How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?

This study assesses a 40-day 3.25-km global simulation of the Simple Cloud-Resolving E3SM Model (SCREAMv0) using high-resolution ground-based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid-level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land-atmosphere coupling. This study highlights the potential use of high-resolution ground-based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process-oriented model sensitivity tests and detailed analyses. This research examines how well a kilometer grid scale global atmospheric model-the Simple Cloud-Resolving Energy Exascale Earth System Model (SCREAMv0)-performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high-resolution ground-based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle-level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground-based observations over a tropical rainforest SCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid-level congestus SCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed