Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations

Abstract

The atmospheric component of Energy Exascale Earth System Model version 1 has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one-at-a-time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the Energy Exascale Earth System Model version 1 model behaviors and physics, we conducted a large number of short simulations (three days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection, and cloud macrophysics and microphysics were perturbed simultaneously using the Latin hypercube sampling method. From the perturbed parameter ensemble simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that perturbed parameter ensemble using short simulations has some bearing on understanding parametric sensitivity of longer simulations. Results from this analysis provide a more comprehensive picture of the Energy Exascale Earth System Model version 1 behavior. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (so-called embedded errors) to inform future development efforts.

Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

“…Yang et al (2013) also indicated the zmconv_tau was sensitive for LWCF. The autoconversion efficiency of cloud water to precipitation (zmconv_c0_lnd and zmconv_c0_ocn) and the cloud ice sedimentation velocity (cldsed_ai) were found to be sensitive for LWCF in Qian et al (2018). That is to say, all the tuning parameters in this study are very sensitive for LWCF, resulting in this field having the most improvement.…”

The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution

“…A detailed analysis of its cloud and convective characteristics and the rationale for model tuning are provided in Xie et al () and Zhang et al (). Sensitivity of EAM to a number of its adjustable parameters is explored in Qian et al (). EAM simulated diurnal cycle of precipitation is analyzed in Xie et al ().…”

Improved Diurnal Cycle of Precipitation in E3SM With a Revised Convective Triggering Function

“…Xie et al () provided more detailed information about the model tuning. Sensitivity of EAMv1 to a number of its adjustable parameters is explored in Qian et al (). EAMv1 simulated cloud and convective characteristics can be found in Xie et al () and Zhang et al ().…”