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Interpreting low frequency sea level signals over the last decade

Interpreting low frequency sea level signals over the last decade Rui M. Ponte 1 , Sergey V. Vinogradov 1 and Carl Wunsch 2 1 Atmospheric and Environmental Research, Inc., Lexington, USA 2 Massachusetts Institute of Technology, Cambridge, USA. ECCO-GODAE state estimation.

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Interpreting low frequency sea level signals over the last decade

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  1. Interpreting low frequency sea level signals over the last decade Rui M. Ponte1, Sergey V. Vinogradov1 and Carl Wunsch2 1Atmospheric and Environmental Research, Inc., Lexington, USA 2Massachusetts Institute of Technology, Cambridge, USA

  2. ECCO-GODAE state estimation MIT-AER solution (pickup from SIO solution/Stammer et al.; in progress/details in talk by P. Heimbach) • A least-squares fit of in situ and satellite data to the MITgcm using the method of Lagrange multipliers (i.e., adjoint method, 4DVAR,…) • MITgcm configuration (1°x1°, 23 vertical levels, 80S-80N) • Forcing by NCEP-NCAR 6-hour reanalysis fields • Currently 1992-2004 period • Over 400 million data constraints (TP, Jason, ERS, Envisat, GRACE, ARGO, WOCE and pre-WOCE CTD, XBT, scatterometer, SST, SSS, drifters) • Control parameters (initial T and S, forcing surface fluxes) ECCO-GODAE efforts at MIT-AER are supported under the U.S. National Ocean Partnership Program (NOPP) by NASA, NOAA and NSF

  3. Altimeter time-variable error (cm) Uncertainty dominated by eddy representation error, given the coarse resolution (1x1 degree) of the ECCO estimate (Ponte et al. poster in OSTST meeting later this week)

  4. Sea level variability (T>2 months) Altimeter Error ECCO solution (no constraining to altimetry at depths < 1000 m)

  5. Correlation/explained variance Correlation of altimeter data and solution Data variance explained = 1-var(model-data)/var(data)

  6. Correlation with tide gauges

  7. Variability in sea level, steric height sea level thermosteric steric halosteric

  8. Depth contributions steric height > 250 m steric height> 1200 m

  9. Bottom pressure variability (cm) (based on monthly mean, detrended time series)

  10. Bottom pressure dynamics ∂pb/∂t + r pb +g J(pb,H/f) ≈ g*curl (stress/f)+ baroclinic effects Amplitude of grad(H/f) (s)

  11. Dynamical balances ∂pb/∂t friction (~3 day damping scale) J(pb,H/f) wind curl southwest Australia southeast Pacific (using 50-yr runs from D. Stammer)

  12. Annual cycle in sea level Amplitude (range: 0-10 cm) Phase (degrees) ECCO ECCO Altimeter Altimeter

  13. Interannual variability ECCO Altimeter Steric height range (0-10 cm)

  14. Interannual variability (15S) Altimeter

  15. Altimeter and solution trends Altimeter (courtesy of S. Nerem) Mean spatial trend removed Error Formal uncertainty ECCO

  16. Global mean steric height Thermosteric Halosteric

  17. Summary • Least squares fit of model to most available data leads to a smoothed, interpolated version of all data consistent with model dynamics, physics and forcing • A tool to explore large-scale variability (annual cycle, interannual variability, long term trends) • More to sea level variability than just steric height at seasonal and longer periods • Sea level trends (warming solution consistent with altimeter; thermosteric effects sufficient to explain observed trend; sampling issues)

  18. Near future plans • Constraining the E-P-R flux fields • Moving to a non-Boussinesq formulation of MITgcm • Error modeling and timescale separation

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