An Object-Based Approach for Quantification of GCM Biases of the Simulation of Orographic Precipitation. Part II: Quantitative Analysis

Abstract

Abstract An object-based evaluation method is applied to the simulated orographic precipitation for the idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and Eulerian spectral transform dynamical cores with varying resolutions. The method consists of the application of k-means cluster analysis to the precipitation features to determine their spatial boundaries and the calculation of the semivariograms (SVs) for the isolated features for evaluation. The quantitative analysis revealed differences between the simulated precipitation by the FV and Eulerian spectral transform models that are not visually apparent. The simulated large-scale precipitation features of the idealized test cases provide analogs to orographic precipitation features observed in simulations of Atmospheric Model Intercomparison Project (AMIP) models. The spatial boundaries of these features (determined by k-means clustering) for Eulerian spectral T85 and T170 resolutions revealed the level of merger between the two large-scale features simulated because of each peak in the double mountain idealized setup. Both FV 1° and 0.5° resolutions were able to simulate the dryer region between the two mountains. The SVs of precipitation for the single and double mountain setups show close agreement between FV 1°, FV 0.5°, and Eulerian spectral T170 resolutions; however, Eulerian spectral T85 simulated the precipitation in lower intensity, indicating the qualitative difference in resolutions previously determined to be equivalent. Such close agreement was not observed in the more realistic idealized setup.