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Abstract The Gulf Stream, a major ocean current in the North Atlantic ocean is a key component in the global redistribution of heat and is important for marine ecosystems. Based on 27 years (1993–2019) of wind reanalysis and satellite altimetry measurements, we present observational evidence that the path of this freely meandering jet after its separation from the continental slope at Cape Hatteras, aligns with the region of maximum cyclonic vorticity of the wind stress field known as the positive vorticity pool. This synchronicity between the wind stress curl maximum region and the Gulf Stream path is observed at multiple time-scales ranging from months to decades, spanning a distance of 1500 km between 70 and 55W. The wind stress curl in the positive vorticity pool is estimated to drive persistent upward vertical velocities ranging from 5 to 17 cm day⁻¹over its ~ 400,000 km²area; this upwelling may supply a steady source of deep nutrients to the Slope Sea region, and can explain as much as a quarter of estimated primary productivity there. 

Abstract Downstream of Cape Hatteras, the vigorously meandering Gulf Stream forms anticyclonic warm core rings (WCRs) that carry warm Gulf Stream and Sargasso Sea waters into the cooler, fresher Slope Sea, and forms cyclonic cold core rings (CCRs) that carry Slope Sea waters into the Sargasso Sea. The Northwest Atlantic shelf and open ocean off the U.S. East Coast have experienced dramatic changes in ocean circulation and water properties in recent years, with significant consequences for marine ecosystems and coastal communities. Some of these changes may be related to a reported regime shift in the number of WCRs formed annually, with a doubling of WCRs shed after 2000. Since the regime shift was detected using a regional eddy‐tracking product, primarily based on sea surface temperatures and relies on analyst skill, we examine three global eddy‐tracking products as an automated and potentially more objective way to detect changes in Gulf Stream rings. Currently, global products rely on altimeter‐measured sea surface height (SSH), with WCRs registering as sea surface highs and CCRs as lows. To identify eddies, these products use either SSH contours or a Lagrangian approach, with particles seeded in satellite‐based surface geostrophic velocity fields. This study confirms the three global products are not well suited for statistical analysis of Gulf Stream rings and suggests that automated WCR identification and tracking comes at the price of accurate identification and tracking. Furthermore, a shift to a higher energy state is detected in the Northwest Atlantic, which coincides with the regime shift in WCRs. 

Abstract Benthic storms are episodes of intensified near‐bottom currents capable of sediment resuspension in the deep ocean. They typically occur under regions of high surface eddy kinetic energy (EKE), such as the Gulf Stream. Although they have long been observed, the mechanism(s) responsible for their formation and their relationships with salient features of the deep ocean, such as bottom mixed layers (BMLs) and benthic nepheloid layers (BNLs), remain poorly understood. Here we conduct idealized experiments with a primitive‐equation model to explore the impacts of the unforced instability of a surface‐intensified jet on near‐bottom flows of a deep zonal channel. Vertical resolution is increased near the bottom to represent the bottom boundary layer. We find that the unstable near‐surface jet develops meanders and evolves into alternating, deep‐reaching cyclones and anticyclones. Simultaneously, EKE increases near the bottom due to the convergence of vertical eddy pressure fluxes, leading to near‐bottom currents comparable to those observed during benthic storms. These currents in turn form BMLs with thickness of O(100 m) from enhanced velocity shears and turbulence production near the bottom. The deep cyclonic eddies transport fluid particles both laterally and vertically, from near the bottom through the entire BML and may contribute to the formation of the lower part of BNLs. A sloping bottom reduces both the intensity of the near‐bottom currents and the extent of vertical transport. Overall, our study highlights a significant response of the abyssal environment to near‐surface current instability, with potential implications for sediment transport in the deep ocean. 

Abstract The strong sea‐surface temperature (SST) gradient associated with the Gulf Stream (GS) is widely acknowledged to play an important role in shaping mid‐latitude weather and climate. Despite this, an index for the GS SST gradient has not yet been standardized in the literature. This paper introduces a monthly index for the large‐scale SST gradient across the separated GS based on the time‐varying GS position detected from sea‐surface height. Analysis suggests that the variations in the monthly average SST gradient throughout the year result primarily from SST variability to the north of the GS, with little contribution from SST to the south. The index exhibits a weak periodicity at ∼2 years. Sea level pressure and turbulent heat flux patterns suggest that variability in the large‐scale SST gradient is related to atmospheric (rather than oceanic) forcing. Ocean‐to‐atmosphere feedback does not persist throughout the year, but there is some evidence of wintertime feedback. 

{"Abstract":["This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.   <\/p>\n\nAfter the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy\u2019s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. <\/p>\n\nThe monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). <\/p>\n\nCorresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   <\/p>\n\nThe files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  <\/p>\n\nUnits of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. <\/p>\n\nFilenames: <\/p>\n\nmonthly_windstress_wsc_upwelling.nc<\/strong>: 480 time steps from 80W to 45W and 30N to 45N.<\/p>\n\nseasonal_windstress_wsc_upwelling.nc<\/strong>: 160 time steps from 80W to 45W and 30N to 45N.<\/p>\n\nannual_windstress_wsc_upwelling.nc<\/strong>: 40 time steps from 80W to 45W and 30N to 45N.<\/p>"],"Other":["Please contact igifford@earth.miami.edu for any queries.","{"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\\u202fJournal of the Meteorological Society of Japan. Ser. II,\\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\\u202fJournal of physical oceanography,\\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\\u202fJournal of Physical Oceanography,\\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\\u202fJournal of Climate,\\u202f2(12), pp.1507-1516."]}"]}