An Object-Based Approach for Quantification of GCM Biases of the Simulation of Orographic Precipitation. Part I: Idealized Simulations

Abstract

n object-based evaluation method to quantify biases of general circulation models (GCMs) is introduced using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Idealized experiments with different topography are designed to reproduce the spatial characteristics of precipitation biases that were present in Atmospheric Model Intercomparison Project simulations using the CAM finite volume (FV) and CAM Eulerian spectral dynamical cores. Precipitation features are identified as ?objects? to understand the causes of the differences between CAM FV and CAM Eulerian spectral dynamical cores. Three different mechanisms of precipitation were simulated in idealized experiments: stable upslope ascent, local surface fluxes, and resolved downstream waves. The results indicated stronger sensitivity of the CAM Eulerian spectral dynamical core to resolution. The application of spectral filtering to topography is shown to have a large effect on the CAM Eulerian spectral model simulation. The removal of filtering improved the results when the scales of the topography were resolvable. However, it reduced the simulation capability of the CAM Eulerian spectral dynamical core because of Gibbs oscillations, leading to unusable results. A clear perspective about models biases is provided from the quantitative evaluation of objects extracted from these simulations and will be further discussed in part II of this study.