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Christopher Garrett and Fouad Majaess

. The multiple regression coefficients for the dependence of sea level on wind are shown by the resultsdigplayed in Fig. 7 to be very poorly determined. Indeedthey are not significantly different from zero. The comparative unimportance of wind, for this particular location, is supported by an analysis of the amount ofsea level variance that can be attributed to the variousinputs. If we multiply (3.1) by its complex conjugate,on both sides, we obtain~'~'* = aa*PP* + bb*EE* + cc*NN* + (bc*EN* + b

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Lucia Bunge and Allan J. Clarke

that it must exist since the wind stress curl that drives it plays a major role in Atlantic equatorial ocean dynamics (e.g., Arhan et al. 2006 ). This article documents the existence of this zonally symmetric mode and shows that it is in fact the dominant mode of Atlantic equatorial thermodynamics and SSH variability. We will also show that the sum of this mode and the zonally asymmetric tilt mode explains the observed eastward SSH and thermocline depth propagation. In all the analysis to follow

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Michael J. McPhaden and Bruce A. Taft

25 m, 80 m and160 m. Smooth curves are for daily data smoothed with a 51-dayHanning filter.tions is evident in Fig. 2. A portion of that variabilityis associated with t 000 km long instability waves withperiods of 20-30 days. Averaging over the period ofthese waves will simultaneously reduce the energy inthe associated wavenumber band. Results derived from linear least squares analysis ofthe smoothed data are quoted throughout the text. Dataare fit to a six-parameter regression model similar

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S. J. Lentz and C. D. Winant

gradient and bottomstress. The correlation between acceleration and windstress is significant, although weak at the mid- andouter-shelf moorings. Regression coefficients are generally O(1) in absolute value. A two-input regression analysis with the wind stressand the ASL gradient as inputs and the bottom stressas the output was also performed. The results of thisanalysis are displayed in Table 5. Confidence levels for TABLE 4. Correlations (C) at zero lag between terms in Eq. (2) for each mooring

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Irene Polo, Jon Robson, Rowan Sutton, and Magdalena Alonso Balmaseda

of the regression analysis do not change substantially. Empirical orthogonal function (EOF) analysis ( Bretherton et al. 1992 ) has also been performed in order to identify the dominant modes of density variability. Maps of different variables regressed onto either the AMOC index or onto principal components (PC) time series from the EOF modes have been statistically tested at the 90% confidence level based on a Student’s t test, with effective degrees of freedom reduced following Metz (1991

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Steven J. Lentz

to uncertainty in major axis). Fig . 4. (a) Major axis amplitude and (b) phase of along-shelf current from harmonic analysis as a function of water depth. Phases are time (month) of maximum equatorward flow. Error bars indicate 95% confidence interval for estimates. Minor axis amplitudes (not shown) are not significantly different from zero for any of the time series ( Table 1 ). Fig . 5. (a) Slope of linear regression as a function of along-shelf distance from northeastern Georges Bank (0 km) to

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John Trowbridge and Steve Elgar

of υ ′ w ′ were then used to compute κ 2 N 2 z 2 /| υ ′ w ′ | for each 1-h record. To exclude observations in which υ ′ w ′ was too small to be estimated accurately, 54 1-h records for which | κ 2 N 2 z 2 / υ ′ w ′ | > 2 were omitted from the analysis. The remaining 800 records were sorted into four equally sized groups in order of increasing κ 2 N 2 z 2 /| υ ′ w ′ |. The 200 × 6 measurements of D υw within each group were then regressed against (5) to produce 200 final estimates of υ

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Sutara H. Suanda and John A. Barth

1 is shown for clarity. (bottom) For midshelf profile, because of the lack of measurements below 70-m water depth, the density profile is assumed constant. 3. Tidal band variability The temporal variation of the dominant semidiurnal tidal signal M 2 is extracted from the high-pass filtered velocity and displacement data. Decomposition into vertical modes using background density profiles ( Fig. 4 ) is combined with harmonic analysis in sliding 4-day windows to derive M 2 internal tide

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William D. Grant, Albert J. Williams III, and Scott M. Glenn

.e., it depends whether the point is on abiologically induced mound or an indentation or slightundulation in the bottom). Pitch and roll measurements indicated no tripod settlement occurred duringthe analysis period. Estimates of the displacementthickness, using the results of Jackson (1981), are onthe order of I cm and are within the uncertainty ofthe bottom location. The final selection of sensor heightabove the bottom was picked by using the zero shift,which on the average, maximized the regression

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Cécile Cabanes, Tong Lee, and Lee-Lueng Fu

drives an “Ekman” cell? Can wind stress curl cause density gradient at depth to drive a MOC change?). We address these science questions by analyzing an ocean analysis product and forcing sensitivity experiments. The paper is organized as follows: In section 2 , we describe the ocean analysis product and the model sensitivity experiments used to decipher the effects of different forcings. In section 3 , we present the results of the analysis of dominant forcing and perform a dynamical decomposition

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