Reed, Kevin A., and Silvers, Levi G., and Wing, Allison A. and Hu, I-Kuan and Medeiros, Brian, : Using Radiative Convective Equilibrium to Explore Clouds and Climate in the Community Atmosphere Model. Journal of Advances in Modeling Earth Systems, 13, e2021MS002539 , https://doi.org/10.1029/2021MS002539
Key Points
Plain Language Summary
Clouds, circulations and rainfall in the tropics play an important role in Earth's climate. However, global climate models differ in their representation of these features, contributing to uncertainty in future climate projections. One useful tool to better understand model differences and inform efforts to improve models is to analyze idealized configurations. We explore two different numerical representations of an idealized atmosphere relevant to tropical regions to determine the impact on the characteristics of clouds, rainfall and circulations as well as the tropical atmospheric response to warming. We show that our idealized models mirror differences in the low-cloud structure in the deep tropics of more realistic models. This work also finds similarities between the two numerical representations, such as a decrease in the spatial extent of high altitude clouds with warming. As the surface is warmed, both versions also show increases in the clustering of clouds, the likelihood of extreme precipitation rates, and an estimate of how much warming would occur in response to a doubling of carbon dioxide.
Abstract
Abstract Characteristics of, and fundamental differences between, the radiative-convective equilibrium (RCE) climate states following the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts.
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Acknowledgments
Reed and Silvers acknowledge support from NSF award number 1830729, Wing acknowledges support from NSF award number 1830724, Hu acknowledges sup- port from NSF award number 1917328, and Medeiros acknowledges support from the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy via NSF IA 1844590. This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. The authors would also like to acknowledge Dr. Colin Zarzycki (Pennsylvania State University), Dr. Vincent Larson (University of Wisconsin-Milwaukee), Dr. Andrew Gettelman (NCAR) and Dr. Adam Herrington (NCAR) for guidance in exploring sensitivities in the CAM6 simulations, Gary Strand (NCAR) for help with formatting CAM output for RCEMIP, and Cheryl Craig (NCAR) for software engineering related to the QPRCEMIP compset in CESM. Finally, the authors would also like to thank four anonymous reviewers for their constructive and thorough reviews. We would like to acknowledge high-performance computing support from Cheyenne (https:// doi.org/10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation.