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<p>Accurate ocean forecasts require sufficient observations to resolve key processes, yet conventional observing systems often miss fine-scale variability in dynamic ocean regions. Top predators frequently target these features, offering an opportunity for instrumented animals to sample underrepresented areas. Here, we use sharks equipped with depth- and temperature-sensing satellite tags as opportunistic ocean observers to reduce climate forecast errors in a proof-of-concept model experiment. We compiled &gt;8200 high-resolution shark-derived depth–temperature profiles from the Northwest Atlantic Ocean and used these data to inform an operational forecasting model. Retrospective forecasts incorporating shark-derived observations showed up to 40% lower surface temperature error than control forecasts when compared against reference satellite observations and ocean reanalysis products. Forecast improvements from shark-derived measurements were strongest in dynamic shelf and slope regions that traditional observing approaches often under-sample. These results demonstrate the potential for animal-borne observations to strengthen operational forecasting and capture complex, ecologically important dynamics in a changing ocean.</p>

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Improved seasonal climate forecasting using shark-borne sensor data in a dynamic ocean

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Laura H. McDonnell,
  • Ben P. Kirtman,
  • Camrin D. Braun,
  • Neil Hammerschlag

摘要

Accurate ocean forecasts require sufficient observations to resolve key processes, yet conventional observing systems often miss fine-scale variability in dynamic ocean regions. Top predators frequently target these features, offering an opportunity for instrumented animals to sample underrepresented areas. Here, we use sharks equipped with depth- and temperature-sensing satellite tags as opportunistic ocean observers to reduce climate forecast errors in a proof-of-concept model experiment. We compiled >8200 high-resolution shark-derived depth–temperature profiles from the Northwest Atlantic Ocean and used these data to inform an operational forecasting model. Retrospective forecasts incorporating shark-derived observations showed up to 40% lower surface temperature error than control forecasts when compared against reference satellite observations and ocean reanalysis products. Forecast improvements from shark-derived measurements were strongest in dynamic shelf and slope regions that traditional observing approaches often under-sample. These results demonstrate the potential for animal-borne observations to strengthen operational forecasting and capture complex, ecologically important dynamics in a changing ocean.