Impact of Grids and Dynamical Cores in CESM2.2 on the Surface Mass Balance of the Greenland Ice Sheet

Abstract

General Circulation Models (GCMs) are powerful tools for understanding the meteorology and climate of the high latitudes, which are among the most sensitive regions on Earth to global and environmental change. GCMs differ vastly in their numerical treatment of polar regions because of the so-called pole problem (Williamson, 2007). The pole problem refers to numerical instability arising from the convergence of meridian lines into polar singularities on latitude-longitude grids (e.g., Figure 1a, hereafter referred to as lat-lon grids). Depending on the numerics, methods exist to suppress this instability, and lat-lon grids may be advantageous for polar processes by representing structures with finer resolution than elsewhere in the computational domain. With the recent trend toward quasi-uniform unstructured grids, any potential benefits of lat-lon grids in polar regions may be lost (hereafter, quasi-uniform refers to approximately isotropic grids, in contrast to lat-lon grids, which are highly anisotropic due to the polar singularity). In this study, we evaluate a number of grids and dynamical cores (hereafter referred to as dycores) available in the Community Earth System Model, version 2.2 (CESM2.2; Danabasoglu et al., 2020), including new variable-resolution grids (i.e., grids with enhanced resolution over a particular region), to understand their impacts on the simulated Arctic climate. We focus specifically on the climate and surface mass balance of the Greenland Ice Sheet.

The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results

“…In the E3SMv2 atmosphere model (EAMv2), the dynamical core uses the High-Order Method Modeling Environment (HOMME) package (Dennis et al, 2005(Dennis et al, , 2011Evans et al, 2013) on the spectral element grid (Taylor and Fournier, 2010). HOMME has been updated to use a potential temperature formulation of the equations with a more accurate pressure gradient (Taylor et al, 2020;Herrington et al, 2022) and a new interpolation semi-Lagrangian scheme (Islet) for passive tracer transport (Bradley et al, 2022). The physics operates on a separate finite-volume grid (Hannah et al, 2021), which has four-ninths as many columns as the corresponding spectral element grid (see Table 1) and hence runs about 2× faster than it would on the spectral element grid.…”

Evaluating the Model Representation of Asian Summer Monsoon Upper Troposphere and Lower Stratosphere Transport and Composition Using Airborne In Situ Observations

“…A simulation from the recently developed Multi-scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0, Schwantes et al, 2022) is also evaluated in this study. In contrast to WACCM, MUSICA uses a spectral element dynamical core built on unstructured grid meshes (Herrington et al, 2019(Herrington et al, , 2022Lauritzen et al, 2018). This dynamical core gives MUSICA the capability for user-customized horizontal grid refinement to improve sampling over a region of interest.…”

Resolving weather fronts increases the large-scale circulation response to Gulf Stream SST anomalies in variable-resolution CESM2 simulations

“…Our simulations use the Community Earth System Model version 2.2 (Danabasoglu et al, 2020;Herrington et al, 2022). Specifically, they use the Community Atmospheric Model version 6 (CAM6), with the spectral element (SE) dynamical core (P. H. Lauritzen et al, 2018), coupled to a data ocean (specified SST) and the Community Land Model version 5 (Lawrence et al, 2019).…”

“…All simulations were performed on the Cheyenne Supercomputer (Computational and Information Systems Laboratory, 2019). Based on the known scaling behavior of variableresolution CAM-SE (discussed in Herrington et al, 2022), we chose a relatively small number of nodes (30 nodes; 1080 cores) for the NATLx8 simulations for efficiency, because we were compute-time rather than throughput limited. The computational cost (including I/O) was approx.…”

“…In this work, we use variable-resolution (VR) simulations, where resolution is enhanced only in the region of interest, to evaluate the potential benefit of resolving mesoscale processes for atmospheric predictability stemming from persistent SSTs. VR modeling is widely used in weather forecasting (e.g., Buizza et al (2007)), but it is only starting to be explored for simulating climate variability and change (e.g., Zarzycki & Jablonowski, 2014;Zarzycki et al, 2015;van Kampenhout et al, 2019;Herrington et al, 2022;Wijngaard et al, 2023) and has not yet been used to study the influence of midlatitude SST anomalies on the atmospheric circulation. Here, we use VR configurations of the spectral element dynamical core in the Community Atmosphere Model (CAM-SE; P. H. Lauritzen et al, 2018), with 14-km (∼ 1 /8…”