17 December 2014

Tall Clouds from Tiny Raindrops Grow

Science

Big clouds get bigger and small clouds shrink may seem like a simple statement, but the myriad mechanisms behind how clouds are born, grow, and die are surprisingly complex. These very mechanisms may be key to understanding future weather patterns and global climate change. In a study published in the Journal of Geophysical Research: Atmospheres, a team led by scientists from Pacific Northwest National Laboratory examined how well a state-of-the-art high-resolution model simulated tropical clouds and their interaction with the warm ocean surface compared to real-world observations. They found that factors as small as how sizes of raindrops were represented in the model made a big difference in the accuracy of the results.

Approach

Scientists from PNNL and their collaborators from NASA Goddard Space Flight Center and the National Center for Atmospheric Research used high-resolution regional models to simulate cloud lifecycles with different ranges of rain-drop size. They compared those results to observational data collected from the Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Madden-Julian Oscillation Investigation Experiment (AMIE) and the Earth Observing Laboratory’s Dynamics of the Madden-Julian Oscillation (DYNAMO) research campaign. The AMIE/DYNAMO campaign collected data in the South Pacific from October 2011 to March 2012 to support studies on the birth, growth, and evolution of certain types of clouds.

The scientists used satellite and ground-based radar measurements from the campaign to examine how well the Weather Research and Forecasting Model simulated tropical clouds. They looked specifically at four approaches to handling the physics of tiny raindrops. Two key factors were rain rates at ground level and the modeling of cold pools, the cold dry air flowing out from deep thunderstorms. The simulations clearly showed that larger clouds tended to grow larger because they capture less dry air, while smaller clouds dwindled away. These simulations matched observations. On the other hand, the model faltered when simulating cold pools. The team modified the way raindrop physics was simulated, yielding results that more closely matched observations.

Impact

Scientists need to understand clouds to effectively predict weather patterns, including the potential for droughts and floods. Until recently, computer models for simulating climate on a global scale relied on mathematical formulas to approximate how clouds were born and grew. Those formulas did not always reflect reality. With more advanced computers came the ability to explicitly simulate large-cloud systems instead of approximating them. Still, scientists wondered whether results from those high-resolution models were accurate compared with real-world data. This study was an important first step in identifying the modifications needed to make sure these cutting-edge models are up to the challenge of simulating the true lifecycle of clouds.

“Our study highlights the utility of using observations to evaluate how well the next generation of climate models, called cloud-permitting models, can directly capture the behavior of clouds,” said Samson Hagos, the PNNL scientist who led the study. “While these newer models cannot completely overcome some challenges, they do represent cloud size-depth relationships fairly well.”

Summary

Traditionally, convective clouds are represented in regional and global climate models through parameterizations. With increased efficient computational resources and scalability, realistic modeling is becoming plausible without the need for cumulus parameterization. A research team, led by DOE scientists at Pacific Northwest National Laboratory, tested regional convection-permitting model simulations of cloud populations observed during the 2011 Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation Investigation Experiment/Dynamics of the Madden-Julian Oscillation Experiment (AMIE/DYNAMO) field campaign. Results were evaluated against ground-based radar and ship-based observations. They examined the sensitivity of model simulated reflectivity, surface rain rate, and cold pool statistics to variations of raindrop breakup/self-collection parameters in four state-of-the-art two-moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model. Both the radar observations and model simulations of cloud populations show an approximate power-law relationship between convective echo-top height and equivalent convective cell radius. However, the model simulations generally overestimated reflectivity from large and deep convective cells, and underestimated stratiform rain and the frequency of cold pools. In the sensitivity experiments, introduction of more aggressive raindrop breakup or decreasing the self-collection efficiency increased the cold pool occurrence frequency in all of the simulations, and slightly reduced the reflectivity and precipitation statistics bias in some schemes. However, this had little effect over mean precipitation bias. 

Contact
Samson Hagos
Pacific Northwest National Laboratory (PNNL)
Publications
Hagos, S, Z Feng, CD Burleyson, KS Lim, CN Long, D Wu, and G Thompson.  2014.  "Evaluation of Convection-Permitting Model Simulations of Cloud Populations Associated with the Madden-Julian Oscillation using Data Collected During the AMIE/DYNAMO Field Campaign."  Journal of Geophysical Research Atmospheres 119(21): 12052-12068.  https://doi.org/10.1002/2014JD022143.
Acknowledgments

Sponsors: The U.S. Department of Energy’s Office of Science, Office of Biological and Environmental Research, sponsored this work as part of the Regional and Global Climate Modeling Program and Atmospheric System Research Program.

Facilities: Scientists obtained data from the ARM Climate Research Facility and the National Center for Atmospheric Research Earth Observing Laboratory’s DYNAMO Data Catalog. The National Energy Research Scientific Computing Center and Oak Ridge Leadership Computing Facility provided computing resources for simulations.

Research Team: Samson Hagos, Zhe Feng, Casey Burleyson, Kyo-Sun Sunny Lim, and Chuck Long, PNNL; Di Wu, NASA Goddard Space Flight Center; and Greg Thompson, National Center for Atmospheric Research.