The Northeast U.S. Shelf (NES) Large Marine Ecosystem (LME) supports some of the most commercially valuable fisheries in the world and has experienced dramatic ecosystem change in response to fishing pressure, climate variability and climate change, the combined effects of which create a huge challenge for the fisheries stock assessment in this region. Fisheries stock assessments describe the past and current population abundance of a fish population and importantly, develop a forecast for future population growth. Most stock assessment forecasts are used to set Annual Catch Limits over a 6–36 month scale. This forecast has been typically based on the biology of the fish and is highly uncertain. Incorporating physical environmental variables into the stock assessment population model and subsequent forecast could improve model performance and reduce uncertainty in future population size.
Therefore, a reliable prediction of the NES environmental variables, such as the ocean temperature, could lead to a significant improvement of the fisheries stock assessment. However, the current generation climate model-based seasonal-to-interannual predictions exhibit a limited prediction skill in the coastal environment. On the other hand, recent studies by the PIs reported statistically significant correlations between the NES temperature and the multiple large-scale climate features, such as the Gulf Stream path variability, with some lead time up to a few years, which indicates predictability.
Here, we propose to develop a seasonal-to-interannual statistical prediction system for ocean temperatures on the NES, which will be tailored to the needs of the National Marine Fisheries Service (NMFS) Northeast Fisheries Science Center (NEFSC) for fisheries stock assessment. The measures of uncertainty and predictability skill of the prediction product will be rigorously evaluated against three independent long-term regional hindcast simulations based on probabilistic skill metrics. Our first goal is to use previously described statistical relationships linking shelf ocean temperature to atmospheric circulation, Gulf Stream path, and coastal sea-level to develop a prediction system at the 3–36 month time scale. Our second goal is to evaluate this system in the context of selected stock assessments performed by NOAA Fisheries. Our third goal is to clarify the dynamical basis for the statistical relationships using ocean hindcast models and coupled ocean-atmosphere models.
This proposal is targeting the FY 2017 NOAA Modeling, Analysis, Predictions, and Projections (MAPP) Program solicitation Competition 2: Research to explore seasonal prediction of coastal high water levels and changing living marine resources and its third sub-element: Develop and evaluate experimental probabilistic-based prediction products tailored to the needs of the NOS and/or NMFS, as appropriate, by proposing to develop and test a new statistical seasonal-to-interannual prediction system of NES temperature specifically tailored to the needs of NMFS stock assessment, by the team of PIs including the scientists from NMFS. Our proposed work addresses every element of the NOAA’s long-term climate goal of advancing scientific understanding, monitoring, and prediction of climate and its impacts, to enable effective decisions.
Climate Risk Area: Marine Ecosystems
Principal Investigator (s): Young-Oh Kwon (Woods Hole Oceanographic Institution)
Co-PI (s):Ke Chen (Woods Hole Oceanographic Institution), Glen Gawarkiewicz (Woods Hole Oceanographic Institution), Terry Joyce (Woods Hole Oceanographic Institution), Janet Nye (Stony Brook University), Jon Hare (NOAA/NMFS), Paula Fratantoni (NOAA/NMFS), Vincent Saba (NOAA/NMFS), Tim Miller (NOAA/NMFS)
Task Force: Marine Prediction
Year Initially Funded:2017
Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources
Final Report: NA17OAR4310111_Final Report.pdf