A study provides substantial insight into the model errors in clouds and precipitation and guidance for future E3SM development.
The DOE Energy Exascale Earth System Model (E3SM) is aimed to develop a high-resolution Earth system model specifically targeting next-generation DOE supercomputers to meet the science needs of the nation and the mission needs of DOE. The increase of model resolution along with improvements in representing cloud and convective processes in the E3SM atmosphere model version 1 (EAMv1) has led to quite significant model behavior changes from its earlier version (EAMv0), particularly in simulated clouds and precipitation. To understand what causes the model behavior changes and provide guidance to future E3SM developments, a research team led by scientists at the DOE Lawrence Livermore National Laboratory, Brookhaven National Laboratory, Pacific Northwest National Laboratory, and Oak Ridge National Laboratory along with their institutional collaborators conducted a number of sensitivity experiments to isolate the impact of changes in model physics, resolution, and parameter choices on these changes and offered thoughtful discussion on the underlying physical processes.
Accurately projecting future changes in clouds and precipitation is critical for an Earth System Model (ESM), however, large model errors remain in current ESMs, including the newly developed DOE E3SM. This study performed an in-depth analysis of errors in the E3SM simulated clouds and precipitation fields and discussed the underlying physical processes. Results from this study provide substantial insight into the model errors and guidance for future E3SM development.
This study conducted several sensitivity experiments to isolate the impact of changes in model physics, resolution, and parameter choices on these differences. The overall improvement in EAMv1 clouds and precipitation is primarily attributed to the introduction of a simplified third-order turbulence parameterization (CLUBB; Cloud Layers Unified By Binormals) (along with the companion changes) for a unified treatment of boundary layer turbulence, shallow convection, and cloud macrophysics, though it also leads to a reduction in subtropical coastal stratocumulus clouds (Sc). This lack of Sc is considerably improved by increasing vertical resolution from 30 to 72 layers, but the gain is unfortunately subsequently offset by other retuning to reach the top-of-atmosphere (TOA) energy balance. Increasing vertical resolution also results in a considerable underestimation of high clouds over the Tropical Warm Pool, primarily due to the selection for numerical stability of a higher air parcel launch level in the deep convection scheme. Increasing horizontal resolution from 1° to 0.25° without retuning leads to considerable degradation in cloud and precipitation fields, with much weaker tropical and subtropical short- and longwave cloud radiative forcing and much stronger precipitation in the intertropical convergence zone, indicating poor scale-awareness of the cloud parameterizations. To avoid this degradation, significantly different parameter settings for the low-resolution (1°) and high-resolution (0.25°) were required to achieve optimal performance in EAMv1.
Contacts (BER PM)
Earth and Environmental Systems Modeling
Lawrence Livermore National Laboratory
This research was primarily supported as part of the Energy Exascale Earth System Model (E3SM) project and partially supported by the Climate Model Development and Validation activity, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research.
Xie, S., W. Lin, P. Rasch, et al. “Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model.” Journal of Advances in Modeling Earth Systems 10(10), 2618-2644 (2018). [DOI: 10.1029/2018MS001350]
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]
May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]
May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]
May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]
Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]
List all highlights (possible long download time)