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Climate Dynamics

, Volume 32, Issue 6, pp 833–854 | Cite as

Regional climate model sensitivity to domain size

  • Martin Leduc
  • René Laprise
Article

Abstract

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.

Keywords

Regional climate model Big-brother experiment Domain size Small-scale features Lateral boundary conditions Spin-up 

Notes

Acknowledgments

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.

References

  1. Antic S, Laprise R, Denis B, de Elía R (2004) Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Clim Dyn 23:473–493CrossRefGoogle Scholar
  2. Arakawa A, Lamb V (1977) Computational design of the basic dynamical processes of UCLA General Circulation Model. Methods in Computational Physics, vol 17. Academic Press, New York, pp 173–265Google Scholar
  3. Bärring L, Laprise R (eds) (2005) Extended Abstracts “High-resolution climate modelling: Assessment, added value and applications”. WMO/WCRP-sponsored regional-scale climate modelling Workshop, 29 March–2 April 2004, Lund (Sweden). Lund University electronic reports in physical geography, 132 pp. (http://www.nateko.lu.se/ELibrary/Lerpg/5/Lerpg5Article.pdf)
  4. CAS/JSC WGNE (1999) Report of fourteenth session of CAS/JSC working group on numerical experimentation no.14, 28 ppGoogle Scholar
  5. CAS/JSC WGNE (2000) Report of fifteenth session of CAS/JSC working group on numerical experimentation no.15, 29 ppGoogle Scholar
  6. Caya D, Laprise R (1999) A semi-implicit semi-Lagrangian regional climate model: the Canadian RCM. Mon Weather Rev 127:341–362CrossRefGoogle Scholar
  7. Davies HC (1976) A lateral boundary formulation for multi-level prediction models. Quart J Roy Meteorol Soc 102:405–418Google Scholar
  8. Denis B, Côté J, Laprise R (2002a) Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using discrete cosine transform (DCT). Mon Weather Rev 130:1812–1829CrossRefGoogle Scholar
  9. Denis B, Laprise R, Caya D, Côté J (2002b) Downscaling ability of one-way nested regional climate models: The Big-Brother experiment. Clim Dyn 18:627–646CrossRefGoogle Scholar
  10. Denis B, Laprise R, Caya D (2003) Sensitivity of a regional climate model to the resolution of the lateral boundary conditions. Clim Dyn 20:107–126Google Scholar
  11. Diaconescu EP, Laprise R, Sushama L (2007) The impact of lateral boundary data errors on the simulated climate of a nested regional climate model. Clim Dyn 28:333–350CrossRefGoogle Scholar
  12. Dimitrijevic M, Laprise R (2005) Validation of the nesting technique in a RCM and sensitivity tests to the resolution of the lateral boundary conditions during summer. Clim Dyn 25:555–580CrossRefGoogle Scholar
  13. de Elia R, Laprise R, Denis B (2002) Forecasting skill limits of nested, limited-area models: a perfect-model approach. Mon Weather Rev 130:2006–2023CrossRefGoogle Scholar
  14. Gal-Chen T, Somerville RCJ (1975) On the use of a coordinate transformation for the solution of the Navier–Stokes equations. J Comput Phys 17:209–228CrossRefGoogle Scholar
  15. Giorgi F, Bates GT (1989) The climatological skill of a regional model over complex terrain. Mon Weather Rev 117:2325–2347CrossRefGoogle Scholar
  16. Giorgi F, Mearns LO (1999) Introduction to special section: regional climate modeling revisited. J Geophys Res 104:6335–6352CrossRefGoogle Scholar
  17. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Quart J Roy Meteorol Soc 121:1413–1449Google Scholar
  18. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802CrossRefGoogle Scholar
  19. Laprise R (2008) Regional climate modelling. J Comput Phys, Special issue on “Predicting weather, climate and extreme events” 227:3641–3666Google Scholar
  20. McFarlane NA, Boer GJ, Blanchet J-P, Lazare M (1992) The Canadian Climate Centre Second-Generation General Circulation Model and its equilibrium climate. J Clim 5:1013–1044CrossRefGoogle Scholar
  21. McGregor JL (1997) Regional climate modelling. Meteorol Atmos Phys 63:105–117CrossRefGoogle Scholar
  22. Miguez-Macho G, Stenchikov GL, Robock A (2004) Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J Geophys Res 109(D13):D13104. doi: 10.1029/2003JD004495 CrossRefGoogle Scholar
  23. Paquin D, Caya D (2000) New convection scheme in the Canadian Regional Climate Model. In: Ritchie H (ed) Research activities in atmospheric and oceanic modelling, WMO/TD-No. 987, Report No. 30, 7.14–7.15Google Scholar
  24. Robert A., Yakimiw E. (1986) Identification and elimination of an inflow boundary computational solution in limited area model integration. Atmos Ocean 24:369–385Google Scholar
  25. Seth A, Giorgi F (1998) The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model. J Clim 11:2698–2712CrossRefGoogle Scholar
  26. Seth A, Rojas M (2003) Simulation and Sensitivity in a Nested Modeling System for South America. Part I: Reanalyses boundary forcing. J Clim 16:2437–2453CrossRefGoogle Scholar
  27. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106 (D7):7183–7192CrossRefGoogle Scholar
  28. Yakimiw E, Robert A (1990) Validation experiments for a nested grid-point regional forecast model. Atmos Ocean 28:466–472Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Canadian Regional Climate Modelling and Diagnostics (CRCMD) Network, ESCER CentreUniversité du Québec à MontréalMontréalCanada
  2. 2.UQAM/OuranosMontréalCanada

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