Regional climate model sensitivity to domain size
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Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.
KeywordsRegional climate model Big-brother experiment Domain size Small-scale features Lateral boundary conditions Spin-up
This research was done as part of the Masters project of the first author and as a project within the Canadian Regional Climate Modelling and Diagnostics (CRCMD) Network, funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) and the Ouranos Consortium for Regional Climatology and Adaptation to Climate Change. We would like to thank MM. Claude Desrochers and Mourad Labassi for maintaining a user-friendly local computing facility. Thanks are also extended to the Ouranos Climate Simulation Team for their support of the CRCM software.
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