A18: Parametric Sensitivity in EAM

Region and cloud regime dependence of parametric sensitivity in E3SM Atmosphere Model

• Yun Qian

• yun.qian@pnnl.gov

Yun Qian, Zhun Guo, Vincent E. Larson, L. Ruby Leung, Wuyin Lin, Po-Lun Ma, Hui Wan, Hailong Wang, Heng Xiao, Shaocheng Xie, Kai Zhang, Shixuan Zhang, and Yuying Zhang

Atmosphere

E3SM

The DOE Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that are most sensitive over different regions and compare model responses to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity (w’) (gamma) and the damping of the w’ skewness (c8) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance (c1) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

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