Seasonal Prediction of Bottom Temperature on the Northeast U.S. Continental Shelf. Issue 5 (12th May 2021)

• Record Type: Journal Article
• Authors: Chen, Zhuomin; Kwon, Young‐Oh; Chen, Ke; Fratantoni, Paula; Gawarkiewicz, Glen; Joyce, Terrence M.; Miller, Timothy J.; Nye, Janet A.; Saba, Vincent S.; Stock, Brian C.
• Is Part Of: Journal of geophysical research. Volume 126:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-12
• Subjects: bottom temperature -- Northeast U.S. shelf -- seasonal prediction; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2021JC017187
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model‐based seasonal‐to‐interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy‐resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∼5 months in the Mid‐Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large‐scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES. Plain Language Summary: Reliable prediction of bottom temperature could improve fisheries stock assessments on the Northeast U.S. shelf (NES), where some of the most commercially valuable fisheries are located. In this study, we have developed a collection of statistical models that produce seasonal predictions of NES bottom temperature with 1–12 months lead time. Variables considered in these prediction models include local persistence of bottom temperature from prior months, bottom temperature from an upstream or nearby region, and large‐scale atmospheric and oceanic indices representing the North Atlantic Oscillation or position of the Gulf Stream (GS). Only considering local persistence provides significant skill for lead times up to ∼5 months in the Mid‐Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the skill varies by season. Using upstream or nearby bottom temperature and the GS index both generally improve the prediction skill. However, the GS index does not provide higher prediction skill than those upstream or nearby bottom temperatures. A simplified statistical model has been developed, which can be tailored to fisheries management on the NES. Key Points: A local persistence model for bottom temperature provides prediction skill up to ∼10 months with strong variation by region and season Including upstream or nearby bottom temperature as a predictor largely improves prediction skill, mainly attributed to ocean advection An index of Gulf Stream position produces skillful predictions in fall