PNNL

The Science

The Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), incorporates many relatively new features. In a prior investigation, a comprehensive parametric sensitivity analysis for the EAM was performed based on short, perturbed parameter ensemble (PPE) simulations, focusing on global mean climate features and metrics. While parameter values in global climate models are typically assigned constant values across space and time, the model's response to parameter perturbations can vary by region and climate regime. This underscores a more nuanced understanding of EAM model behaviors and physics at the regional scale and process level. Researchers at DOE's Pacific Northwest National Laboratory demonstrated a new analysis on how parametric sensitivity within the EAM varies with regions and cloud regimes. This study proposes a systematic and computationally efficient approach to assess and enhance model accuracy at the regional scale.

The Impact

Modeling water cycle processes including clouds and precipitation at the regional scale presents a formidable challenge for global Earth system modeling. This study offers a comprehensive insight into the behavior of the EAM, enhancing our understanding of how the model responds to parameters and their interactions. The employed short ensemble simulation strategy also yields valuable insights into optimizing the use of DOE's leadership computing facilities for exascale Earth system modeling. The results of this study furnish valuable information regarding parametric sensitivity across different climate regimes in the EAM. This, in turn, improves our comprehension of the treatment of model physics concerning adjustable parameters and their interactions, thereby guiding future parametrization development for climate models to reduce uncertainties in projecting future changes in the water cycle.

Summary

Enhancing a model's predictive capability necessitates fine-tuning to optimize its representation of physical processes across diverse regions and climatic regimes. While global climate model parameters are generally assigned constant values across space and time, the model's response to parameter perturbation can differ depending on the region and climate regime. This drives the necessity for deeper comprehension of the EAM model's behavior and physics at regional and process levels. In this study, using 256 short PPE simulations and a sensitivity analysis framework, we identify parameters that cause the largest sensitivities over different regions and compare the model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. Our findings reveal that certain parameters have contrasting effects on cloud forcing in mid-latitude versus tropical land regions. We also explore how parametric sensitivity changes as stratocumulus transitions to shallow convection and to deep convection over the ocean, and investigate how the parametric sensitivity evolves with prediction length. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. 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 models.