Numerical Investigation with a Physically Based Regional Interpolator for Off-Line Downscaling of GCMs: FIZR

Stéphane Goyette Cooperative Centre for Research in Mesometeorology and Atmospheric Sciences, Department of Earth Sciences, Université du Québec á Montréal, Montreal, Quebec, Canada

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J. P. René Laprise Cooperative Centre for Research in Mesometeorology and Atmospheric Sciences, Department of Earth Sciences, Université du Québec á Montréal, Montreal, Quebec, Canada

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Abstract

A novel approach for regional climate modeling based on an off-line downscaling of GCM simulations is described and illustrated with a one-month simulation example. The model is physically based and it requires outputs from a previous GCM integration. The methodology is based upon the premise that much of “small-scale” variability (i.e., for spatial scales below current GCM resolution) is often the result of surface forcings rather than small-scale dynamical effects. Following on this consideration, the present work seeks to address the question of regional climate diagnostics by combining precomputed GCM atmospheric large-scale transports of momentum, heat, and moisture, called “the dynamics,” with recomputed GCM subgrid-scale parameterized effect, called “the physics,” including an additional mesoscale forcing term that is parameterized in terms of large-scale flow resolved by GCM coupled with fine-scale geophysical surface fields. This combination is integrated in a prognostic mode on a high-resolution grid over a chosen limited area of the earth. This is an original one-way nesting technique and it offers major advantages over simpler techniques used to interpolate GCM outputs down to finer scales. While the dynamics, inferred from a GCM and solely projected on the high-resolution grid, does not interfere with smaller scales, the proposed model does simulate the nonlinear vertical interactions. The model is nicknamed FIZR: FIZ serves to remind that the model is physically based, and R stands for regional.

To validate the FIZR approach the authors have performed a test on a 0.5° resolution grid over the west coast of North America to downscale January conditions simulated by the Canadian Climate Centre second-generation general circulation model (GCNM). Due to the coarse spatial resolution of GCMII, the West Coast January simulated precipitation pattern suffers from a lack of mesoscale details. To circumvent this resolution problem, we have conducted an experiment with FIZR in which large-scale dynamics is inferred from GCMII outputs and interpolated on the grid, the entire GCMII physics package is recalculated on a high-resolution grid using geophysical fields of vegetation types, soil characteristics, background albedos, and SSTs. A mesoscale forcing term is further added to parameterize orographic upslope and downslope in terms of large-scale flow resolved by GCMII coupled with high-resolution topography. Preliminary results indicate that FIZR can add a number of realistic mesoscale features to GCMII simulation, especially in precipitation and hydrological surface fields. The mesoscale hydrologic features are consistent with the thermal features simulated by the downscaling FIZR technique. Although the scope of this study is restricted to a single January scenario, it does provide evidence that FIZR is a promising technique for downscaling of GCM.

Abstract

A novel approach for regional climate modeling based on an off-line downscaling of GCM simulations is described and illustrated with a one-month simulation example. The model is physically based and it requires outputs from a previous GCM integration. The methodology is based upon the premise that much of “small-scale” variability (i.e., for spatial scales below current GCM resolution) is often the result of surface forcings rather than small-scale dynamical effects. Following on this consideration, the present work seeks to address the question of regional climate diagnostics by combining precomputed GCM atmospheric large-scale transports of momentum, heat, and moisture, called “the dynamics,” with recomputed GCM subgrid-scale parameterized effect, called “the physics,” including an additional mesoscale forcing term that is parameterized in terms of large-scale flow resolved by GCM coupled with fine-scale geophysical surface fields. This combination is integrated in a prognostic mode on a high-resolution grid over a chosen limited area of the earth. This is an original one-way nesting technique and it offers major advantages over simpler techniques used to interpolate GCM outputs down to finer scales. While the dynamics, inferred from a GCM and solely projected on the high-resolution grid, does not interfere with smaller scales, the proposed model does simulate the nonlinear vertical interactions. The model is nicknamed FIZR: FIZ serves to remind that the model is physically based, and R stands for regional.

To validate the FIZR approach the authors have performed a test on a 0.5° resolution grid over the west coast of North America to downscale January conditions simulated by the Canadian Climate Centre second-generation general circulation model (GCNM). Due to the coarse spatial resolution of GCMII, the West Coast January simulated precipitation pattern suffers from a lack of mesoscale details. To circumvent this resolution problem, we have conducted an experiment with FIZR in which large-scale dynamics is inferred from GCMII outputs and interpolated on the grid, the entire GCMII physics package is recalculated on a high-resolution grid using geophysical fields of vegetation types, soil characteristics, background albedos, and SSTs. A mesoscale forcing term is further added to parameterize orographic upslope and downslope in terms of large-scale flow resolved by GCMII coupled with high-resolution topography. Preliminary results indicate that FIZR can add a number of realistic mesoscale features to GCMII simulation, especially in precipitation and hydrological surface fields. The mesoscale hydrologic features are consistent with the thermal features simulated by the downscaling FIZR technique. Although the scope of this study is restricted to a single January scenario, it does provide evidence that FIZR is a promising technique for downscaling of GCM.

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