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Using Profiling Float Trajectories to Estimate Ocean Circulation

Using Profiling Float Trajectories to Estimate Ocean Circulation. A Presentation to the ARGO Workshop November 2003. Breck Owens Woods Hole Oceanographic Institution. Collaborators. Mapping the circulation from profiling float trajectories Kara Lavender (SIO  WHOI  SEA) Russ Davis (SIO)

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Using Profiling Float Trajectories to Estimate Ocean Circulation

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  1. Using Profiling Float Trajectories to Estimate Ocean Circulation A Presentation to the ARGO Workshop November 2003 Breck Owens Woods Hole Oceanographic Institution

  2. Collaborators • Mapping the circulation from profiling float trajectoriesKara Lavender (SIO  WHOI  SEA)Russ Davis (SIO) • Mapping using variety of data typesBruce Cornuelle (SIO)Steve Jayne (WHOI)Jim McWilliams (UCLA)

  3. Outline of Presentation • Procedure • Results from profiling floats for- the northern North Atlantic- the Indian and South Pacific • Combining with other data- combining with other floats for the North Atlantic- Using other data to get a 3 dimensional description- first results for the North Atlantic- comparisons with altimetry

  4. Procedures • Ascent and descent positions used to compute average velocity at depth for that cycle. Time to surface << time at depth  Errors for at depth velocities are small O(1 cm/s). • Space and time averages computed over bins (nominal size 1º degree or larger) • Impose geostrophy and construct smoothed version of circulation using Optimal Interpolation (OI) • Future work can include other data or weak constraints as input into the OI procedure

  5. Schematic of float cycle - Surface Positions used to obtain best estimate of ascent and descent positions - As long as travel time to surface << drift time, positions can be converted into average velocity at depth

  6. To estimate uncertainties in bin averages and to provide a priori statistics from observations (or analysis of modeling studies) requires - Time scale from temporal covariances - Space scales from spatial covariances (including noise) Spatial Covariances Temporal Covariances

  7. Bin Averaging and Estimating Float Velocities Bin size ~ 110 km square Obtain mean and variance for velocities averaged over all floats that pass through bin Using integral time scale (10 days), convert statistics to means + error estimates

  8. Optimal Estimation • Objective Mapping uses a priori statistics (covariances) for spatial interpolation and takes into account estimated errors when drawing maps • Imposes dynamical balance (hard constraint) ie Geostrophy • Directly provides estimates of linearly related fields, for example, dynamic pressure from velocity estimates

  9. Results from the Northern North Atlantic • Based on profiling float data collected from 1996 - 2002 deployed in support of the Atlantic Climate and Circulation Experiment (ACCE) and Labrador Sea Deep Convection Experiment • Results described by Lavender, Owens and Davis (2003, DeepSea Research, accepted)

  10. Path of Floats from pre-Argo Array • 208 P-ALACE and SOLO floats • 21,886 velocity measurements (arrows) •  578 years of drift velocity data

  11. Number of Observations per bin

  12. Bin Averaged Velocities Bins nominally 1º square Error ellipses based on degrees of freedom (10 day time scale) All Bins Bins with significant means

  13. Objective Mapped Velocity, 700 m • Data from bin averages • Imposed Geostrophy • Guassian form for dynamic presssure covariance, scale = 150 km • Recirculation near boundaries • Interconnection between Labrador and Irminger Seas • Strong influence of topographic steering

  14. Dynamic Pressure Lavender, Owens, & Davis, 2003

  15. Indian and South Pacific Oceans • Russ Davis has used WOCE and Argo float data for these two Oceans to estimate the circulation at 1000 m depth • Projections onto basis functions, assuming an a priori variances (spectra) for these functions, ie form of OI. • Bin size larger than Atlantic, O(300 km)

  16. Indian Ocean Velocities at 1000 m From WOCE and Argo floats 1174 float years of data Davis, 2003

  17. Indian Ocean Absolute Dynamic Pressure at 1000 m From WOCE and Argo floats 1174 float years of data Davis, 2003

  18. South Pacific Ocean Velocities at 1000 m From WOCE and Argo floats 1332 float years of data Davis, 2003

  19. South Pacific Ocean Absolute Dynamic Pressure at 1000 m From WOCE and Argo floats 1332 float years of data Davis, 2003

  20. Expanded Analysis of North Atlantic • Used all float observations from North Atlantic 1972-2002- SOFAR floats- RAFOS floats- Profiling floats • First maps from project to eventually combine subsurface and surface velocities, dynamic height, altimetry

  21. North Atlantic - using all float data Profiling and acoustically tracked floats 1972-2001 1540 float years of data from 1240 floats Used same spatial scale, 150 km for  covariance, as earlier North Atlantic maps

  22. Combining 700 m Floats and Shear from Hydrographic Climatology Dynamic Height from Hydrobase Sea surface relative to 700 m 700 m Floats + hydrography Sea surface pressure

  23. Comparing Float + Hydrography with Altimeter Estimates Topex/Posidion mean relative to Geoid from Grace Mission Floats + hydrography Sea surface pressure

  24. Another Estimate of Surface Circulation • Peter Niiler, Nikolai Maximenko and Jim McWilliams • Circulations estimated from 1992-2002 surface drifter data (15 m drogue) and NCEP reanalysis for Ekman velocity estimate

  25. Estimate of Surface Pressure from Surface Drifters Niiler, Maximenko and McWilliams, 2003

  26. Conclusions • Profiling float trajectories provide an excellent means of estimating ocean circulation • Broadly consistent with other measures of circulation. Examinations of differences should elucidate both physical processes and sampling issues. • Future work will combine velocities, Argo profiles, altimetry, surface drifters, and winds to give mean and low-frequency 3-D estimates of the ocean circulation

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