FY 2024

Skillful multi-month predictions of ecosystem stressors in the surface and subsurface ocean

Mogen, S.C., N.S. Lovenduski, S. Yeager, L. Keppler, J.D. Sharp, S.J. Bograd, N. Cordero Quiros, E. Di Lorenzo, E.L. Hazen, M.G. Jacox, and M. Pozo Buil

Earth's Future, 11(11), e2023EF003605, doi: 10.1029/2023EF003605, View open access article at Wiley/AGU (external link) (2023)

Anthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long-term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi-month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation-based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making.

Plain Language Summary. Human-driven climate change is rapidly altering the global ocean, with strong warming, increasing acidity, and declining oxygen trends. On top of long-term trends, short term variations can lead to rapid changes that can have major effects on marine ecosystems. There is a growing need to predict these short-term changes in order to better inform marine fisheries managers. In this study, we use a climate model designed to predict changes in the real world months-to-years in advance to better determine our ability to forecast changes. Previous studies with similar goals have been limited by sparse observations of acidity and oxygen. We utilize brand new observational products that estimate acidity and oxygen levels in the subsurface ocean for the first time to analyze subsurface forecasts. Our results demonstrate a high potential to predict warming, acidity, and oxygen levels in key marine ecosystems with this climate model. These results suggest that there is potential for eventual operational forecasts of marine ecosystems to better inform marine managers.