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Abstract Accurate and interpretable marine heatwave (MHW) forecasts allow decision makers and industries to plan for and respond to extreme ocean temperature events. Recent work demonstrates skillful pointwise prediction of MHWs. Here, we evaluate a method of detecting and predicting spatially connected MHW objects. We apply object‐based forecast verification to the Community Earth Systems Model Seasonal‐to‐Multiyear Large Ensemble (SMYLE) experiment, a set of initialized hindcasts with 20‐member ensembles of 24‐month simulations initialized quarterly from 1970 to 2019. We demonstrate that SMYLE predicts MHWs that occur near observed MHWs with high skill at long lead times, but with errors in location, area, and intensity that grow with lead time. SMYLE exhibits improved skill in predicting the intensity of MHWs in December and January, and worse skill from August to October. This work illustrates the capacity to forecast connected MHW objects and to quantify the uncertainty in those forecasts with potential applications for future community use.
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Skillful Multi‐Month Predictions of Ecosystem Stressors in the Surface and Subsurface Ocean
Abstract 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.
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- PAR ID:
- 10476315
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth's Future
- Volume:
- 11
- Issue:
- 11
- ISSN:
- 2328-4277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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