May 30, 2017
Feature

Clearing Up the Gray Zone for Convection

Scientists optimized scale-aware convection modeling to sharpen simulations of U.S. summer precipitation

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Ominous-looking storm clouds, a familiar sight in the Midwest during the summer.

Midwestern summer storms are notorious for their ferocity. They dump heavy rain or hail on everything in their path, moving on as swiftly as they roll in. However, simulating the turbulence of these convective storms, including intense updrafts and high rain rates, has long been a gray area for climate models.

Motivated by recent model resolution improvements, Pacific Northwest National Laboratory scientists conducted climate simulations at several model resolutions over the lower 48 United States. Their model realistically simulated summer precipitation when its grid size was reduced to 4 by 4 kilometers (about 2.5 by 2.5 miles), well below a 100 km grid size. Scientists also found that realistic simulations could occur with larger grid sizes using convection formulations that are less sensitive to grid sizes. These two approaches hold promise for improving current and future simulations of precipitation.

Why It Matters: The Earth's water cycle supports life and activities we might take for granted, such as swimming in the river on a hot day or enjoying a bountiful winter snowpack. Gathering more accurate information about rain and snow through better precipitation simulations will help people answer questions about how crops will grow or whether reservoirs will be full in the future.

Limitations in computing speed and capacity, as well as model representations of convection, have created a "gray zone" in resolution for modeling convection and precipitation around the globe. In the gray zone, traditional convective parameterizations (simplified formulas representing complex processes) are not valid by convection is not yet explicitly resolved. With recent advances in computing, scientists are helping to bridge the gray zone. They can simulate the climate over smaller and smaller grid sizes.

Meanwhile, the search for better convection representations—some of the toughest to do—has led modelers to "scale-aware" approaches applicable across a wide range of grid sizes. The scientists in this study showed that modeling at 4 km grid spacing to explicitly resolve convection or representing convection using scale-aware representations produces skillful precipitation simulations. The research will help reveal more strategies to advance Earth system modeling.

Methods: PNNL scientists evaluated the effects of model resolution and convective representations across gray zone resolutions (approximately between 4 km and 15 km). Researchers conducted simulations using the Weather Research and Forecasting model at resolutions of 36 km, 12 km, and 4 km for two summers over the contiguous 48 states.

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How the precipitation cycle varies during the day over the Great Plains

The convection-permitting simulations at 4 km grid spacing proved to be most capable in reproducing the location and intensity of precipitation and its sub-daily variability—during the times of day when different areas experience rain; e.g., one area might get it only at night and another mostly during the afternoon. Researchers analyzed notable differences between simulations with the traditional and scale-aware convection formulations. Combining convection-permitting modeling and scale-aware physical representations less sensitive to resolution improved simulations of the nocturnal timing of precipitation in the Great Plains and North American monsoon regions. Their design showed the scale-aware representation is less sensitive to model resolution compared with the traditional method. Researchers also performed analyses to understand the commonly found warm bias—an offset from observations that leans toward warmer temperatures—in the Southern Great Plains.

Overall, the research demonstrated notable improvements in simulating summer rainfall and its sub-daily variability. These resources will lead to better simulation of the water cycle process in models that simulate the Earth system.

What's Next: Convection strongly interacts with large-scale atmospheric circulations. Researchers will evaluate convection-permitting modeling and scale-aware representations in a global variable-resolution modeling framework to improve convection simulation and its feedbacks on large-scale circulation. This is important for understanding how warming may affect convection and associated extreme precipitation.

Acknowledgements

Sponsor: The Department of Energy's Office of Science, Biological and Environmental Research supported this research as part of the Regional and Global Climate Modeling program for the Water Cycle and Climate Extremes Modeling (WACCEM) Scientific Focus Area.  

User Facility: Computational resources from the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility. PNNL's Institutional Computing program provided additional computational resources.

Reference: Gao Y, LR Leung, C Zhao, and S Hagos. 2017. "Sensitivity of U.S. Summer Precipitation to Model Resolution and Convective Parameterizations Across Gray Zone Resolutions." Journal of Geophysical Research: Atmospheres 122:2714-2733. DOI: 10.1002/2016JD025896

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About PNNL

Pacific Northwest National Laboratory draws on its distinguishing strengths in chemistry, Earth sciences, biology and data science to advance scientific knowledge and address challenges in sustainable energy and national security. Founded in 1965, PNNL is operated by Battelle for the Department of Energy’s Office of Science, which is the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, visit https://www.energy.gov/science/. For more information on PNNL, visit PNNL's News Center. Follow us on Twitter, Facebook, LinkedIn and Instagram.

Published: May 30, 2017

PNNL Research Team

Yang Gao, L. Ruby Leung, Chun Zhao, and Samson Hagos