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Impact of Geoid Improvement on Ocean Mass and Heat Transport Estimates

  • P. Le Grand
Part of the Space Sciences Series of ISSI book series (SSSI, volume 17)

Abstract

One long-standing difficulty in estimating the large-scale ocean circulation is the inability lo observe absolute current velocities. Both conventional hydrographic measurements and altimetric measurements provide observations of currents relative to an unknown velocity at a reference depth in the case of hydrographic data, and relative to mean currents calculated over some averaging period in the case of altimetric data. Space gravity missions together with altimetric observations have the potential to overcome this difficulty by providing absolute estimates of the velocity of surface oceanic currents. The absolute surface velocity estimates will in turn provide the reference level velocities that are necessary to compute absolute velocities at any depth level from hydrographic data.

Several studies have been carried out to quantify the improvements expected from ongoing and future space gravity missions. The resuhs of these studies in terms of volume flux estimates (transport of water masses) and heat flux estimates (transport of heat by the ocean) are reviewed in this paper. The studies are based on ocean inverse modeling techniques that derive impact estimates solely from the geoid error budgets of forthcoming space gravity missions. Despite some differences in ihe assumptions made, the inverse modeling calculations all point lo significant improvements in estimates of oceanic fluxes. These improvements, measured in terms of reductions of uncertainties, arc expected to be as large as a factor of 2.

New developments in autonomous ocean observing systems will complement the developments expected from space gravity missions. The synergies of in situ and satellite observing systems are considered in the conclusion of this paper.

Keywords

Ocean Circulation Volume Transport Altimetric Data Geoid Height Hydrographic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • P. Le Grand
    • 1
  1. 1.IFREMER, BP 70PlouzaneFrance

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