A data-assimilative modeling investigation of Gulf Stream variability

Details

• Journal Title:
  Deep Sea Research Part II: Topical Studies in Oceanography
• Personal Author:
  Mao, Shun ; He, Ruoying ; Bane, John ; Gawarkiewicz, Glen ; Todd, Robert E.
• NOAA Program & Office:
  NOS (National Ocean Service) ; CINAR (Cooperative Institute for the North Atlantic Region)
• Description:
  An advanced data-assimilative ocean circulation model is used to investigate Gulf Stream (GS) variability during 2017–2018. The modeling system applies a strong-constraint, 4D variational data assimilation algorithm. It assimilates satellite-based sea surface height and sea surface temperature measurements and in situ temperature and salinity profiles. Model skill assessment metrics along with comparisons of GS position and GS's three-dimensional mean kinetic energy with historical observations are applied to validate the data-assimilative model. The resulting time- and space-continuous ocean state estimates are used to diagnose eddy kinetic energy conversion and cross-stream eddy heat and salt fluxes over the two-year study period. The processes leading to kinetic energy conversion are primarily due to GS meanders. Significant inverse energy cascading (EKE→MKE and EKE→EPE) can occur during GS-eddy interactions, particularly during onshore intrusions or offshore meanderings of the GS. Throughout the two-year study period, the cross-stream eddy heat and salt fluxes off Cape Hatteras were predominantly positive (onshore). Both GS offshore meandering (occurring 44% of the time and associated with shelf/slope water export) and GS intrusion (occurring 56% of the time) contribute to onshore heat and salt transport. Improved understanding of these processes and dynamics requires strong integration of an advanced observational infrastructure that combines remote sensing; fixed, mobile, and shore-based observing components; and high-resolution data assimilative models.
• Keywords:
  Algorithms Gulf Stream Ocean Circulation Ocean Circulation Data-assimilative modeling
• Source:
  Deep–Sea Research II 211 (2023) 105319
• DOI:
  https://doi.org/10.1016/j.dsr2.2023.105319
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA16NOS0120028
• Rights Information:
  Accepted Manuscript
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:b724b67cfe96e66b1d48fb850768f1d67b91d7af5163299f8fb5f75f67a8cfc734fc4eacc41262330d766fb16b1da31434ca442f3b6d45db8782d5bde8de1ce0
• Download URL:
  https://repository.library.noaa.gov/view/noaa/72125/noaa_72125_DS1.pdf
• File Type:
  [PDF - 4.49 MB ]