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

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 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.

Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

“…For instance, mountains are often identified as fractal (Prusinkiewicz and Hammel 1993) and may require special techniques (e.g., Strachan et al 2016; Mountain Research Initiative Elevation-Dependent Warming Working Group 2015; Daly et al 2010). Yorgun and Rood (2015) investigated the spatial continuity of orographic precipitation using climate model simulations and synthetic data using variogram analysis. The results of this study indicated significant differences in the behavior of the variograms of orographic precipitation when compared with that of the variables with spatially coherent nature such as temperature.…”

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

“…We focus on a comparative study of the Eulerian spectral and the finite volume (FV) [ Lin and Rood , ] dynamical core components of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM) version 5.0 [ Neale et al ., ] and the simulation of orographic precipitation. In our previous studies [ Yorgun and Rood , ], we focused on the monthly mean simulations and quantified the differences between the two dynamical cores. In this study, we add the time dimension to the analysis by analyzing daily simulations.…”

“…The precipitation features are mechanistically represented in simplified experimental setups using idealized test cases. A brief summary of the significant results of our previous time‐averaged analysis is given in section 2 of this paper; however, the detailed results can be found in Yorgun and Rood [] and Yorgun and Rood []. Once the study features are analyzed qualitatively, the identification and detection algorithm with classification trees (explained in section 3) are applied.…”

Quantitative characterization of spurious numerical oscillations in 48 CMIP5 models

“…Spectral model results have been shown to be sensitive to the transformed orography and spectral resolution, where simulation of variables such as precipitation over mountainous terrain is more realistic across multiple scales when using smoothed orography and when the same model is run at higher resolution versus a lower resolution [ Lindberg and Broccoli , ; Yorgun and Rood , ]. Local‐, regional‐, and global‐scale precipitation patterns, among other model variables, can be affected by SNOs.…”

“…Local issues can include grid‐point storms near mountainous terrain caused by spurious vertical velocity associated with SNOs [ Webster et al ., ] and a connection between SNOs and unrealistic bands of precipitation [ Bouteloup , ]. Locally and regionally, poor representation of precipitation near mountainous terrain [ Bala et al ., ; Yorgun and Rood , ] has been associated with spectral numerics. SNOs have been the cause of unrealistic “spotty” precipitation over the Sahel region of Africa [ Navarra et al ., ] and have also been shown to be detrimental to global precipitation patterns [ Lindberg and Broccoli , ].…”