Previous work by Ghan et al. (2002, 2996a,b) showed that applying all atmospheric and land physics to multiple elevation classes within each grid cell and adding an orographic forcing term to the heat and tracer budgets within each elevation class can yield greatly improved simulations of local climate in regions with complex terrain. The spatial distributions of simulated surface temperature, precipitation, and snow water in particular, after mapping from elevation classes according to a high-resolution (order 5 km) distribution of surface elevation, agree much better with data mapped to the same resolution from station measurements. The computation cost of the scheme depends on the width of the elevation bands selected for the classification, but Ghan found impressive results with a maximum of twelve elevation classes that yield on average about two classes per grid cell. A load balancing scheme (Ghan and Shippert, 2005) limited the computational burden to about a factor of two.
However, because the scheme (originally developed and applied to a regional model by Leung and Ghan,1995, 1998) does not distinguish between precipitation on the windward and lee sides of subgrid topography, it consistently under-simulates rainshadow effects of topography when applied at resolutions too coarse to resolve rainshadows.
The ACME model is designed to run at 25 km resolution, which should be sufficiently fine to resolve rainshadows explicitly, leaving the elevation class scheme to improve fidelity at finer scales. Moreover, since more topographic variability is explicitly resolved at finer resolution, the average number of elevation classes is smaller than at coarser resolution, so the computational burden will be less than a factor of two.
We propose to apply the elevation class scheme to the ACME model in two stages. The first stage, targeting ACME V3.0, will assume the atmosphere and land models operate on the same grid and adopt the same elevation classification algorithm. This results in a one-to-one mapping between elevation classes in the land and atmosphere, so that no interpolation is required to couple the land and atmosphere for each elevation class. Although we expect this configuration to yield spectacular results, it does not allow the land model to distinguish subgrid watersheds.
In stage two, targeting ACME V4.0, the land model will represent watersheds, so that interpolation will be required to couple elevation classes in the land and atmosphere.
Ghan, S. J., X. Bian, A. G. Hunt, and A. Coleman, 2002: The thermodynamic influence of subgrid orography in a global climate model, Climate Dynamics, 20, 31-44, 10.1007/s00382-002-0257-5.
Ghan, S. J., and T. Shippert, 2005: Load balancing and scalability of a subgrid orography scheme in a global climate model. Int. J. High Performance Comput. Appl., 19, 237-245, doi: 10.1177/1094342005056112
Ghan, S. J., T. Shippert, and J. Fox, 2006: Physically-based global downscaling: Regional evaluation. J. Climate, 19, 429–445, http://dx.doi.org/10.1175/JCLI3622.1.
Ghan, S. J., and T. Shippert, 2006: Physically-based global downscaling: Climate change projections for a full century. J. Climate, 19, 1589–1604, http://dx.doi.org/10.1175/JCLI3701.1.
Leung, L. R., and S. J. Ghan, 1995: A subgrid parameterization of orographic precipitation. Theoretical and Applied Climatology, 52, 95-118.
Leung, L. R., and S. J. Ghan, 1998: Parameterizing subgrid orographic precipitation and surface cover in climate models. Mon. Wea. Rev., 126, 3271-3291.