grouped into one region called the ‘‘EastLand’’ (Fig. 14).

For each of the four regions identified in Fig. 14(Pacific Ocean, West Land, East Land and ArcticOcean), vertically integrated water budget equationsmay be written for the atmosphere and the surface asfollows:

dtWa ¼ þC � P þ E

dtWs ¼ �Rþ P � E

(e.g. equations 12.12 and 12.1 of Peixoto and Oort 1992,respectively). In these equations, Wa refers to the verti-cally integrated atmospheric water content (or precipi-table water) and Ws is the total surface water content(over land, this is composed of liquid and frozen groundwater, plus snow); R refers to the runoff and C to thevertically integrated atmospheric water flux convergence(or atmospheric ‘‘run-in’’ as some say); and P and E referto precipitation and evaporation, respectively. Upontime averaging over several annual cycles, the time ten-dency terms normally become very small, so that theknowledge of P and E suffices to draw some informationabout the water cycle without an explicit evaluation ofthe transport contributions R and C. The atmosphericand surface flux convergencesC and –Rmay be written as

C ¼ � ~rr �~FFa � �DFa and �R ¼ � ~rr �~FFs � �DFs

and integrated over each of the four regions to yield thetrans-region boundary fluxes, ~FFaand ~FFs; knowledge ofone of the boundary fluxes can yield the others bycontinuity. Figure 15 gives a schematic view of the fourregions and the values of the terms in the equation for 1·and 2 · CO2 simulations of the CRCM-II and theCGCM2. In this figure the values for R, C, P and E areexpressed as an amount per unit area (in units ofkg m–2 an–1) whereas the fluxes ~FF are given as theamount for the region (in units of km3 an–1). This pro-cedure, which is common practice for land area, appliesequally well to ocean surface where the quantity dtWs isidentically zero, and �R ¼ � ~rr �~FFscorresponds to theintegrated effect of ocean currents and sea-ice motion.

Consider first the numbers for the 1 · CO2 simula-tions of CRCM-II (upper line) and CGCM2 (lower line),given in the left column of each block of six numbers onFig. 15. Both models similarly capture the distinct cli-mate regime of the two land regions, the West and theEast Lands. This is clearly evidenced by the drasticdifference in net water budget, with values of positive P– E (corresponding to atmospheric flux convergence andsurface flux divergence) of 716 and 746 kg m–2 an–1 inthe West Land and 143 and 262 kg m–2 an–1 in the EastLand, for CRCM-II and CGCM2 respectively. For bothland regions, P – E from the CGCM2 is more importantthan that from the CRCM-II and the larger difference inthe East region is related to CGCM2’s greater precipi-tation intensity. The corresponding fluxes ~FFs give the

Fig. 14 The four regions used for performing the hydrologicalbudget. The regions identified as ‘‘West Land’’ and ‘‘East Land’’are separated by the Continental Divide, so that West Land drainsinto the Pacific Ocean and East Land drains into the Arctic Oceanand partly into the Gulf of Mexico

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 417

values of 2294 and 2393 km3 an–1 for total river dis-charge of West Land into Pacific Ocean and 904 and1552 km3 an–1 for total river discharge of East Landinto Arctic Ocean and Gulf of Mexico, for CRCM-IIand CGCM2 respectively. Interestingly enough, the twowater regions in the domain of interest also have a waterbalance with positive P – E although with very differentintensities: the Pacific Ocean has values of 788 and600 kg m–2 an–1, compared to lesser values of 231 and353 kg m–2 an–1 for the Arctic Ocean (and Gulf ofMexico), in CRCM-II and CGCM2 respectively. Thismeans that there is net atmospheric water convergence,and correspondingly net land runoff and divergent oceancurrents, over the entire domain of interest. It is note-worthy that the average evaporation over the regionlabelled ‘‘Pacific Ocean’’ is substantially different in theCRCM-II and CGCM2 (428 and 622 kg m–2 an–1

respectively), despite the fact that both models use thesame sea surface temperature values. The annual aver-age evaporation maps (not shown) reveal that theCRCM-II develops a narrow region of low evaporation(with values less than 1 mm da–1) just off the WestCoast. This region is too narrow to be resolved by theCGCM2; furthermore its land-sea mask considers asland part of that region that is actually ocean (seeFig. 3).

Available runoff estimates from Cogley (1998) wereinterpolated from their original 1� latitude–longitudegrid onto the CRCM-II 45-km polar-stereographic grid;

these gave values of 2250 and 768 km3 an–1 for the Westand East Land regions, respectively. By comparing theseobserved estimates with total river discharge from bothmodels (given already), we find that CRCM-II has asomewhat better skill than the CGCM2. More specifi-cally, total river discharge from CRCM-II is quite goodin the West Land (with a value of 2294 km3 an–1) butbecomes too large on the East Land (with a value of904 km3 an–1).

Integrating the water flux divergence over the entiredomain of interest gives the following figures for the do-main-averaged atmospheric water flux convergence (orsurface flux divergence): DFs = 5939 and 6116 km3 an–1

for CRCM-II and CGCM2, respectively. If the steady-state assumption were valid for the 10-year average of thesimulations, and given that the CRCM-II is nested by theCGCM2 atmospheric data, these figures should ideally beidentical in the CRCM-II and the CGCM2. The 3%difference is a measure of the inconsistencies associatedwith the nesting technique, including the several inter-polations required as part of the pre- and post-processingof model data. In addition, a programming error wasfound in the transfer of precipitation to the land surfacescheme in CRCM-II as mentioned previously in Sect.3.1.4. As a result, there was a detectable trend in theground water content in the course of each CRCM-II 10-year time slice over the East Land region, amounting toabout –10 kg m–2 an–1 (or –60 km3 an–1; not shown); nosuch imbalance could be identified in the West Land

Fig. 15 Summary of the terms in the annual average hydrologicalbudget simulated by CRCM-II and CGCM2, for the 1 · CO2 and2 · CO2 periods, as well as their difference, over each of the fourregions (R, C, P and E are in units of kg m–2 an–1), and the trans-region fluxes ( ~FF and D~FF in units of km3 an–1). By definition of theContinental Divide, the fluxes are identically zero there, and by

convention the fluxes are positive (negative) when directed in theeastward (westward) direction. Each group of six numbers give thefigures for the CRCM-II (upper line) and CGCM2 (lower line), forthe 1 · CO2 period (left column), 2 · CO2 period (centre column)and the climate-change delta (right column)

418 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model

region. By itself, the East Land region trend found in theCRCM-II only amounts to 34% of the inter-model dis-agreement (=177 km3 an–1).

Figure 15 also gives the values corresponding to the2 · CO2 simulations of CRCM-II (upper line) andCGCM2 (lower line) in the central column of eachblock of six numbers, and the climate-change ‘‘delta’’in the right-hand side column. The two models simu-late a similar climate-change increase of P – E in thewestern part of the domain, but disagree or give adecrease of P – E in the eastern part of the domain.For example, the deltas of P – E are 49 and 33 kg m–

2 an–1 for the Pacific Ocean region, and become +2and –32 over the Arctic Ocean region, for CRCM-IIand CGCM2 respectively. In the Pacific Ocean andWest Land regions, both P and E increase, but Pincreases more. Over the East Land and Arctic Oceanregions, both P and E increase, but models disagreeon the relative amounts: the CRCM-II predicts asomewhat larger increase in P than in E, while theCGCM2 predicts a rather larger increase in E com-pared to P, hence the different signs of deltas.

The P–E deltas become increasingly different east-ward and northward between the two models; whilethe small size of the Arctic Ocean region may accountfor the larger differences, this argument cannot beapplied to the East Land where the difference in deltasis also large despite the large surface area of this re-gion. When integrated over the entire domain ofinterest, the domain-averaged atmospheric water fluxconvergence (and surface divergence) deltas are only413 and 22 km3 an–1 for CRCM-II and CGCM2,respectively, corresponding to 7% and 0.4% of the 1 ·CO2 flux convergence values.

4 Conclusion

This study presented the results of climate simulationsperformed with the second-generation Canadian Re-gional Climate Model (CRCM-II) on a 45-km grid meshover a region covering north-western North America,nested in the coupled Canadian General CirculationModel (CGCM2, Flato and Boer 2001), for two 10-yeartime slices in a scenario of transient greenhouse gasesand aerosols (GHG & A). The new versions of modelscomprise several changes that were made to earlierversions of both the regional model (CRCM-I, Lapriseet al. 1998) and the global model (GCMii; Boer et al.1992); the most important changes in the CRCM-II arerelated to the parameterisation of moist processes andthe use of a larger regional domain, while the couplingwith dynamical ocean and ice models constitutes asalient change in the CGCM2. In addition CRCM-IIand CGCM2 simulations were performed following atransient GHG & A scenario, as described in Boer et al.(2000), whereas the CRCM-I and GCMii simulationswere performed for equilibrium 1 · CO2 and 2 · CO2

conditions.

For the time slice corresponding to recent-past cli-mate conditions, both the CRCM-II and the CGCM2reproduce the main features of the climate over the re-gion of interest, with additional spatial definition in theCRCM-II owing to its finer computational grid. A warmbias in the screen temperature of the CRCM-II wasidentified for several parts of the domain and seasonsalong with a systematically too small diurnal tempera-ture range (DTR); these problems appear to resultmainly from overly warm overnight minimum temper-atures, which points to an excess of stratiform clouds inCRCM-II. The precipitation rates are in general bettersimulated in CRCM-II than in CGCM2, in part owingto the finer computational grid of CRCM-II, but alsobecause of changes in the moist convective parameteri-sation from CRCM-I. Snow depths (as estimated fromsnow amounts in CRCM-II and CGCM2) were com-pared with available analyses over North America,revealing a fairly successful simulation of CRCM-II.Soil water content and total cloud cover exhibited ratherimportant differences between the regional and globalmodels, but few data are available to verify the respec-tive skill of models. For current climate conditions, thelatest simulation of CRCM-II represents overall amodest, but general, improvement over the earlier re-sults obtained with CRCM-I nested with GCMii. Theclimate-change ‘‘delta’’ between two decades, the 1975to 1984 decade (1 · CO2) and the 2040 to 2049 decade(2 · CO2), as simulated by the CRCM-II and theCGCM2, were compared. Overall the regional modeldeltas agree with that of the nesting global model, butthere are substantial local differences for some fields.Most of the differences between deltas could be tracedback to differences in the simulated reference (1 · CO2)climate, often related to different topographic heightsresulting from different computational resolutions.

A simplified hydrological budget was performed forthe two simulated decades by CRCM-II and theCGCM2. It was done by spatially integratingthe hydrological contributions over four sub-regions ofthe domain of interest corresponding to drainage basinsand adjacent seas. While both models capture the es-sence of the vastly differing hydrological regimes of eachsub-regions, there are also some surprising differencespointing to inconsistencies between the nested regionaland the nesting global model. The limited availableobservational evidence from runoff data, points to aslight advantage of the CRCM-II over the CGCM2 inreproducing the observed hydrological balance. In con-clusion, it is important to restate a feature of nestedRCMs; the global model atmosphere and ocean climatebiases are passed onto the regional model through thenesting of the atmospheric fields in the regional model.This point must serve as a reminder of the paramountimportance of quality GCM nesting data in climate-change downscaling studies with RCMs. Because of thedifferent GCM climates, and also in part because ofchanges to the regional model itself, the results simulatedwith the CRCM-II nested by CGCM2, as presented

Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 419

here, differ noticeably from those obtained with theCRCM-I nested by GCMii, as reported by Laprise et al.(1998).

Owing to the limited size of the decade-long simula-tions, the question of statistical significance of the sim-ulated differences, either between the regional and globalmodels for current climate conditions and observations,or between the climate-change deltas simulated by eithermodel, has been completely alleviated. Such study willconstitute the endeavour of forthcoming work.

Finally, as mentioned in the description of theexperimental protocol, a third 10-year time slice corre-sponding roughly to 3 · CO2 has also been realised. Thisperiod has not been analysed here to conserve space. Thesimulated data are available as mentioned before at thefollowing electronic address: http://www.cccma.-bc.ec.gc.ca/data/rcm.shtml.

Acknowledgements The authors want to thank their colleagues ofthe Canadian Regional Climate Modelling group at UQAM,Mr Michel Giguere, Ms Helene Cote, Mr Sebastien Biner andDr. Pascale Martineu, for their contributions that made it possiblefor us to complete this work. We also thank Claude Desrochers formaintaining a user-friendly local computing environment at theDepartment of Earth and Atmospheric Sciences of UQAM. Wewould like to express our gratitude to the Climatic Research Unit(CRU) of the University of East Anglia, for the use of theirobservation analyses; the CRU 0.5� latitude–longitude griddedmonthly climate data was supplied by the Climate Impacts LINKProject (UK Department of the Environment Contract EPG 1/1/16) on behalf of the CRU. We also want to thank Mr Ross D.Brown of the Meteorological Service of Canada for providing uswith the North American gridded snow data in a convenient for-mat. The collaboration of the Canadian Centre for Climate Mod-elling and Analysis (CCCma) in Victoria BC is warmlyacknowledged. The availability of Drs Francis Zwiers, GeorgeJ. Boer, Norman A. McFarlane and Gregory M. Flato for dis-cussing modelling and diagnostics issues, was kindly appreciated.Without free access to CCCma’s software, CGCM2-simulated dataand allocation on the super-computing facility at the Centre d’In-formatique de Dorval, this project would not have been possible.This research was financially supported by the MeteorologicalService of Canada (MSC), through funding of the Canadian Cli-matic Research Network (CRN) operated by the Canadian Insti-tute for Climate Studies (CICS), by the Canadian National Scienceand Engineering Research Council (NSERC), through a StrategicProject grant, by the Canadian Foundation for Climate andAtmospheric Sciences (CFCAS), and by the Universite du Quebeca Montreal (UQAM).

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