Sharks may offer scientists an unexpected way to improve seasonal climate forecasts. A study published in npj Climate and Atmospheric Science found that temperature and depth measurements collected by electronically tagged sharks improved ocean forecast accuracy, reducing errors by as much as 40% in some dynamic regions of the Northwest Atlantic.

The research was led by Laura H. McDonnell, a postdoctoral investigator at Woods Hole Oceanographic Institution, who conducted the study while pursuing her doctorate at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science.

Researchers tagged 18 blue sharks and one shortfin mako shark, equipping them with electronic sensors that recorded water temperature, depth, and location. Over six months, the sharks transmitted more than 8,200 temperature-depth profiles, including measurements from depths approaching 2,000 meters (6,500 feet).

These observations provided valuable information from areas that conventional monitoring systems can struggle to cover. Sharks frequently travel through dynamic ocean features, including fronts and eddies, where water temperatures and currents change rapidly.

The researchers incorporated the shark-collected measurements into a seasonal climate model and compared its performance with forecasts produced without the additional data. Including the observations improved forecast accuracy, particularly in coastal and continental shelf regions.

The findings represent the first experimental integration of animal-borne sensor data into a seasonal climate model to measure its effect on forecasting performance.

Although sharks will not replace satellites, research vessels, or other ocean-monitoring technologies, their movements could complement existing systems by providing observations from otherwise under-monitored locations and depths.

More accurate ocean forecasts could benefit fisheries management, marine operations, and coastal communities by reducing uncertainty about changing conditions. Further research will determine whether the approach can be expanded across additional regions and forecasting systems.