Oware et al. (2025) Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2025
• Date: 2025-10-18
• Authors: Raymond Kwaku Twumasi Oware, Youtong Zheng, Peter Bogenschutz, Yunyan Zhang, Hsi‐Yen Ma, Shaocheng Xie, Cheng Tao
• DOI: 10.1029/2025jd044113

Research Groups

E3SM (Energy Exascale Earth System Model) project team; atmospheric modeling groups involved in SCREAM development and analysis.

Short Summary

This study evaluates the performance of the Doubly Periodic Simple Cloud-Resolving E3SM Atmosphere Model (DP-SCREAM) in simulating deep convection in the coastal Houston region, finding it reproduces diurnal cycles well but exhibits persistent biases in cloud representation that are partially addressed by sensitivity experiments on mixing length and buoyancy flux.

Objective

• To assess the performance of the Doubly Periodic Simple Cloud-Resolving E3SM Atmosphere Model (DP-SCREAM) in simulating deep convection in a coastal environment (Houston) using TRACER campaign observations, and to investigate persistent model biases through sensitivity experiments.

Study Configuration

• Spatial Scale: Coastal region (Houston, USA); cloud-resolving scale; sensitivity tests at 0.5 km horizontal grid spacing.
• Temporal Scale: Focus on diurnal cycles of clouds and precipitation, covering the period of the TRACER campaign.

Methodology and Data

• Models used: Doubly Periodic Simple Cloud-Resolving E3SM Atmosphere Model (DP-SCREAM), E3SM single column model, Simplified Higher Order Closure scheme.
• Data sources: Observations from the TRACER (TRacking Aerosol Convection interactions ExpeRiment) campaign; external forcing data sets.

Main Results

• DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model.
• DP-SCREAM is applicable to coastal regions, partly due to forcing data sets already capturing the influence of breezes.
• Persistent biases in DP-SCREAM (and the global version of SCREAM) include: underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and "popcorn convection" characterized by small and disorganized convective cells generating the strongest precipitation.
• Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence.
• Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells.
• A refined horizontal resolution of 0.5 km alone was insufficient to resolve these biases.

Contributions

• First evaluation of DP-SCREAM's performance in a coastal environment, demonstrating its applicability.
• Identifies and characterizes specific persistent biases in cloud and convection representation within the SCREAM framework.
• Provides insights into the physical mechanisms behind these biases through targeted sensitivity experiments on mixing length and buoyancy flux.
• Highlights the limitations of increased horizontal resolution alone in addressing certain model biases, suggesting the need for improved physics parameterizations.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Oware2025Evaluating,
  author = {Oware, Raymond Kwaku Twumasi and Zheng, Youtong and Bogenschutz, Peter and Zhang, Yunyan and Ma, Hsi‐Yen and Xie, Shaocheng and Tao, Cheng},
  title = {Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2025},
  doi = {10.1029/2025jd044113},
  url = {https://doi.org/10.1029/2025jd044113}
}