B13. Cloud Deck Spatial Errors in the EAMv1

Title

Subtropical Marine Stratocumulus Cloud Deck Spatial Errors in the E3SMv1 Atmosphere Model

Authors

Michael A. Brunke (The University of Arizona), Po-Lun Ma (Pacific Northwest National Laboratory), J. E. Jack Reeves Eyre (The University of Arizona), Philip J. Rasch (Pacific Northwest National Laboratory), Armin Sorooshian (The University of Arizona), Xubin Zeng (The University of Arizona)

Abstract

Subtropical marine low-level clouds continue to be poorly simulated in models despite many studies and field experiments devoted to their improvement. Many of these previous studies have noted the lack of simulated clouds in the regions where the subtropical marine stratocumulus cloud decks should be, implying amplitude errors, but it is also recognized that these decks have spatial errors. Here we focus on the spatial errors in the Atmospheric Model Intercomparison Project (AMIP) run of version 1 of the Energy Exascale Earth System Model (E3SMv1) developed by the Department of Energy relative to the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) climatology. Location errors of the cloud decks are characterized by centroid distances, while size errors are quantified by area ratios. The combined effects of location, size, and shape errors are measured by overlap ratios. E3SMv1’s spatial errors are compared to those of three other U.S. climate and Earth system models.

Model dynamics is better simulated than clouds in E3SMv1. Therefore, the spatial errors in the AMIP run are attributed primarily to model physics. To gain additional insight, we performed a sensitivity run in which model winds were nudged to those of MERRA-2. This results in a large change (but not necessarily an improvement) in the simulated cloud decks that are mainly due to the interactions between model dynamics and physics, since the same physical parameterizations are used in both runs. These results suggest that both model physics (widely recognized) and its interaction with dynamics (less recognized) are important to model improvement in simulating these low-level clouds.