An 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.
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 18 and 0.58 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 18, FV 0.58, 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.
An object‐based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small‐scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.
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