Canadian regional climate model
Canadian Centre for Climate Modelling and Analysis
The Canadian regional climate model, CanRCM4, was developed employing a new approach of "coordinated" global and regional climate modelling (Scinocca et al., 2016). Following this approach, the regional climate model (RCM) is paired with, and driven exclusively by, a global parent model (GCM) for all of its applications. CanRCM4's parent model is CanAM4 (von Salzen et al., 2013), which forms the atmospheric component of the second generation earth system model CanESM2 (Arora et al., 2011).
A primary goal of such coordination is to eliminate all controllable differences between the RCM and driving GCM so that, when they are applied for a specific experiment, their response differences are more likely to be associated with the enhanced resolution in the RCM than with the potentially confounding influence of differences in their model formulations. To this end, CanRCM4 shares exactly the same package of physical parameterizations as its parent model CanAM4 (e.g., radiative transfer, clouds, precipitation).
CanAM4 employs a spectral dynamical core and so does not offer a straightforward path to limited-area integrations required for regional climate modelling. To facilitate an ongoing program of regional climate modelling, it was decided to transfer the full physics package from CanAM4 to the limited-area configuration of the Global Environmental Multiscale (GEM) model, which was developed by Environment and Climate Change Canada’s Meteorological Research Division (MRD) and employed for both global (Côté et al., 1998) and regional (Zadra et al., 2008) numerical weather prediction. GEM is a two-time-level semi-Lagrangian model. While a fully elastic nonhydrostatic configuration of GEM has been implemented (Yeh et al., 2002), the configuration employed here for CanRCM4 is hydrostatic and employs a hybrid pressure- and terrain-following vertical coordinate and a regular latitude-longitude grid with rotated pole.
In general, the driving data for all applications of CanRCM4 falls into three categories: observations or reanalysis data; global model output from CanAM4, CanCM4, or CanESM2; and global model output from a GCM that is not its parent model. For independent regional climate modelling centres that are not paired with a global parent model, only the dynamical variables of winds, temperature, and specific humidity can be used to drive their RCMs. This is due to the fact that the formulation of prognostic variables used to represent physical processes (e.g., prognostic cloud schemes, aerosol cloud interactions, atmospheric chemistry) differ markedly across models and so do not generally match between independent RCMs and GCMs (including reanalysis models which assimilate observations). Consequently, within an RCM it is not generally possible to represent the distribution and evolution of aerosols and chemical species that have source regions outside the RCM domain. For the situation that CanRCM4 is driven by its parent model, CanAM4, this is not an issue. Because the two models share exactly the same package of physical parameterizations, all prognostic variables will be available from CanAM4 to drive CanRCM4 on its lateral boundaries. This is referred to as "all-field" driving (Scinocca et al., 2016).
In order for all-field driving of CanRCM4 to be possible for the two additional categories in which CanRCM4 is driven by reanalysis data (i.e., observations) or an external GCM that is not its parent, a two step procedure is employed. In the first step, the winds, temperatures, and specific humidity in CanAM4 are constrained by a spectral nudging procedure (Scinocca et al., 2016) to follow the evolution described by the reanalysis or external GCM. In this way, all other physical prognostic fields related to aerosols and chemistry in CanAM4 are naturally slaved to this evolution and so made consistent with the foreign driving data. In the second step, the output of this constrained global simulation is used to provide all-field driving of CanRCM4. The ability to derive such a complete set of driving fields requires both a parent global model and a philosophy of strict physics compatibility. In principle, therefore, all-field driving of CanRCM4 can be employed for all of its applications. Examples of the impact of all-field driving are presented in Scinocca et al. (2016).
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Scinocca J.F., Kharin V.V., Jiao Y., Qian M.W., Lazare M., Solheim L., Flato G.M., Biner S., Desgagne M., Dugas B., 2016: Coordinated global and regional climate modeling, Journal of Climate, 29 (1), pp. 17-35, doi: 10.1175/JCLI-D-15-0161.1
Von Salzen K., Scinocca J.F., McFarlane N.A., Li J., Cole J.N.S., Plummer D., Verseghy D., Reader M.C., Ma X., Lazare M., Solheim L., 2013: The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: Representation of physical processes, Atmosphere - Ocean, 51, 104-125, doi: 10.1080/07055900.2012.755610.
Yeh, K. S., J. Côté, S. Gravel, A. Methot, A. Patoine, M. Roch, and A. Staniforth, 2002: The CMC-MRB Global Environmental Multiscale (GEM) model. Part III: Nonhydrostatic formulation. Mon. Wea. Rev., 130, 339-356, doi:10.1175/ 1520-0493(2002)130,0339:TCMGEM.2.0.CO;2.
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