| Summary: | Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 126(8), (2021): e2021JC017614, https://doi.org/10.1029/2021JC017614. Fluctuations in the path of the Gulf Stream (GS) have been previously studied by primarily connecting to either the wind-driven subtropical gyre circulation or buoyancy forcing via the subpolar gyre. Here we present a statistical model for 1 year predictions of the GS path (represented by the GS northern wall—GSNW) between 75°W and 65°W incorporating both mechanisms in a combined framework. An existing model with multiple parameters including the previous year's GSNW index, center location, and amplitude of the Icelandic Low and the Southern Oscillation Index was augmented with basin-wide Ekman drift over the Azores High. The addition of the wind is supported by a validation of the simpler two-layer Parsons-Veronis model of GS separation over the last 40 years. A multivariate analysis was carried out to compare 1-year-in-advance forecast correlations from four different models. The optimal predictors of the best performing model include: (a) the GSNW index from the previous year, (b) gyre-scale integrated Ekman Drift over the past 2 years, and (c) longitude of the Icelandic Low center lagged by 3 years. The forecast correlation over the 27 years (1994–2020) is 0.65, an improvement from the previous multi-parameter model's forecast correlation of 0.52. The improvement is attributed to the addition of the wind-drift component. The sensitivity of forecasting the GS path after extreme atmospheric years is quantified. Results indicate the possibility of better understanding and enhanced predictability of the dominant wind-driven variability of the Atlantic Meridional Overturning Circulation and of fisheries management models that use the GS path as a metric. The authors are grateful for financial ...
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