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 (PNNL) used NERSC's Edison supercomputer and 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", stated Samson Hagos, atmospheric scientist at PNNL and lead author of the paper. "The variance is even more pronounced during the suppressed or dry phase of the MJO."
This study, published in theJournal of Climate, shows the importance of accurately representing the interaction of clouds with the environmental air for accurate modelling of the MJO.
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 modelling assumptions to whittle down the range of results, ultimately to gain a better grip on what makes the MJO tick.
The PNNL research team took 11 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.
This research was supported by the Department of Energy (DOE) Office of Science, Biological and Environmental Research under the Atmospheric System Research Program and the Regional and Global Climate Modelling Program. Data collected on Gan Island during the AMIE field campaign - including radar, lidar, surface MET and sounding data - were obtained from the DOE as part of the Atmospheric Radiation Measurement (ARM) Climate Research Facility.