Predicting the types of clouds over the ocean is critical for climate projections. However, current climate models lack the spatial resolution necessary to accurately characterize low-altitude marine clouds and the physical processes that affect them. A new study, by a U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER) Early Career scientist and others from DOE’s Environmental Molecular Sciences Laboratory (EMSL) and Pacific Northwest National Laboratory, shows that current climate models lacking high spatial resolution are biased toward accelerating the transition from stratocumulus clouds to cumulus clouds. Most current climate models assess cloud properties at coarse spatial scales, averaging important processes such as radiative heating and turbulence across tens to hundreds of kilometers. To explore the impact of large-scale spatial averaging, the scientists performed large eddy simulations with the Weather Research and Forecasting model using EMSL’s Chinook supercomputer. The simulations demonstrate that low spatial resolution in climate models leads to an underestimation of cloud cover, resulting in an overestimation of how much radiation reaches the sea surface. These biases also result in an underestimate of both temperature variability and turbulent mixing in the cloud layer. In short, models that average across large distances and thus neglect small-scale interactions between radiation and turbulence accelerate the transition from stratocumulus clouds to cumulus clouds. These low-cloud biases contribute significantly to uncertainties in climate projections, underscoring the need for models to adopt ways to estimate the impact of small-scale radiation and turbulence interactions that affect clouds and thus improve climate projections.
Reference: Xiao, H., W. I. Gustafson Jr., and H. Wang. 2014. “Impact of Subgrid-Scale Radiative Heating Variability on the Stratocumulus-to-Trade Cumulus Transition in Climate Models,” Journal of Geophysical Research: Atmospheres 119(7), 4192–203. DOI:10.1002/2013JD020999. (Reference link)
Acknowledgement: This project was funded by a BER Early Career grant award and DOE’s Earth System Modeling Program.
Contact: Paul E. Bayer, SC-23.1, (301) 903-5324
SC-33.1 Earth and Environmental Sciences Division, BER
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