Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model (CRCM-II) over northwestern North America

Transcription

1 Climate Dynamics (2003) 21: DOI /s R. Laprise Æ D. Caya Æ A. Frigon Æ D. Paquin Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model (CRCM-II) over northwestern North America Received: 8 July 2002 / Accepted: 25 April 2003 / Published online: 23 August 2003 Ó Springer-Verlag 2003 Abstract An updated version of the Canadian Regional Climate Model (CRCM-II) has been used to perform time-slice simulations over northwestern North America, nested in the coupled Canadian General Circulation Model (CGCM2). Both driving and driven models are integrated in a scenario of transient greenhouse gases (GHG) and aerosols. The time slices span three decades that were chosen to correspond roughly to single, double and triple current GHG concentration levels. Several enhancements have been implemented in CRCM-II since the CRCM-I climate-change simulations reported upon earlier. The larger computational domain, extending further to the west, north and south, allows for a better spin-up of weather systems as they enter the regional domain. The increased length of the simulations, from 5 to 10 years, strengthens the statistical robustness of the results. The improvements to the physical parameterisation, notably the moist convection scheme and the diagnostic cloud formulation, cure the excessive cloud cover problem present in CRCM-I, reduce the warm surface bias and prevent the occurrence of grid-point precipitation storms that occurred with CRCM-I in summer. The dynamical ocean and sea-ice components of CGCM2 that is used to provide atmospheric lateral and surface boundary conditions to CRCM-II, as well as the use of transient rather than equilibrium conditions of GHG and the inclusion of direct aerosols forcing, in both CGCM2 and CRCM-II, increase the realism of the CRCM-II climate-change simulation. R. Laprise (&) Æ D. Caya Æ A. Frigon Æ D. Paquin CRCM/UQAM Ouranos, 550 West Sherbrooke St, 19th floor, Montréal (Que bec), Canada H3A 1B9 laprise.rene@uqam.ca 1 Introduction General circulation models (GCMs), including landsurface processes and coupled with dynamical ocean and sea-ice models (hereinafter termed CGCMs), currently provide the most sophisticated, physically based approach to simulate the large-scale response of the climate system to projected scenarios of increasing greenhouse gases (GHG) and aerosols concentrations. Because of the computational load of integrating complex CGCMs over simulated periods of several centuries, the horizontal resolution has to be limited to grid meshes of the order of a few hundred kilometres. Such resolution is inadequate to resolve atmospheric processes operating at mesoscale, such as frontal zones, and small-scale surface forcing due to local orography or inland water bodies (IPCC 1995). Surface hydrology and atmospheric moist processes in particular are known to exhibit high variability at small (below the CGCMs resolved) scales. Furthermore, climate-change impact and adaptation studies deal with processes operating on much finer scales than currently affordable with CGCMs. Nested limited-area regional climate models (RCMs) represent an appealing approach to achieve finer spatial resolution climate and climate-change simulations at an affordable computational cost. Nested with atmospheric data simulated by CGCMs and integrated for long periods of time, RCMs are potentially useful tools for identifying effects of anthropogenic forcing at regional scale (e.g. Pan et al. 1999). These RCMs can be run at fairly high resolutions (with grid meshes of a few tens of kilometres) over an area of interest covering typically several millions of square kilometres. Such RCM climate-change simulations have been made for various parts of Europe (e.g. Jones et al. 1997; Machenhauer et al. 1998; Jones and Reid 2001; Durman et al. 2001; Christensen et al. 2001; Frei et al. submitted 2002), of North America (e.g. Giorgi et al. 1998; Laprise et al.

2 406 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 1998) and Australia (Whetton et al. 2001); see also the earlier references cited in the reviews by Mearns et al. (1995) and McGregor (1997). A noteworthy alternative approach to nested limited-area RCMs is that of stretched-grid global models (e.g. De que et al. 1998; Fox- Rabinovitz et al. 2001). First-generation RCMs could be classified into two groups, depending upon whether their subgrid-scale physical parameterisation was issued from (1) a highresolution numerical weather prediction (NWP) or mesoscale research model (e.g. Juang and Kanamitsu 1994; Takle et al. 1999), or (2) a low-resolution global climate model (e.g. De que et al. 1998; Laprise et al. 1998; Christensen et al. 2001). In the former case, the parameterisations were developed for weather forecast models with resolutions similar to that of RCMs, but they may not have been designed with consideration of long-term balances required by climate integrations. In the latter case, the parameterisation package is shared by the RCM and its nesting GCM, thus facilitating coupling and the interpretation of the simulated results; there is usually a need however for re-tuning of some parts of the parameterisation, specially those related to the treatment of moist processes, to account for the increased resolution of RCM compared to GCM. Recent studies clearly document the need for parameterisations that are suitable for long-term integration and appropriate for the resolution of the RCMs (e.g. Jacob and Podzun 1997; Christensen et al. 1998; Laprise et al. 1998). Early GCMs were coupled with rather simple mixedlayer ocean and thermodynamic sea-ice models, and equilibrium climate-change simulations with these models were performed with greenhouse gases (GHG) concentrations held fixed throughout the integration. More recent GCMs include coupling with dynamical ocean and sea-ice models (henceforth called CGCMs) that allow to perform more realistic simulations, accounting for the time evolution of observed past and anticipated 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 before launching a transient climate experiment that spans typically a couple of centuries. In order to increase the statistical significance of the results, an ensemble of these simulations may be performed with identical model configuration and scenario of GHG & A. Most RCM climate-change simulations to-date have been performed using, as ocean surface boundary condition, coarse-resolution CGCM-simulated ocean temperature and seaice amounts, simply interpolated on the RCM finer grid. Because of this simplified treatment of the ocean conditions, it is possible to run an RCM for selected time slices within a long CGCM simulation, in order to lessen computation costs. In this work, we will present results of climate simulations performed with the second-generation of the Canadian Regional Climate Model (CRCM-II) integrated over a computational domain covering a northwestern sector of North America and adjacent parts of Hudson Bay and Pacific and Arctic Oceans. The CRCM-II is nested with simulated data of the coupled Canadian General Circulation Model (CGCM2; Flato and Boer 2001) in a scenario of transient GHG & A. The CRCM-II simulation covers three time slices corresponding roughly to single, double and triple current GHG concentration levels. The next section describes the CRCM-II and CGCM2 models and their experimental configurations. Section 3 presents the analysis of simulated results where the first sub-section looks into the decade (referred to as the 1 CO 2 climate), by comparing the simulation of the CRCM-II with that of the CGCM2 and with available recent-past climate observations for basic variables such as temperature, precipitation, snow depth, soil water and cloud cover. Afterwards, sub-section 3.2 discusses the results of the simulated decade (referred to as the 2 CO 2 climate), comparing the CRCM-II and CGCM2- simulated climate-change results. Sub-section 3.3 presents a hydrological budget of the simulated 1 CO 2 and 2 CO 2 climates, carried out over four sub-regions of the CRCM-II domain. Finally, Section 4 will summarise the highlights of the results of this study. It is noteworthy to mention that a subset of the CRCM-IIsimulated data for the 1, 2 and 3 CO 2 time slices is available for applications of interested users via the internet at 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 the first-generation Canadian Regional Climate Model (CRCM-I; Caya and Laprise 1999; Laprise et al. 1998). This limited-area nested model, developed at the Universite du Que bec a` Montréal, uses a dynamical kernel (Laprise et al. 1997) that is based on the fully elastic non-hydrostatic equations solved by a non-centred semi-implicit semi-lagrangian three-time-level marching scheme with a weak running time filter. The lateral boundary conditions are provided through the one-way nesting method of Davies (1976); the regional model receives atmospheric nesting information in the form of a dataset, but the regional model simulation does not influence 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 regional model. Over a ribbon covering some 10 grid points from the edges of the lateral boundaries (called the sponge zone), the simulated horizontal winds are relaxed toward the values of the driving data, with a strength varying as a cosine square from the distance to the boundary. The regional model shares most of the subgrid-scale physical parameterisation package of the global model that provides its nesting data for this study: the second-generation Canadian Coupled 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 processes components of an earlier uncoupled version of the model (GCMii; McFarlane et al. 1992; Boer et al. 1992). The reader is referred to the former paper for an extensive description of the physical

3 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 407 parameterisation and to Caya and Laprise (1999) for a summary of its implementation in CRCM-I. Compared to the earlier version of the Canadian Regional Climate Model (CRCM-I), some noteworthy modifications have been made to the physical parameterisation and numerical components in the second-generation version (CRCM-II) used for this study. The contributions of the physical parameterisation to the time tendencies are now incorporated with the nonlinear dynamical explicit part in the semi-implicit marching algorithm, thus reducing substantially the time truncation associated with the time-splitting procedure used in the earlier version (Caya et al. 1998), and dispensing from the ad hoc hydrostatic adjustment used by Laprise et al. (1998). The nesting of water vapour is now achieved by performing the multiple interpolations required during the preprocessing on the dew-point depression variable rather than specific humidity, thus preventing the occurrence of fictitious supersaturation events resulting from interpolating temperature and specific humidity separately. The strength of the bi-harmonic lateral diffusion has also been reduced by a factor of 2.6 (now a = ; see Appendix of Laprise et al. 1998); this implies a damping factor of 5% per time step for the shortest resolved scales. Three changes have been made in the physical parameterisation package, compared to the earlier CRCM-I version. The moist convective adjustment scheme has been replaced by the deep convection scheme of Kain and Fritsch (1990), better suited to the horizontal resolution of the regional model (Paquin and Caya 2000). Stratiform precipitation however is still parameterised as a simple supersaturation-based condensation scheme as in GCMii and CGCM2. Cloud cover is still parameterised in terms of a simple function of local relative humidity, and assuming maximum (or random) overlap, depending upon whether cloud presence is diagnosed in adjacent layers (or not), as in GCMii and CGCM2. The cloud onset function however has been altered in CRCM-II to reduce the excessive cloudiness noted in the simulations of CRCM- I; the modified vertical profile of critical relative humidity proposed in Sect. 4b of Laprise et al. (1998) is now employed in CRCM-II. Finally, the prognostic calculation of land-surface temperature is implemented with a backward-implicit scheme to suppress the occasional numerical instabilities associated with the original forward-in-time scheme, as documented in Gigue` re et al. (2000). for a few thousand simulated years before initiating the period 1850 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 computational domain covering the north-western North America and adjacent ocean bodies. As can be seen on Fig. 1, this domain is considerably larger than the 100 by 70 grid-point domain used for CRCM-I by Laprise et al. (1998). In the vertical, 18 unequally spaced Gal-Chen scaled-height levels were used; the lowest thermodynamic level is about 170 m above the surface, and the computational rigid lid is located near 29 km. A time step of 15 min was used, with a decentring coefficient of 0.01 in the time-averaging of the semi-implicit algorithm and a running time filter of For nesting of the CRCM-II, the CGCM2-simulated atmospheric fields, archived at 6-hourly intervals, were vertically interpolated from hybrid sigma-pressure coordinate to pressure levels, and horizontally interpolated from Gaussian latitudes and longitudes onto the CRCM-II polar-stereographic grid, and then vertically interpolated to Gal-Chen scaled-height levels and staggered in the vertical and horizontal as required; these atmospheric data were then linearly interpolated in time to nest the CRCM-II at its 15-min time steps. Geophysical fields over land points, such as liquid and frozen soil water content, snow amount and ground temperature, were initialised with the monthly mean values from the CGCM2 simulation. Once-monthly data of sea surface temperatures and sea-ice cover simulated by CGCM2 were interpolated in space and time to serve as time-dependent lower boundary conditions for CRCM-II over oceans. As shall be seen below, there are no lakes in CRCM-II. The reason is that North American lakes are not resolved by the CGCM2 at its resolution, hence no lake temperatures and ice cover were available as lower boundary condition to the 2.2 The CGCM2 For the experiments reported here, the data serving to nest the CRCM-II came from the last of a three-member ensemble of simulations performed with CGCM2 (Flato and Boer 2001) in a scenario of transient GHG & A for the period extending from 1850 to The atmospheric component of CGCM2 is unchanged from earlier versions of GCMii in non-coupled mode (McFarlane et al. 1992) and from the coupled version with simple 50-m mixed-layer ocean (Boer et al. 1992) that was used to produce the CRCM-I nesting data as reported by Laprise et al. (1998). The atmospheric component of CGCM2 is a spectral model with T32 horizontal truncation and 10 unequally spaced hybrid sigma-pressure levels in the vertical. The ocean and ice components of CGCM2 evolved from an earlier version referred to as CGCM1 (Flato et al. 2000). In both cases, the ocean component is a three-dimensional grid-point model based on the GFDL MOM1.1 code (Pacanowski et al. 1993), using a longitude latitude horizontal resolution and 29 levels. CGCM2 differs from CGCM1 by its use of isopycnal eddy-stirring mixing parameterisation of Gent and McWilliams (1990). In addition, the sea-ice component of CGCM2 is based on the dynamical cavitating-fluid scheme of Flato and Hibler (1992), rather than the thermodynamic sea-ice of CGCM1. The atmosphere, ocean and ice components of CGCM2 exchange water, heat and momentum once per day. A monthly mean flux-adjustment procedure for heat and fresh water at the ocean s surface ensures that the coupled model reproduces the observed climatology under current forcing conditions; no adjustment is applied to momentum fluxes however (Flato and Boer 2001). The ocean model was spun-up Fig. 1 Computational domains used for CRCM-I (Solid line, 100 by 70 grid points) and CRCM-II (entire figure 120 by 120 grid points). The dotted line identifies the edge of the 10-grid-point sponge zone and 80 by 50 grid-point diagnostic domain for CRCM-I in Laprise et al. (1998). The dashed line delineates a 20- grid-point ribbon surrounding the 80 by 80-grid-point diagnostic domain for this work. The isolines correspond to the topographic height field in CRCM-II

4 408 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model CRCM-II. The next version, CRCM-III, will incorporate an interactive 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. The GHG concentration (Fig. 2) corresponds to the observed effective CO 2 concentration till 1990, after which time it increases at a rate of 1% per year until year The direct effect of sulfate aerosols is included in CGCM2 and CRCM-II by increasing the surface albedo in a geographically varying fashion, as done in Reader and Boer (1998); the annual average aerosol loading patterns are based on the slow-oxidation version of the chemistry model of Langner and Rodhe (1991), as described by Boer et al. (2000). The CRCM-II simulations were performed for three 10-year time slices of transient GHG & A (the red segments on Fig. 2); this differs from the two 5-year periods of constant 1 and 2 CO 2 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 for the modelled climate system to adjust. Hence the CRCM-II was therefore run for three 12-year periods starting on January , on January and on January The 10-year average equivalent-co 2 concentrations for the , and time slices are respectively 437 ppmv, 827 ppmv and 1255 ppmv. These three time slices will henceforth be referred to as 1 CO 2 (or recent past climate), 2 CO 2 and 3 CO 2 periods. The 10-year statistics were computed for all four seasons (defined as 3-month periods such as December January February for winter, etc.), although the analysis presented in this paper will focus on the winter and summer seasons of the 1 CO 2 and 2 CO 2 periods. For diagnostic purposes, CRCM-II simulated fields were archived at 6-hourly intervals, with surface fluxes and precipitation cumulated as time integrals between archival times. A 20-grid-point ribbon was removed at the perimeter of the computational domain, corresponding to the 10-grid-point sponge zone plus an additional 10 grid points to allow for the spin-up of fine-scale features; the resulting diagnostic domain hence covers 80 by 80 grid points. The simulated atmospheric data of CRCM-II and CGCM2 were all interpolated onto a common set of 14 pressure levels, and CGCM2 data were interpolated from Gaussian latitude and longitude onto the CRCM-II polar-stereographic grid for ease of comparison. Figure 3 presents the CRCM-II ground cover mask, identifying the regions of land, open water and sea-ice, and the topographic height field, on the 80 by 80 points diagnostic domain; for comparison, the corresponding CGCM2 fields (interpolated onto the CRCM-II grid) are also shown. It can be noted that the Rocky Mountains and Continental Divide are much more detailed in the CRCM-II with even some partial representation of the Okanagan Valley in southern British-Columbia (BC) and some coastal mountains near the Vancouver Island, all absent at the CGCM2 resolution. In southern Yukon, the CRCM-II better resolves the Mount Logan, with a peak elevation of 2400 m compared to 1200 m in the CGCM2. The topography field in CRCM has been smoothed in order to reduce the numerical truncation error associated with the use of long time steps that the semi-implicit semi- Lagrangian marching scheme permits (e.g. He reil and Laprise 1996). A drawback of this procedure however is a somewhat degraded representation of mountains, which has some impact on the simulation of snow in regions of complex orography, as shall be seen below. Forthcoming versions of the CRCM will try to improve on the treatment of orography. 3 Results and discussion Fig. 2 Time evolution of the equivalent CO 2 concentration in CGCM2 and CRCM-II. The three red segments identify the three time slices of CRCM-II: the decade (referred to as the 1 CO 2 period), the decade (2 CO 2 period) and the decade (3 CO 2 period) Gridded surface climate data from various sources were used to evaluate the simulated seasonal mean variables under current conditions: screen temperature from the Climatic Research Unit (CRU; New et al. 2000), precipitation from Willmott and Matsuura (WM; 1995, 2000), snow depths from Brown et al. (2003), and surface runoff from Cogley (1998). Simulated seasonal mean screen temperatures, diurnal screen temperature range and total clouds were compared with the CRU database; the CRU dataset takes account of station Fig. 3 Topographic height and ground cover mask of CRCM- II and CGCM2, displayed over the CRCM-II diagnostic domain. The following colour codes are used for the ground cover mask: brown for land, light blue for open ocean and white for sea ice of one of the January periods. The CGCM2 Gaussian latitude longitude fields were interpolated onto the CRCM-II polar-stereographic grid for ease of comparison

5 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 409 elevation in processing screen temperatures. Simulated seasonal mean precipitation rates were compared with the WM database; the WM dataset however did not correct for under-catch of precipitation. The observational datasets were interpolated from their original latitude longitude grid (1 for the Cogley (1998) dataset, 0.5 for the CRU and WM datasets, and 0.3 for the Brown et al. (2003) dataset) onto the CRCM-II 45-km polar-stereographic grid. The 1975 to 1984 datasets were used to compute the 10-year means, to compare the corresponding recent past climate (1 CO 2 ) CRCM-II and CGCM2 simulations in the next sub-section. 3.1 The recent past climate: the decade Screen temperature Figures 4 and 5 present seasonal average screen temperatures simulated by the CRCM-II and the CGCM2, as well as the gridded analyses of surface observations by the Climatic Research Unit (CRU; New et al. 2000), for winter and summer, respectively. The winter climate over the domain of interest is characterised by a very large thermal gradient between the temperate maritime climate of eastern Pacific Ocean, where temperatures range from 5 to 15 C, and the continental Arctic climate of the Canadian North-Western Territories (NWT), where temperatures reach below 30 C (see CRU; right panel of Fig. 4). In summer, the continental temperatures exhibit a gradual variation with latitude and altitude, ranging from 25 C in the prairies in the southern part of the domain to near freezing at the edge of the Arctic Ocean (see CRU: right panel of Fig. 5). Not too surprisingly, both models are successful in simulating the major characteristics of spatial distribution and seasonality contrasts. Also expectedly, due to their higher resolution, the CRCM-II fields display finer scale variations, especially over steep topographical features 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 contour interval is 5 C

6 410 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model In winter, the CRCM-II simulation suffers from a generalised warm bias of about 5 C over the western part of the domain (between 2 6 C over the Canadian Prairies) reaching 10 over southern Yukon. There are however some regions where the CRCM-II is somewhat too cold, such as Wyoming ( 6 C) and the northern sector of the NWT. A comparison of CRCM-II simulated minimum and maximum temperatures against CRU data reveals that the warm bias is mostly the result of too warm simulated minimum temperatures. As an example, the minima are too warm over central BC by as much as 16 C, compared to the maxima that are too warm by only about 2 C there. CGCM2 also has a warm bias over Canada; compared to CRCM-II, this bias is less pronounced over BC but more pronounced over the Canadian Prairies and NWT. In summer, the CRCM-II is substantially warmer than the observations at the northern edge of the NWT, but overall the warm bias of the CRCM-II is generally small; there are even generalised regions of negligible or cold biases west of the Continental Divide. This small bias however is somewhat coincidental, resulting from compensating errors in the minimum and maximum temperatures. In the CRCM-II, maxima are generally too cold and minima too warm over most of the region west of the Continental Divide; as shall be seen later, this results in part from the excessive cloud cover. In summer, the CGCM2 temperatures are generally somewhat better than those of the CRCM-II, except over the northwestern USA where the cold bias is nearly doubled, reaching almost 10 C. Figure 6 presents the seasonal average diurnal temperature range (DTR) simulated by the CRCM-II, as well as the analyses of observations by the Climatic Research Unit (CRU), for winter and summer. The DTR is simply the difference between the monthly averages of daily maximum and minimum temperatures further averaged for the appropriate season. The DTR intends to measure the amplitude of the diurnal cycle but is also affected by the temperature variations associated with the passage of synoptic weather systems. Over the region of interest, the spatial pattern of DTR exhibits the combined effect of two influences: a northward decrease associated with the smaller solar zenith angle (understandably more pronounced in summer than in winter), and a decrease towards the West Coast due to the important cloud cover there (see CRU; bottom panels of Fig. 6). In the CRCM-II, the DTR values are noticeably too small almost everywhere, but the largescale spatial pattern does reproduce the overall characteristic of the observations. A part of the simulated DTR deficit may be an artefact of the definition of screen Fig. 6 Diurnal temperature range (DTR) at screen level as simulated by CRCM-II (top) and as analysed by CRU (bottom), for winter (left) and summer (right). The contour interval is 2 C

7 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 411 temperature in CRCM-II as discussed below. The overly large simulated soil water amounts, that result in large ground thermal inertia, thus contribute to reduce the DTR in CRCM-II. It can be noted that the model s DTR values become extremely small in regions such as the West Coast, with thick cloud cover; this negative correlation between small DTR and large cloud cover has been well documented at both the regional and global scales (Karl et al. 1993). Before concluding, we note that the CRCM-II error in average screen temperature is less important over the West Coast, precisely where observation stations are more numerous; part of the CRCM-II s thermal biases in the NWT and in the Rocky Mountains can be attributed to the sparse station network. Since the observing stations in the NWT are mainly located along the coast (see: Fig. 2 in New et al. 2000), the cool summertime marine temperatures may spread inside the continent in the gridding operation, i.e. coastal stations may have large influence radii because of the lack of continental stations in the area. Such station configuration may produce gridded data with too cool summer temperatures in the NWT and make the CRCM-II warm bias appear worse than it really is. The errors in the CRCM-II simulated average screen temperature result from the combined effects of several sources of modelling and diagnostics errors. Errors in the simulated cloud cover will affect the solar and terrestrial radiative fluxes entering the surface energy budget. Errors in the simulated ground water will result in an incorrect partitioning of sensible and latent surface fluxes, producing an erroneous surface temperature. Such errors may compound those due directly to the simplified treatment of land-surface energy budget, and to the absence of lakes that contribute to moderate temperatures. In addition to these are the errors in the circulation of the nesting CGCM2. Furthermore, screen temperature is not a prognostic variable in CRCM-II and CGCM2: it is calculated diagnostically from the land ocean-ice surface temperature and the lowest thermodynamic atmospheric prognostic level, as in GCMii (McFarlane et al. 1992). It has been noted that this diagnostic estimate of screen temperature is poor under conditions of strong inversion, which are frequent in continental climates over night and in winter. Finally, the definition of average screen temperature differs between the observations and the model results. In observations, it is defined as the mean of daily minimum and maximum temperatures; in the model it is obtained as the average of six-hourly archived temperatures. Cursory analysis of this aspect has revealed that the two methods may produce results differing by as much as 2 C, with either signs. The next version of the CRCM will produce a diagnostic of average screen temperature in accordance with that of the observations Precipitation Figures 7 and 8 present the seasonal average precipitation rates simulated by the CRCM-II and the CGCM2, as well as the gridded analyses of observations by Willmott and Matsuura (WM; 1995, 2000), for winter and summer, respectively. From the point of view of precipitation, the region of interest is characterised by four vastly distinct climate zones: the western coast of North America (including the Coastal Range), the western slopes of the Continental Divide, a valley separated by these two mountain systems, and the plains to the east of the Continental Divide (see WM; right panels of Figs. 7, 8). The western coast of North America and the western slopes of the Continental Divide are characterized by winter seasons that are very wet and wet, respectively. The middle valley is fairly dry with little seasonal variation of precipitation. The plains to the east of the Continental Divide are dry and receive their maximum precipitation in summer. Overall, both models capture well these seasonal and geographical contrasts of precipitation. Owing to its higher 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

8 412 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model Fig. 8 Summertime average precipitation rates simulated by CRCM-II (left), CGCM2 (centre) and analysed by WM (right). The contour intervals are irregular, starting at 1 mm da 1 for small values reproduce with fidelity the narrowness of the precipitation band on the edge of the west Coast and on the windward side of the Coastal Range and Continental Divide, and its correct seasonal variation. Over the prairies CRCM-II precipitation amounts are excellent in winter; in summer however CRCM-II precipitation amounts are too large but their distribution is better simulated than in CGCM2. In Yukon in summer and in the inter-mountain valley system in general, CRCM-II precipitations are too abundant, probably due to the use of smoothed topography that reduces the rain shadow of the mountains. The contrast of precipitation between the West Coast and the Prairies is well simulated by CGCM2, albeit at low resolution. Hence, the narrow band of precipitation over the west coast is smeared in CGCM2, and its maximum intensity is under-estimated. The minimum precipitation in the inter-mountain valley system cannot be resolved by CGCM2. The CGCM2 precipitation is excessive over the Canadian Prairies and NWT in summer, probably as a result of the underestimated height of the Continental Divide as represented with the coarse resolution of CGCM2, resulting in insufficient drying in the downslope part of the flow Snow depth The CRCM-simulated current-climate snow depths were verified against North American gridded data analyses made by Brown et al. (2003). Following a critical rescue of Canadian in situ snow-depth measurements (Brown and Braaten 1998) and with access to the important USA measurement network, Brown et al. (2003) proceeded to produce objective analyses of daily snow depths over North America. Their approach also made use of daily precipitation and screen temperature data produced as part of the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis procedure. Gridded snow depths analyses are available as monthly climatology from the period. The Brown et al. (2003) data were interpolated from their original 0.3 latitude longitude grid onto the CRCM-II 45-km polar-stereographic grid. The prognostic variable for surface snow in the CRCM-II and CGCM2 models is snow amount in units of kg m 2, which is roughly equivalent to millimetres of equivalent water thickness. The models snow amounts were converted to approximate snow depths in order to be compatible with the surface measurements and gridded analyses of Brown et al. (2003). A snow density value of 160 kg m 3 was used; this value corresponds to average snow density for taiga and maritime regions in the snow classification of Sturm et al. (1995), and this value is rather on the low end of the range of values of snow densities (120 to 500 kg m 3 ) estimated by Brown et al. (2003). Figures 9 and 10 show snow depths simulated by the CRCM-II and the CGCM2, as well as the gridded observational dataset of Brown et al. (2003), for winter and summer, respectively. The middle- and high-latitude climate of the region in this study is characterised by a large seasonal variation in snow cover. Only few areas keep snow in summer, such as the most elevated parts of the Continental Divide; snow depths of a few millimetres are also found in the extreme northeastern areas of the NWT (see Brown et al. 2003; right panel of Fig. 10). Conversely, few regions in the domain of interest do not have some snow in winter; southwestern BC and the westernmost states of the USA remain snow free (see Brown et al. 2003; right panel of Fig. 9). Both models simulate the seasonal variation of snow amounts with some fidelity. In winter, simulated maximum snow depths over high terrain correspond to the respective models representation of the Continental Divide, and a broad region with moderate snow depths in the NWT extends southward in the Canadian Prairies west of Hudson Bay. Small-scale details in the snow simulated by the CRCM-II over the Rocky Mountains reflect the model s finer resolution and higher local topographic height, but values are still somewhat

9 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 413 Fig. 9 Wintertime average snow depth simulated by CRCM-II (left), CGCM2 (centre) and analysed by Brown et al. (2003) (right). The contour 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). The contour intervals are irregular, starting at 10 cm for small values smaller than the Brown et al. (2003) analyses. The major weakness of the CRCM-II simulated snow depth is the lack of snow cover along the coastal mountains of BC and northwestern USA; this defect is likely due to the degraded mountain features resulting from topographic smoothing in the CRCM as explained earlier. In the Canadian Prairies and NWT, the snow depth is substantially less in the CRCM-II than in the CGCM2. By comparison with the Brown et al. (2003) analyses, the CGCM2 snow depths seem better than the CRCM-II in the Prairies, but become too important in the NWT. The light-snow line just east of the Continental Divide is well simulated by the CRCM-II. In summer, substantial snow remains in the CRCM- II over the Mount Logan area (in southern Yukon) as a result of the model s higher elevation (2400 m in CRCM-II Vs 1200 m in CGCM2), in accord with the Brown et al. (2003) analyses. In the CGCM2, more than 50 cm of snow persists west of Hudson Bay, which appears to be excessive according to the Brown et al. (2003) analyses, although these analyses are known to underestimate snow depths in the area Summary of the simulation of the decade Both the CRCM-II and the CGCM2 successfully reproduced several features of the geographical distribution and seasonal variation of the observed climate. Several terrain-induced features of the climate that are missed by the CGCM2 due to its coarse spatial resolution are captured by the CRCM-II. The systematic biases of CRCM-II appear to have been reduced compared to the results obtained with the earlier version (CRCM-I) by Laprise et al. (1998), although several aspects still leave a lot to be desired. The errors in the CRCM-II simulation result from the combination of several contributions: modelling, nesting and diagnostics. Modelling errors origin from approximations,

10 414 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model especially in the physical parameterisation package. Moreover, when considering nesting errors, one must not forget that the RCM is affected by biases in the nesting GCM (Rummukainen et al. 2001; Pan et al. 1999; Giorgi et al. 1995); these biases are not present when nesting with objective analyses (e.g. Biner et al. 2000; Frigon et al. 2002). Finally, diagnostics errors result from comparing somewhat different quantities (as alluded to already for cloud cover, screen temperature and the definition of daily average screen temperature) and from the limited knowledge of the real climate afforded by the gridded analysed climatologies. Soil moisture content (not shown) is markedly different between CRCM-II and CGCM2, despite the fact that both models share the same surface scheme. Detailed observations lack to assess the relative merits of each model in this respect. The drier soils in the CRCM-II simulation are consistent with warmer summer screen temperatures (Fig. 5) and reduced cloud cover (not shown) in this model compared to CGCM2. The drier values in the CRCM-II are also partly the result of an incomplete transmission of the precipitation to the surface hydrology scheme in the CRCM-II computer code; this error was only uncovered after the completion of these extensive simulations through a detailed examination of the water budget. As seen later in Section 3.3, this coding error has luckily only limited impact on the overall hydrological cycle budget. 3.2 The 2 CO 2 climate: the decade In this section, we focus on the climate change delta between the two following 10-year periods: the , i.e. the 1 CO 2 period, and the , socalled 2 CO 2 period. The climate change delta simulated by the CRCM-II will be compared to that simulated by the CGCM2 for the winter and summer seasons. We want to emphasise clearly that we do not pretend (at this stage) that the following simulated climate changes have any predictive skills at the regional scale. We are mostly interested in understanding the differences between two climates simulated under different forcing by two models with different resolutions but a common parameterisation of several physical processes. An important effort of climate-modelling research is devoted to trying to understand the mechanisms responsible for the changes and their differences Screen temperature, snow depth and albedo feedback Figure 11 presents maps of screen temperature deltas for winter and summer as simulated by the CRCM-II and the CGCM2. Both models agree in simulating an increased warming from the south-west corner of the domain, with less than 2 C over the Pacific Ocean, towards the north-east corner, with about 4 C in summer, and up to 7 C in winter over the NWT. The CRCM-II warming is noticeably weaker than that of the CGCM2 over central Alberta and Saskatchewan where the differences exceed 2 C. The average computed over the displayed domain of the screen temperature deltas simulated by the CRCM-II and the CGCM2 indicate quite similar values of warming however: 2.6 and 2.7 C annually, 3.3 and 3.6 C in winter, and 2.1 and 2.3 C in summer, for the CRCM-II and the CGCM2, respectively. The CRCM-II delta is modulated by topographical height, especially in winter: for example the warming is less than 1 C in the low-elevation BC interior and more than 2 C over high-elevation Rocky Mountains of BC. The same effect is also noted in southern Idaho, with more pronounced amplitude. As we shall see later, the change of snow, and its albedo feedback, is largely responsible for this elevation amplification. Altitude amplification was also noted over the Rocky Mountains in a climate-change simulation made with CGCM1 by Fyfe and Flato (1999). Such an altitude amplification of climate trends over the Alps had also been noted earlier by Beniston and Rebetez (1996) in the records of observed surface temperatures, and by Giorgi et al. (1997) in the simulated results of the regional climate model RegCM. Next, we turn our attention to the delta in winter snow depth as simulated by the CRCM-II and the CGCM2. Because the delta of snow depth appears to be highly correlated with the snow depth itself, the relative change of snow depth (relative to 1 CO 2 climate values) appears to be best suited to display the information in Fig. 12. The overall patterns of relative change are similarly simulated by the CRCM-II and the CGCM2. A vast region with relative reduction between 50 and 100% covering the southwestern part of the domain becomes virtually snow-free in the 2 CO 2 climate, and only the highest parts of the Rocky Mountains retain any snow. The relative reduction in snow depth decreases from its maximum in the southwestern part of the domain to reach zero in some parts of the NWT, Alaska and Yukon. It is interesting to note that, despite a widespread decrease of winter snow in the warmer climate, some regions receive an increase. This is the case for the region at the northern edge of the NWT, Yukon and Alaska. This increase of snow depth is caused by an increase of winter precipitation, as seen below in Sect (Fig. 13). Summertime snow-depth deltas are not very interesting and are not shown. Most of the domain of interest is snow-free even in today s climate, save for the high ground around Mount Logan in southern Yukon and, in the CGCM2 where it is rather excessive, over the eastern part of the NWT, west of Hudson Bay. Over these two areas, snow decreases under 2 CO 2 forcing and vanishes over the high ground around Mount Logan in the CGCM2. In the 1 CO 2 climate simulated

11 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 415 Fig. 11 The climate-change delta of screen-level temperature as simulated by CRCM-II (top) and CGCM2 (bottom), for winter (left) and summer (right). The contour intervals are irregular, starting at +/ 1 C for small values Fig. 12 The climate-change relative delta of winter snow depth (in percent of the 1 CO 2 period values) as simulated by CRCM-II (left) and CGCM2 (right). The contour intervals are 0, +/ 10, +/ 25, +/ 50 and +/ 100% by the CGCM2, the excessive amount of snow west of Hudson Bay (more than 60 cm in winter) is so large that more than 30 cm remain in summer (Fig. 10). In the 2 CO 2 projection, almost all this summer snow disappears, resulting in a large change of snow depth. A concomitant decrease in surface albedo is responsible for the large climate-change warming found west of Hudson Bay in the CGCM2: up to 5 C compared to about 3 C in the CRCM-II. This is an example of a case where a poor current-climate simulation can result in an unlikely climate-change signal, thus the paramount importance of a faithful reproduction of current conditions by climate models. Another area of difference between the two model simulations in winter screen temperature delta is notable at the northern edge of Alaska (Fig. 11); the CGCM2 simulates an area of minimum warming reaching values less than 2 C, whereas the CRCM-II simulated

12 416 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model Fig. 13 The climate-change relative delta of precipitation (in percent of the 1 CO 2 period values) as simulated by CRCM-II (top) and CGCM2 (bottom), for winter (left) and summer (right). The contour intervals are 0, +/ 10, +/ 20, +/ 40, +/ 60, +/ 80 and +/ 100% warming is 3 to 5 C. Figure 9 showed that the CGCM2 simulates winter snow depths between 60 and 120 cm in that region for the 1 CO 2 climate, compared to CRCM-II values of about 50 cm. Under a scenario of increased GHG, the decreased snow depth for both models (not shown) is about 10 cm on average. This decrease in snow is sufficient to change the winter surface albedo in the CRCM-II but insufficient to appreciably change the CGCM2 albedo, hence the smaller temperature delta in the CGCM2 simulation. The same type of snow-albedo feedback argument applies to explain the larger winter climate-change warming simulated by the CRCM-II in southern Idaho. A similar situation is found in winter over the Canadian Prairies where the CGCM2 has a climate-change warming of 5 C compared to around 3 C in the CRCM-II; this is related to the snow depth reduction that is larger in the CGCM2 than in the CRCM-II Clouds, precipitation and ground water Figure 13 presents the relative changes of winter and summer precipitation (relative to 1 CO 2 climate values) simulated by the CRCM-II and the CGCM2. These relative changes, ranging between 30% and +40% in both models and both seasons, correspond to precipitation changes of less than +/ 1 mm da 1. Over land, the signal is weak and poorly organised, although there is a tendency for a small decrease of precipitation in both models and for both seasons over two regions: southern BC and an area around or to the southwest of the Athabasca, Great Slave and Great Bear lakes. These results contrast sharply with previous simulations obtained with CRCM-I nested within GCMii (Laprise et al. 1998) in which winter precipitation changes over Vancouver (BC) reached values of +3 and +5 mm da 1 in the GCMii and the CRCM-I, respectively. The factor explaining the GCM differences is that the GCMii simulation corresponded to equilibrium conditions and was coupled with mixed-layer ocean and thermodynamic sea-ice models, while the CGCM2 simulation was performed for a transient GHG & A scenario and was coupled with dynamical ocean and ice models. These GCM differences were then passed onto the regional model through the nesting of atmospheric variables. There is a noticeable increase of precipitation over the northern part of the domain, especially in winter and in the CRCM-II simulation. This increase is associated to the decreased sea ice off the north coast of North America. The open waters can lead to decreased boundary layer stability and increased evaporation,

13 Laprise et al.: Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model 417 leading to increased convective precipitation near the coastal waters. Perhaps not too surprisingly, cloud cover changes (not shown) are fairly small, less than +/ 10% everywhere in both models and for both seasons, and these changes relate only weakly to the changes in precipitation. The overall pattern of total soil water fraction varies little between the 1 CO 2 and the 2 CO 2 simulations (not shown); the amplitude of changes can reach values of 40% and +30% locally, but the patterns of change are not well defined. 3.3 The hydrological cycle and its climate change In this section, we turn our attention to the hydrological cycle and its climate change as simulated by the CRCM-II and the CGCM2. The hydrological budget will be averaged over four selected regions located within the diagnostic domain (80 by 80 grid points). This integrative approach has the merit of providing more robust statistics than point-wise calculations. The domain of interest comprises several drainage basins that may be grouped on the basis of the ocean in to which their rivers drain. For the case at hand, some basins drain into the Pacific Ocean (e.g. Columbia, Fraser, Yukon and several smaller West Coast basins), into the Arctic Ocean either directly or via Hudson Bay (e.g. Mackenzie, Churchill, Nelson and several others in the NWT), and into the Atlantic Ocean via the Gulf of Mexico (e.g. Mississippi). For our purposes, the first set of land basins west of the Continental Divide draining into the Pacific Ocean will be called the West Land, and the remaining sets located east of the Continental Divide will be grouped into one region called the East Land (Fig. 14). For each of the four regions identified in Fig. 14 (Pacific Ocean, West Land, East Land and Arctic Ocean), vertically integrated water budget equations may be written for the atmosphere and the surface as follows: d t W a ¼þC P þ E d t W s ¼ R þ P E (e.g. equations and 12.1 of Peixoto and Oort 1992, respectively). In these equations, W a refers to the vertically integrated atmospheric water content (or precipitable water) and W s is the total surface water content (over land, this is composed of liquid and frozen ground water, plus snow); R refers to the runoff and C to the vertically integrated atmospheric water flux convergence (or atmospheric run-in as some say); and P and E refer to precipitation and evaporation, respectively. Upon time averaging over several annual cycles, the time tendency terms normally become very small, so that the knowledge of P and E suffices to draw some information about the water cycle without an explicit evaluation of the transport contributions R and C. The atmospheric and surface flux convergences C and R may be written as Fig. 14 The four regions used for performing the hydrological budget. The regions identified as West Land and East Land are separated by the Continental Divide, so that West Land drains into the Pacific Ocean and East Land drains into the Arctic Ocean and partly into the Gulf of Mexico C ¼ ~r~f a DF a and R ¼ ~r~f s DF s and integrated over each of the four regions to yield the trans-region boundary fluxes, ~F a and ~F s ; knowledge of one of the boundary fluxes can yield the others by continuity. Figure 15 gives a schematic view of the four regions and the values of the terms in the equation for 1 and 2 CO 2 simulations of the CRCM-II and the CGCM2. In this figure the values for R, C, P and E are expressed as an amount per unit area (in units of kg m 2 an 1 ) whereas the fluxes ~F are given as the amount for the region (in units of km 3 an 1 ). This procedure, which is common practice for land area, applies equally well to ocean surface where the quantity d t W s is identically zero, and R ¼ ~r~f s corresponds to the integrated effect of ocean currents and sea-ice motion. Consider first the numbers for the 1 CO 2 simulations of CRCM-II (upper line) and CGCM2 (lower line), given in the left column of each block of six numbers on Fig. 15. Both models similarly capture the distinct climate regime of the two land regions, the West and the East Lands. This is clearly evidenced by the drastic difference in net water budget, with values of positive P E (corresponding to atmospheric flux convergence and surface flux divergence) of 716 and 746 kg m 2 an 1 in the West Land and 143 and 262 kg m 2 an 1 in the East Land, for CRCM-II and CGCM2 respectively. For both land regions, P E from the CGCM2 is more important than that from the CRCM-II and the larger difference in the East region is related to CGCM2 s greater precipitation intensity. The corresponding fluxes ~F s give the