Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling

Abstract

Abstract. Climate simulations with more accurate process-level representation at finer resolutions (<100 km) are a pressing need in order to provide more detailed actionable information to policy makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building and running high-resolution (HR, here 0.25∘ average grid spacing at the Equator) models (HRMs). A more affordable path to HRMs is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low resolution (LR, 1∘). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces the precipitation metrics of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behavior of physical parameterizations, especially for variables influencing sub-grid-scale physical processes. RRMs can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a test bed and as such represents an efficient and more robust physics test bed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective test bed for HRM development.

Impact of Nudging Strategy on the Climate Representativeness and Hindcast Skill of Constrained EAMv1 Simulations

“…In terms of the nudging method, it would be useful to further investigate the benefit of using spectral nudging (Uhe & Thatcher, ), applying nudging only for the nondivergent wind component (Jeuken et al, ), and further optimization of the nudging time scale. Several scientific questions can be explored such as (1) how to apply nudging optimally in simulations using the Regionally Refined Model (RRM; e.g., Tang et al, ); (2) how to choose the best hindcast strategy (nudging versus transpose AMIP or CAPT); and (3) how to better evaluate the nudged model against in situ measurements with limited spatial and temporal coverage.…”

An Overview of the Atmospheric Component of the Energy Exascale Earth System Model

“…This setup allows variable resolution grids to maintain the key conservation and scalability aspects that makes the dynamical core a desirable model choice for climate simulations (Zarzycki et al, ). A detailed evaluation of the impacts of variable resolution on the climate for EAMv1 appears in Tang et al (). Two examples of a horizontal discretization in EAM can be seen in Figure , which shows the basic “cubed sphere” grid with an embedded regional refinement over the Continental United States (CONUS) region, and over the Tropical Western Pacific.…”

Effects of Coupling a Stochastic Convective Parameterization with Zhang-McFarlane Scheme on Precipitation Simulation in the DOE E3SMv1 Atmosphere Model

“…This is encouraging and desirable for model development efforts. However, we note that for higher horizontal resolutions or a regionally refined mesh version of EAMv1 (Tang et al, 2019), spatial averaging of the input fields of the stochastic scheme would be needed to make use of convective quasiequilibrium over a larger domain. This could be challenging for computational efficiency and it requires further research in the future.…”