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John M. Wahr and Abraham H. Oort


Seasonal, zonal surface torques between the atmosphere and the earth are estimated and compared, using data from a number of independent sources. The mountain torque is computed both from surface pressure data and from isobaric height data. The friction torque is estimated from the oceanic stress data of Hellerman and Rosenstein. Results for the total torque are inferred from atmospheric angular momentum data. Finally, the globally integrated total torque is compared with astronomical observations of the earth's rotation rate. These comparisons help us to assess the quality of the different results.

Zonal torques are also computed using results from a GFDL general circulation model of the atmosphere. A comparison with the corresponding results inferred from real data is presented and interpreted in terms of model accuracy.

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Eric W. Leuliette and John M. Wahr


Though thermal effects dominate steric changes in sea level, the long-period contribution of thermal expansion to sea level is uncertain. Nerem et al. found that a global map of sea surface temperature (SST) trends and a corresponding map of TOPEX/Poseidon-derived sea surface height (SSH) trends were strongly correlated. This result is explored with a coupled pattern analysis (CPA) between five years of global SST and SSH, which allows for matching of modes of common temporal variability.

The dominant mode found is an annual cycle that accounts for nearly all (95.3%) of the covariance between the fields and has a strong SST/SSH spatial correlation (0.68). The spatial correlation is strong in both the Atlantic (0.80) and the Pacific (0.70). Good temporal and spatial agreement between the SSH and SST fields for the primary seasonal mode suggests that a robust regression between fields may have some physical significance with respect to thermal expansion and that the regression coefficient might be a proxy for the mixing depth of the mode. The value of the regression coefficient, H, scaled by a thermal expansion coefficient of 2 × 10−4 °C−1 is 40 m for this mode, and ranges from 33 to 47 m among the basins.

The primary mode of a nonseasonal CPA is an interannual mode that captures 38.0% of the covariance and has significant spatial correlations (0.54) between SSH and SST spatial patterns. The spatial pattern and temporal coefficients of this mode are correlated with ENSO events. A robust regression between fields finds that the nonseasonal modes have a regression coefficient 2–4 times that of the seasonal modes, indicative of deeper thermal mixing. The secondary nonseasonal mode captures most of the secular trend in both fields during the period examined. The temporal coefficients of this mode lag those of primary mode. Evidence is presented that this mode is consistent with the behavior expected from secular trends that are dominantly forced by thermal expansion.

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