Machine learning identifies links between world's oceans

MIT News 

Oceanographers studying the physics of the global ocean have long found themselves facing a conundrum: Fluid dynamical balances can vary greatly from point to point, rendering it difficult to make global generalizations. Factors like the wind, local topography, and meteorological exchanges make it difficult to compare one area to another. To add to the complexity, one would have to analyze billions of data points for numerous parameters -- temperature, salinity, velocity, how things change with depth, whether there is a trend present -- to pinpoint what physics are most dominant in a given region. "You would have to look at an overwhelming number of different global maps and mentally match them up to figure out what matters most where," says Maike Sonnewald, a postdoc working in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and a member of the EAPS Program in Atmospheres, Oceans and Climate (PAOC). Sonnewald, who has a background in physical oceanography and data science, uses computers to reveal connections and patterns in the ocean that would otherwise be beyond human capability.