Using Drifter Velocity Measurements to Assess and Constrain Coarse-Resolution Ocean Models

Details

• Journal Title:
  Journal of Atmospheric and Oceanic Technology
• Personal Author:
  Zhao, Mengnan ; Ponte, Rui M. ; Wang, Ou ; Lumpkin, Rick
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory)
• Description:
  Properly fitting ocean models to observations is crucial for improving model performance and understanding ocean dynamics. Near-surface velocity measurements from the Global Drifter Program (GDP) contain valuable information about upper-ocean circulation and air–sea fluxes on various space and time scales. This study explores whether GDP measurements can be used for usefully constraining the surface circulation from coarse-resolution ocean models, using global solutions produced by the consortium for Estimating the Circulation and Climate of the Ocean (ECCO) as an example. To address this problem, a careful examination of velocity data errors is required. Comparisons between an ECCO model simulation, performed without any data constraints, and GDP and Ocean Surface Current Analyses Real-Time (OSCAR) velocity data, over the period 1992–2017, reveal considerable differences in magnitude and pattern. These comparisons are used to estimate GDP data errors in the context of the time-mean and time-variable surface circulations. Both instrumental errors and errors associated with limitations in model physics and resolution (representation errors) are considered. Given the estimated model–data differences, errors, and signal-to-noise ratios, our results indicate that constraining ocean-state estimates to GDP can have a substantial impact on the ECCO large-scale time-mean surface circulation over extensive areas. Impact of GDP data constraints on the ECCO time-variable circulation would be weaker and mainly limited to low latitudes. Representation errors contribute substantially to degrading the data impacts.
• Keywords:
  Ocean circulation Climate Models Data Assimilation Error Analysis
• Source:
  Journal of Atmospheric and Oceanic Technology, 38(4)
• DOI:
  https://doi.org/10.1175/JTECH-D-20-0159.1
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:3b59fa7e776d5b4ebda21e761a7843192688e78818f0d8f5c170f176964c0972
• Download URL:
  https://repository.library.noaa.gov/view/noaa/29336/noaa_29336_DS1.pdf
• File Type:
  [PDF - 3.05 MB ]