A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations: CESM/CAM EVALUATION BY DECISION TREES

Journal Article · Fri Nov 11 00:00:00 EST 2016 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1002/2016MS000657· OSTI ID:1425409

Soner Yorgun, M. ^[1]; Rood, Richard B. ^[2]

---

1. Univ. of Colorado, Boulder, CO (United States); National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab.; University of Colorao Boulder, Boulder, Colorado
2. Univ. of Michigan, Ann Arbor, MI (United States)

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.

• Research Organization: Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0006684
• OSTI ID: 1425409
• Journal Information: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 4 Vol. 8; ISSN 1942-2466
• Publisher: American Geophysical Union (AGU)
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES