Skip to Main Content U.S. Department of Energy
Science Directorate
Page 59 of 740

Atmospheric Sciences & Global Change
Research Highlights

April 2018

How Small-Scale Processes in Clouds Influence Large-Scale Precipitation Variability and Extremes

Observed large-scale rainfall statistics could be used to directly constrain small-scale microphysical parameters in models.

In climate models, the representation of cloud microphysical processes strongly influences precipitation variability and extremes—important aspects of the water cycle. Enlarge Image.

The Science

To accurately simulate and predict precipitation, particularly when it is extreme, it is critical to understand how in-cloud microphysical processes, such as condensation of vapor and evaporation of rain and cloud particles, cascade up to influence large-scale precipitation variability. However, because these influences are non-linear and cross a broad range of spatial scales, arriving at this understanding is challenging.

Using high-resolution modeling with theoretical and statistical analysis, a research team led by scientists at the U.S. Department of Energy's Pacific Northwest National Laboratory revealed a direct link between the in-cloud processes and the frequency of precipitation extremes. Their findings led to a new approach for using observations to constrain the representation of cloud microphysical processes in Earth system models.

The Impact

Precipitation variability and extremes are important aspects of the water cycle that have direct societal implications ranging from water resource management to emergency response. In climate models, these processes are strongly influenced by how cloud microphysical processes are represented.

The approach developed in this study would allow the use of readily available remote sensing observations of large-scale rainfall statistics to estimate difficult-to-observe, small-scale in-cloud parameters.


Researchers sought to provide a theoretical ground for interpreting the sensitivities of precipitation statistics to changes in microphysical parameters, and used observations to constrain those parameters. The researchers simulated rainfall associated with a Madden-Julian Oscillation event—a major fluctuation in tropical weather on weekly to monthly timescales—using the Model for Prediction Across Scales-Atmosphere with a refined region at 4-kilometer grid spacing over the Indian Ocean.

The simulation revealed that because cloud microphysical processes regulate precipitable water (water vapor throughout an atmospheric column), and because of the non-linear relationship between precipitation and precipitable water, the amount of precipitable water above a certain critical threshold contributes disproportionately to precipitation variability. However, the frequency of precipitable water exceeding the threshold decreases rapidly as a function of precipitable water vapor. Therefore, changes in microphysical processes that shift the statistics even slightly relative to the threshold have large effects on precipitation variability. Furthermore, precipitation variance and extreme precipitation frequency are approximately linearly related to the difference between the mean and critical precipitable water threshold.

Thus, using radar observations from the Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign that took place in 2011 and 2012 over the equatorial Indian Ocean, researchers demonstrated that observed large-scale precipitation statistics could be used to directly constrain small-scale microphysical parameters in models.


Sponsors: The U.S. Department of Energy (DOE) Office of Science, Biological and Environmental Research supported this research as part of the Regional and Global Climate Modeling program. Chun Zhao is supported by the "Thousand Talents Plan for Young Professionals" program of China.

User Facility: The research used computational resources at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility.

Research Area: Climate and Earth Systems Science

Research Team: Samson Hagos, L. Ruby Leung, Zhe Feng, and Koichi Sakaguchi, PNNL; and Chun Zhao, University of Science and Technology of China

Reference: S. Hagos, L.R. Leung, C. Zhao, Z. Feng, K. Sakaguchi, "How Do Microphysical Processes Influence Large-Scale Precipitation Variability and Extremes?" Geophysical Research Letters 45, 1661-1667 (2018). [DOI: 10.1002/2017GL076375]

Page 59 of 740

Science at PNNL

Core Research Areas

User Facilities

Centers & Institutes

Additional Information

Research Highlights Home


Print this page (?)

YouTube Facebook Flickr TwitThis LinkedIn