COLUMBIA, Mo. The world's oceans cover more than 72 percent of the earth's surface, impact a major part of the carbon cycle, and contribute to variability in global climate and weather patterns. However, accurately predicting the condition of the ocean is limited by current methods. Now, researchers at the University of Missouri have applied complex statistical models to increase the accuracy of ocean forecasting that can influence the ways in which forecasters predict long-range events such as El Nińo and the lower levels of the ocean food chainone of the world's largest ecosystems.
"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 tornados," said Chris Wikle, professor of statistics in the MU College of Arts and Science. "At the same time, it is essential in producing a food chain that is a critical part of the world's fisheries."
The vastness of the world's oceans makes predicting its changes a daunting task for oceanographers and climate scientists. Scientists must use direct observations from a limited network of ocean buoys and ships combined with satellite images of various qualities to create physical and biological models of the ocean. Wikle and Ralph Milliff, a senior research associate at the University of Colorado, adopted a statistical "Bayesian hierarchical model" that allows them to combine various sources of information as well as previous scientific knowledge. Their method helped improve the prediction of sea surface temperature extremes and wind fields over the ocean, which impact important features such as the frequency of tornadoes in tornado alley and the distribution of plankton in coastal regionsa critical first stage of the ocean food chain.
"Nate Silver of The New York Times combined various sources of informat
|Contact: Jeff Sossamon|
University of Missouri-Columbia