Evaluating EAMv2 simulated stratiform mixed-phase cloud properties at Northern and Southern high latitudes against ARM measurements

Abstract

This study evaluates high-latitude stratiform mixed-phase clouds (SMPC) in the atmosphere model of the newly released Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one-year-long ground-based remote sensing measurements from the U.S. Department of Energy Atmospheric Radiation and Measurement (ARM) Program. A nudging approach is applied to model simulations for a better comparison with the ARM observations. Observed and modeled SMPCs are collocated to evaluate their macro- and microphysical properties at the ARM North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates (underestimates) SMPC frequency of occurrence at the NSA (AWR) site nearly all year round. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud boundaries and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phases are substantially biased at each location. The model largely overestimates liquid water path at NSA, whereas it is frequently underestimated at AWR. Meanwhile, the simulated ice water path is underestimated at NSA, but at AWR, it is comparable to observations. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that continuous improvement in cloud microphysics is needed for high-latitude mixed-phase clouds.

Evaluating EAMv2 Simulated High Latitude Clouds Using ARM Measurements in the Northern and Southern Hemispheres

“…The U.S. DOE E3SMv2 (E3SM Project, DOE, 2021, September 29) model was used in the creation of this manuscript. The model data used in this study can be accessible at https://doi.org/10.5281/zenodo.7921892 (Zhang et al, 2023). The ARM observational data are available online at https://www.arm.gov/data.…”