AI unlocks Atlantic circulation insights from 20 years of ocean float data Lisa Lock Scientific Editor Andrew Zinin Chief Editor For more than 20 years, about 4,000 autonomous profiling floats have been drifting through the ocean. They form part of the international Argo program (Argo—global array of profiling floats). At regular intervals, they descend to a depth of 2,000 meters (6,600 feet) and, as they rise, measure parameters such as temperature, salinity and pressure.

Once they reach the sea surface again, they transmit these data via satellite to the Argo network, which is available to researchers worldwide. Hardly any other observation system has transformed ocean research so profoundly in recent decades. Thousands of measuring buoys, a global perspective A new study shows that these data can be analyzed far beyond their original intended use.

"We wanted to know whether we could use the scattered Argo measurements to gain insights into large-scale circulation systems that, until now, could only be recorded through very extensive measurement campaigns," says Dr. Yannick Wölker, lead author of the study and until recently a Ph.D. student in the Ocean Dynamics research unit at GEOMAR and the research group Archaeoinformatics—Data Science at Kiel University.

"Artificial intelligence opens up new possibilities here. Combining machine learning with established physical models allows us to get more out of existing measurement data and better understand how key circulation systems work." Why the Atlantic circulation is so important The study, published in Ocean Science, focuses on the Atlantic Meridional Overturning Circulation, or AMOC for short. This vast circulation system acts like a conveyor belt, transporting warm surface water northward, where it cools, sinks into the depths and flows back south as cold deep water.

The AMOC transports large amounts of heat and influences weather and climate, including in Europe. How stable this system is and how it is changing under the influence of climate change are key questions currently discussed in climate research. So far, however, direct observations have relied on just a few fixed measurement series in the Atlantic, which are technically challenging and expensive to carry out.

Learning from simulations To tackle this problem, the researchers combined two approaches: observational data and model calculations. They trained a machine learning algorithm (a subfield of artificial intelligence in which computers recognize patterns in data) using high-resolution ocean simulations. In the process, the model learned how typical ocean current patterns correlate with temperature and salinity profiles.

They then applied this knowledge to real Argo data from the Atlantic. This approach makes it possible to infer large-scale current strengths from point measurements—in particular, the so-called geostrophic component of the circulation (determined by the distribution of temperature and salinity), which has hitherto been difficult to measure continuously. Promising results, but also clear limitations The results show that the estimates calculated from the Argo data agree well with established observational series and model simulations.