| ## Dynamic and regression modeling of ocean variability in the tide-gauge record at seasonal and longer periods## Full citation:Hill, E. M., R. M. Ponte, and J. L. Davis (2007), Dynamic and regression modeling of ocean variability in the tide-gauge record at seasonal and longer periods, J. Geophys. Res., 112, C05007, doi:10.1029/2006JC003745. ## AbstractComparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and an ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's s strengths and weaknesses will allow for future improvements of the model. ## FiguresHigh resolution images may be obtained by clicking the link above.
## Acknowledgements
K. Ueyoshi and D. Stammer provided the 50-year long OM output used in this paper. This work is a contribution to the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO), funded by the National Oceanographic Partnership Program. Suggestions from two anonymous reviewers significantly improved this manuscript. We would also like to thank M. Tamisiea and P. Woodworth for useful discussions. This work was supported by the NASA Earth Science Enterprise's Earth Observing System Interdisciplinary Science Program (grant NNG04GL69G), the NASA Physical Oceanography Program (grant NAG5-12742), and the NSF Geophysics Program (grant EAR-0125518). Some figures were produced using the Generic Mapping Tools version 4 [ |

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