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Acoustic Remote Sensing of Large-Scale Temperature Variability in the North Pacific Ocean

Acoustic Remote Sensing of Large-Scale Temperature Variability in the North Pacific Ocean. Peter F. Worcester, Bruce D. Cornuelle, Matthew A. Dzieciuch, Walter H. Munk Scripps Institution of Oceanography, University of California, San Diego Brian D. Dushaw, Bruce M. Howe,

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Acoustic Remote Sensing of Large-Scale Temperature Variability in the North Pacific Ocean

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  1. Acoustic Remote Sensing of Large-Scale Temperature Variability in the North Pacific Ocean Peter F. Worcester, Bruce D. Cornuelle, Matthew A. Dzieciuch, Walter H. Munk Scripps Institution of Oceanography, University of California, San Diego Brian D. Dushaw, Bruce M. Howe, James A. Mercer, Robert C. Spindel Applied Physics Laboratory, University of Washington Dimitris Menemenlis Jet Propulsion Laboratory, NASA Detlef Stammer Institut für Meereskunde, Universität Hamburg AGU Ocean Sciences Meeting Portland, Oregon, 26-30 January 2004

  2. Acoustic Thermometry of Ocean Climate: Goals • Determine the precision with which acoustic methods can measure large-scale changes in ocean temperature • Determine what effects, if any, the acoustic transmissions have on marine mammals and other marine life

  3. North Pacific Acoustic Laboratory

  4. North Pacific Acoustic Laboratory: Goals • To perform the second phase of research on the feasibility and value of large-scale acoustic thermometry • To study the behavior of sound transmissions in the ocean over long distances • To conduct studies on the possible long-term effects from the sound transmissions on marine life

  5. ECCO OGCM: SIO

  6. Summary • Long-term trends in large-scale ocean temperature are easily visible in the acoustic time series • Travel times can now be readily computed from OGCMs for comparison with acoustic data • Travel times have good signal-to-noise ratios for differences between models • Assimilation of travel times into OGCMs is needed to objectively assess the value of acoustic methods • OGCM parallelization makes non-local observations more complicated to assimilate using adjoint methods

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