A statistical model yields insight into the ocean’s deep secrets.
Model organisms “when beholding the tranquil beauty and brilliancy of the ocean’s skin, one forgets the tiger heart that pants beneath it…” So observed Herman Melville, a man who knew a thing or two about our planet’s briny deep. These days, global climate change has the potential to quicken the pace of this “tiger heart,” say scientists, a development that could profoundly affect even those of us who’ve never set foot on a ship at sea.
“The ocean really is the most important part of the world’s environmental system because of its potential to store carbon and heat, but also because of its ability to influence major atmospheric weather events such as droughts, hurricanes and tornadoes,” said Chris Wikle, professor of statistics at MU.
Wikle’s research involves using data-driven models to divine the oceans’ opaque ways and means. It’s not easy. The vastness of the world’s seas makes predicting their behavior a daunting task. To improve the odds Wikle and a colleague, Ralph Milliff, senior research associate at the University of Colorado, are banking on a powerful statistical tool, the “Bayesian hierarchical model.” It is essentially a means of ordering variables in ways that yield insights into phenomena that might otherwise seem random, while at the same time accounting for dependence and uncertainty in all aspects of the problem. For their ocean analyses, Wikle and Milliff deploy it to make sense of the sometimes bewildering array of direct observational data recorded by ocean buoys, ships and satellite images.
The objective of their current project, Wikle says, is to improve the prediction of surface temperature extremes and wind fields over the ocean. Advancing the accuracy of such predictions will, he says, allow forecasters to do a better job foreseeing a range of diverse phenomena; events as disparate as the appearance of tornadoes in the Midwest and the movements of plankton in coastal regions.
“Nate Silver of The New York Times combined various sources of information to understand and better predict the uncertainty associated with elections,” Wikle says. “So, much like that, we developed more sophisticated statistical methods to combine various sources of data — satellite images, data from ocean buoys and ships, and scientific experience — to better understand the atmosphere over the ocean and the ocean itself.”
This modeling, he said, has also helped to further forecasters in nations affected by weather from the Mediterranean Sea and, closer to home, has assisted in advancing “long-lead-time predictions” of El Niño and La Niña.
El Niño is a band of warm ocean water that periodically develops off the western coast of South America. It typically causes climate effects across the Pacific Ocean and the U.S. Niña, its cold-water counterpart, also creates atmospheric changes. “Missouri, like most of the world, is affected by El Niño and La Niña,” Wikle says.
The study was funded in part by the National Science Foundation and the Office of Naval Research and was published in the journal Oceanography and Statistical Science.