OOI Irminger Sea Data Helping Fill Critical Gap in Climate Models

The OOI surface buoy (shown here in 2018 being serviced by the WHOI-operated research vessel Neil Armstrong) is helping to provide crucial verification of USV and satellite-based models of air-sea interaction in difficult-to-reach high-latitude waters of the North Atlantic and Arctic Oceans. Credit: James Kuo ©Woods Hole Oceanographic Institution.

Researchers at Woods Hole Oceanographic Institution (WHOI) were recently awarded a $500,000 grant from the National Oceanic and Atmospheric Administration’s (NOAA) Climate Observations and Monitoring (COM) program to develop machine learning tools to improve estimates of air-sea heat exchange in the Arctic Ocean and adjacent seas. These tools are expected to fill critical gaps in climate models, which currently show large disparities when simulating the rate of polar ice melt.

Recent advances in remote sensing technologies have provided researchers with the data they need to better understand the forces behind Arctic ice melt and the implications of that heat exchange between the ocean and the atmosphere. These real-world measurements will allow researchers to develop algorithms that will validate and improve satellite-based modeling of the Arctic and subarctic regions.

Due to the difficulty of accessing the Arctic Ocean—especially during the stormy winter months—and the complexity of measuring air-sea heat exchanges, there has previously not been enough quality data to incorporate ice melt and seasonal changes into climate models. This challenge was overcome by recent advances in long-term remote data collection at high latitudes. For the first time in 2019, an Ocean Observatories Initiative (OOI) surface buoy in the Irminger Sea collected over a year’s worth of sensor data, including icy and windy winter conditions. Located in an important area of ocean circulation, the data collected from the OOI surface buoy provides critical verification for satellite-based models.

Lisan Yu, a WHOI senior scientist and the project’s principal investigator, said a machine learning-based framework will improve the accuracy of ocean-surface forcing estimates used to model the global climate. She said it will also improve the accuracy of ice and weather forecasts in a region that is rapidly opening up to commercial exploration. WHOI Senior Scientist Al Plueddemann, who also serves as a co-principal investigator for OOI and project lead for its Coastal and Global Nodes, is a collaborator on this project.

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