A comparison of model and GRACE estimates of the large‐scale seasonal cycle in ocean bottom pressure

Abstract

[1] Seasonal variability in ocean bottom pressure p b is analyzed using GRACE (Gravity Recovery and Climate Experiment) data products and an optimized model solution obtained by fitting most available ocean data in a leastsquares sense. The annual cycle in the spatial mean is a substantial part of the observed seasonal p b variability; net freshwater input and atmospheric pressure effects are both important. For the residual spatially-varying patterns, GRACE and model results agree well over the Southern Ocean where strongest variability at both annual and semiannual periods is present. Phase patterns tend to match well, although model amplitudes are generally weaker. Considerable uncertainty remains in both GRACE and model p b fields, judging from the spread among available estimates. Improving the p b estimates requires removal of data noise from aliasing and leakage of land hydrology signals, and further optimization of the ocean model, including possible use of GRACE data to constrain the solution. Citation: Ponte, R. M., K. J. Quinn, C. Wunsch, and P. Heimbach (2007), A comparison of model and GRACE estimates of the large-scale seasonal cycle in ocean bottom pressure, Geophys. Res. Lett., 34, L09603,

The Global General Circulation of the Ocean Estimated by the ECCO-Consortium

“…Estimates of oceanic mass and velocity fields produced by ECCO have been used to interpret geodetic measurements of Earth's orientation in space and its variable gravity field, and to highlight the major role of ocean angular momentum variability in explaining observed polar motion (e.g., Gross et al, 2005;Ponte et al, 2001Ponte et al, , 2007a. Comparisons with the geodetic data provide entirely independent tests of the ECCO results.…”

Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean

“…An analysis by Ponte et al (2007) concluded that the 750 km version had lowest residuals with another version of the ECCO model. If we compare the RL05 data smoothed with different Gaussian smoothers to the unsmoothed JPL ECCO OBP, we find that 500 km smoothed maps have significantly lower standard deviations (Fig.…”

“…In order to make the models consistent with the GRACE observed OBP, we add the GRACE observation of monthly mean ocean mass to the JPL ECCO and OMCT grids before computing statistics. Moreover, JPL ECCO does not contain the time-variable global mean fluctuation in OBP due to changes in the mean atmospheric pressure over the ocean (e.g., Ponte et al, 2007), which is a 0.6 cm seasonal variation. This is included in the GAD coefficients (and so is also included in the GRACE observations).…”

“…The grids are based on Release-04 (RL04) coefficients (Bettadpur, 2007) and are post-processed to reduce correlated errors which appear as north-south stripes in the data (Swenson and Wahr, 2006), using either an ad hoc destriping algorithm and additional Gaussian smoothing that was optimized for the ocean (Chambers, 2006), or by projecting GRACE data onto empirical orthogonal functions (EOFs) from an ocean model (Chambers and Willis, 2010). Uncertainty of the mapped data has been estimated to be between 2 and 3 cm root-meansquare (RMS) depending on the type of processing, based on comparison to steric-corrected altimetry (Chambers, 2006;Chambers and Willis, 2010), output from an ocean model (Ponte et al, 2007;Quinn and Ponte, 2010), or bottom pressure recorders Park et al, 2008). Even with this level of uncertainty, however, the mapped OBP data from GRACE have proven useful in a number of studies as disparate as, for example, studying low-frequency changes in the Arctic or the North Pacific (Chambers and Willis, 2008;Song and Zlotnicki, 2008;, variability of the Antarctic Circumpolar Current transport (Zlotnicki et al, 2007;Boening et al, 2010;Bergmann and Dobslaw, 2012), exchange of mass between basins (Ponte and Quinn, 2009;Chambers and Willis, 2009), or record anomalous pressure changes in the Southern Ocean (Boening et al, 2011).…”

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

“…The trend is caused by the Boussinesq approximation that is used in the model, i.e. oceanic volume, and not mass, is held constant in the OGCM (Ponte et al 2007). The reader should be aware that real long-term variations in observed OBP are expected due to (1) trends in freshwater fluxes; (2) trends in the atmospheric forcing; and (3) real long-term variations in the large-scale circulation, i.e.…”