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March 2016

Busted: Revealing Mismatches in MJO Modeling

PNNL research uncovers the sources that prevent modeled precipitation from matching real-world results

MJO provokes monsoons that hit Calcutta and storms that batter Seattle
MJO's Impact From Calcutta, India, to Seattle, Wash. and beyond, the MJO can bring storms and plenty of rain in wet years. But the impact of a warming climate on the MJO is something that scientists are working to understand, because many regions of the world rely on the precipitation it brings. zoomEnlarge Image.

An Alaska-sized pulse of clouds and precipitation congregates over the Indian Ocean and lumbers east. Soon it will be mustering monsoons over Mumbai and developing downpours for Portland. So big, so enigmatic, it has a name: Madden-Julian Oscillation—MJO for short.

The MJO occurs on its own timetable—every 30 to 60 days—but its worldwide impact spurs scientists to unlock its secrets. The ultimate answer? Timely preparation for the precipitation havoc it brings—and insight into how it will behave when pressured by a warming climate.

Results: Although it was identified and named in the 1970s, the MJO continues to be a challenge to simulate and predict. Working to reveal the MJO's cycle secrets, a research team from Pacific Northwest National Laboratory used data gathered during a field campaign over the Pacific Ocean to identify the processes that are responsible for too much precipitation in the models especially during the low-rainfall period of the MJO signal. They found the mismatches are related to the fact that most of the models get the relationship between environmental moisture and precipitation wrong, producing more precipitation than is observed for the same moisture content in the environment, especially in drier environments. 

"This error, which is related to representing how clouds entrain (mix with) the environmental moisture contains up to 30% of the overall model precipitation error." said Dr. Samson Hagos, atmospheric scientist and lead author of the paper. "The variance is even more pronounced during the suppressed or dry phase of the MJO."

This study shows the importance of accurately representing the interaction of clouds with the environmental air for accurate modeling of the MJO.

Why It Matters: Better understanding of the MJO is vital. The MJO's unpredictability makes it harder for weather forecasting in western India and points east. Because many parts of the world rely on monsoon rain for their yearly water supply, preparation for more or less rain is critical. For the US West Coast, the MJO can influence the El Niño/La Niña cycle which affects the probability of floods and droughts in those states.

In fact, the MJO has worldwide precipitation influence, stretching all the way to the African continent, especially for Sub-Saharan and Sahel regions. The MJO's influence in all these regions can affect crops, infrastructure, and daily survival for some. Scientists in this study are identifying modeling assumptions to whittle down the range of results, ultimately to gain a better grip on what makes the MJO tick.

Methods: The PNNL research team took eleven regional and global models to perform simulations with simplified representations of climate variables as well as representation of convection forces. They quantified the range of possible answers using a linear statistical model on various specific processes governing the overall performance of the simulations in capturing the MJO. They found that the relationship of precipitation to the moisture content of an atmospheric column (which is related to entrainment and detrainment processes) is an important source of the wide range of possible answers.

What's Next? This study examined the MJO as it moves over the Indian Ocean. The next step is to study how it propagates across the Southeast Asia where it is affected by the daily cycle of heating over the Pacific islands.

Acknowledgments

Sponsors: This research was supported by the Department of Energy, Office of Science, Biological and Environmental Research under the Atmospheric System Research Program, and the Regional and Global Climate Modeling Program.

Data Use: Data collected on Gan Island during the AMIE field campaign, including radar, lidar, surface MET, and sounding data, were obtained from the Department of Energy as part of the Atmospheric Radiation Measurement (ARM) Climate Research Facility. The ARM variational analysis forcing data for AMIE/DYNAMO can be accessed at http://www.arm.gov/data/eval/29. The CombRet retrieval can be accessed at http://dx.doi.org/10.5439/1169498. The DYNAMO field campaign data used in this paper is available at NCAR's Earth Observing Laboratory's DYNAMO Data Catalogue https://www.eol.ucar.edu/field_projects/dynamo. The RAMA buoy data can be obtained from NOAA's website: http://www.pmel.noaa.gov/tao/rama/data.html.

User Facility: The ARM Climate Research Facility's AMIE field campaign; Computing resources for the simulations were provided by National Energy Research Scientific Computing Center (NERSC).

Research Team: Samson Hagos, Zhe Feng, Casey Burleyson, Chun Zhao, Matus Martini, and Larry K. Berg, PNNL

Research Area: Climate & Earth Systems Science

Reference: Hagos SM, Z Feng, CD Burleyson, C Zhao, MN Martini, and LK Berg. 2016. "Moist Process Biases in Simulations of the Madden-Julian Oscillation Episodes Observed during the AMIE/DYNAMO Field Campaign." Journal of Climate 29(3): 1091-1107. DOI: 10.1175/JCLI-D-15-0349.1

Related Highlights: Tall Clouds from Tiny Raindrops Grow; On the Right Track for Tropical Clouds; The Long and Rich Life of Tropical Clouds; Rain and Cloud Resistance; Mastering the Mysteries of the MJO

March 9, 2016

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In short...

In one sentence: A research team from Pacific Northwest National Laboratory identified mismatches in models that represent moisture for a large climate pattern and found they are related to the fact that most of the models get the relationship between environmental moisture and precipitation wrong, producing more rain than is observed for the same moisture content in the environment, especially in drier environments.

In 100 characters: Accurate accounting of cloud interactions with environmental air enables better MJO modeling

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