A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations

Abstract

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.

Probing the Skill of Random Forest Emulators for Physical Parameterizations Via a Hierarchy of Simple CAM6 Configurations

“…While ML is still a relatively novel approach to applications in climate science, there is already an abundance of research utilizing these techniques. Some examples include identifying mixed layer depths in the ocean via observations (Foster et al, 2021), attributing model biases from physics-dynamics coupling in climate models (Yorgun & Rood, 2016), improving severe hail predictions over the US high plains (Gagne et al, 2017), post-processing bias corrections of weather forecasts (Chapman et al, 2019), and implementing corrective schemes like "nudging" physics tendencies via coarse-graining or hindcasting (Bretherton et al, 2022;Watt-Meyer et al, 2021).General Circulation Models (GCMs) are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes. The latter estimate subgrid-scale processes that are generally not resolved by the dynamical core's computational grid.…”