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ECCO Achievements, Capabilities, Applications, and Future Plan

ECCO Achievements, Capabilities, Applications, and Future Plan. Tony Lee, NASA JPL/Caltech. ECCO funded in the past decade under NOPP with funding from NASA, NOAA, NSF, and ONR Main focus: to provide physically consistent estimate of the state of the ocean to support climate research

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ECCO Achievements, Capabilities, Applications, and Future Plan

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  1. ECCO Achievements, Capabilities, Applications, and Future Plan Tony Lee, NASA JPL/Caltech ECCO funded in the past decade under NOPP with funding from NASA, NOAA, NSF, and ONR Main focus: to provide physically consistent estimate of the state of the ocean to support climate research Partners of various ECCO projects: JPL, MIT, AER, SIO, GFDL, NCEP, Harvard, NASA GSFC & ARC, U. of Hamburg, Argonne http://www.ecco-group.org Final IGST Meeting, NOAA/CPO, Washington DC, June 2-4, 2008

  2. ECCO Capabilities and Achievements • Inverse estimation of the time-varying state of the global ocean (along with the estimation of initial & boundary conditions & parameters). • Product serving: a suite of products (from multi-decadal to eddy-permitting) served through LAS, DODS, OPeNDAP. • The physical consistency of ECCO products (e.g., consistent estimate of state and forcing and state, budget closure) is crucial to climate diagnostics. • Budget components of T & S part of ECCO products. • Adjoint tool for sensitivity analysis (process study & Obs. Sys. evaluation). • Online tracer tools to study water-mass pathways (origin and destination) using forward & adjoint of passive-tracer equations based on ECCO-JPL product. • Development of an open-source automatic differentiation tool.

  3. ECCO-GODAE MIT adjoint-based estimation • Assimilate a large suite of existing in-situ & satellite data using the adjoint method by adjusting prior surface forcing & initial conditions. • MITOGCM, 1°x1°, 23 levels • Delayed mode (1+ year lag) • Current product period: 1992-2006 G-ECCO: similar system, also a large ste. of data constraints, product 1950-2000

  4. ECCO-GODAE JPL Kalman filter/smoother assimilation http://ecco.jpl.nasa.gov/ • Assimilate altimeter-derived SSH & in-situ T profiles using Kalman filter/smoother method. • MITOGCM & MOM4, 1°x0.3° in tropics, 1°x1° extra-tropics, 46 levels. • Near real time (10-30 days lag). • Product period: 1993 onward.

  5. ECCO-2 High-res. Global-Ocean & Sea-Ice Data Synthesis Velocity (m/s) At 15 m depth http://ecco2.org • MITOGCM (Cubed-sphere) • 18x18 km, 50 levels • Product: 1992-2006 • Green’s function method • Data constraint: • - SSH mean & anomaly • - T & S profiles (XBT, CTD, ARGO, TAO) • - SST (GHRSST) • - Sea ice concentration (SSMI) • - Sea ice motion (radiometers, QuikSCAT, RGPS) • - Sea ice thickness (ULS) Control parameters: Initial T & S; atmospheric surface boundary conditions; background vertical diffusivity; critical Richardson numbers for KPP; air-ocean, ice-ocean, air-ice drag coefficients; ice/ocean/snow albedo coefficients; bottom drag and vertical viscosity

  6. Product Validation Examples Described in various publications for different subject of investigations Comparison of mixed-layer temperature near cold tongue between ECCO-JPL product (curve) and TAO data (dots) (Kim et al. 2007) Comparison of mixed-layer temperature & velocity in tropical Indian Ocean between ECCO-JPL product & RAMA mooring data (Halkides & Lee 2008)

  7. Evaluation of newly released ECCO-2 high-resolution Product using TAO mooring data at a “tough location” ECCO-2 release 1 TAO ECCO-2 baseline

  8. Evaluation of newly released ECCO-2 high-resolution Product using TAO mooring data 0N, 165E 0N, 110W

  9. Applications of ECCO products & tools for research • A wide-range of topics including (not limited to): • Data assimilation & model improvement • Ocean circulation • Mixed-layer heat budget • Sea level variability and changes • Initialization of S-I prediction • Biogeochemistry • Geodesy • Providing open-boundary conditions for regional systems. • Over 150 peer-reviewed publications in the past decade (not including many external-user publications)

  10. Application example Mixed-layer temperature (MLT) balance in southeastern tropical Indian Ocean (SETIO) (Halkides & Lee 2008) The eastern box defined by Saji et al. (1999) spans areas with different forcing & ocean dynamics. Differences in processes controlling MLT within the box need to be understood. Variance of horizontal advective tendency suggest potential effects of equatorial currents in Box 1, coastal currents in Box 2, and South Equatorial Current in Box 3.

  11. Application example MLT budget in SETIO for 1994, 1997, & 2006 IODZM events: spatially inhomogenous & event dependent (Halkides & Lee 2008) Box 1: equatorial Horizontal advection warms in Box 1 but cools in Boxes 2 & 3 during IOZDM cooling Box 2: coastal Subsurface processes cools in Boxes 1 & 2 but warms in Box 3 during IOZDM cooling Box 3: SEC Horiz. Advection in Box 1 help terminate cooling in 94 & 06 but not 97 Made possible by budget closure

  12. Application example CO2 Importance of physical consistency to interdisciplinary applications (McKinley 2002) CO2 flux in tropical Pacific during ENSO inferred from a Kalman filter estimation (physically inconsistent) is unrealistically large (left), but that based on Kalman filter-smoother (physically consistent) is reasonable (right). Kalman Filtered estimate Kalman-filter/smoother estimate

  13. Application example Variability of N. Atl. Meridional Overturning Circulation (Wunsch and Heimbach 2007) Vertical distribution of volume transport (upper) & mid-depth transport time series (lower) • No significant slowdown of Atlantic MOC found. • Serious issue of potential aliasing for analysis based on infrequent hydrographic sections because of large month-to-month & synoptic fluctuations.

  14. Application example Understanding decadal sea-level patterns (Wunsch et al. 2007) T Vertical partition in density trends due to • trends in temperature T • trends in salinity S • trends in T, S TS S

  15. DJF hindcast for March initial conditions Application example Improvement of seasonal climate forecast by using ECCO-JPL product as initial state in a coupled model (UCLA atmos. Coupled to MITOGCM) ECCO baseline persistence Anomaly correlation increases with ECCO ECCO baseline Cazes-Boezio, Menemenlis, and Mechoso, 2008: J. Climate, 21, 1929-1947 . persistence Standard error reduces with ECCO

  16. Future Plan of ECCO • Sustain production and accelerate improvement in support of climate research (e.g., CLIVAR sciences). • Sustaining production of delayed-mode adjoint-based estimation system. • Near real-time extension using Kalman filter/smoother. • Enhancement of resolution. • Improvement of error covariance. • Expansion of control space (e.g., including mixing coeff.) • Longer term: coupling with atmosphere & biogeochemistry.

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