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Motoki Nagura and Michael J. McPhaden

Fig. 1a ). The resulting phase speed obtained from the fitted curve was used to integrate the model in this study. Results obtained from the model run were compared with satellite altimetry in terms of pattern correlation and neutral regression coefficients computed using longitude–time sections of SSH anomalies at each latitude. In contrast to standard regression analysis, neutral regression coefficients are calculated by minimizing the sum of the square distances between the regression line and

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Julien Jouanno, José Ochoa, Enric Pallàs-Sanz, Julio Sheinbaum, Fernando Andrade-Canto, Julio Candela, and Jean-Marc Molines

) are more tied to low-frequency LCFEs (periods > 30 days) rather than short-period LCFEs. The link between low-frequency negative SSH anomalies near Campeche and Caribbean eddies is confirmed with a lagged regression analysis ( Fig. 11 , top) that indicates some correlation between negative SSH anomalies at the northeastern shelf of the CB (lag 0 in Fig. 11 ) and cyclonic eddies interacting 1.5 months before with the Yucatan coast (near 20°N, lag −45 days in Fig. 10 ). The regression analysis

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Anders Stigebrandt and Jarle Molvaer

brackish layer and approximately equal freshwater heights. From this observation one may conclude that, in general, there is nointernal hydraulic control in this strait. To find out how well Eq. (11 ) describes the freshwater height in the fjords as a function of runoff fromland we have done a regression analysis of observedH~f versus Qf using the function His = aQ~. (20)For the regression analysis we used for Qf the sum ofthe freshwater supplies to

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A. D. Kirwan Jr., G. McNally, S. Pazan, and R. Wert

) averaging period with thecombined regression coefficients. The third test determined the "best" special case for each theory.Finally, a subjective comparison was made of thebest special cases of the three different theories. In the analysis described above, the drifters movecontinuously, whereas the FNWC surface windanalysis prescribes the wind on a 2.5- x 2.5- gridevery synoptic period. It is necessary then to obtain records of the wind field and drifter velocitiesat the same point in time and space

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David M. Checkley Jr. and Martin Lindegren

change points in ResSST SIO,PDO (1988; Figs. 5a , 6 ) and ResSST SC,PDO (1988; Fig. 6 ) that manifest as troughs, consistent with the STARS analysis. The other OLS-CUSUM plots using PDO as the predictor show fluctuations largely within the confidence intervals, except for PG and ER during the 1930s ( Fig. 6 ). These results are insensitive to the regression period; for example, the same discontinuity was detected when the predictor was based on the PDO–SST SIO regression from 1916 to 1975 (not

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Michael M. Whitney, David S. Ullman, and Daniel L. Codiga

bin is the same width. A running mean (averaging over the previous 30 days) is applied to river discharge for the regime analysis (discussed in section 4c ) and multivariate regressions (discussed in section 5b ); the guiding principle is the recent discharge history influences salinity and velocity fields in large estuaries. The mean discharge bin (492–883 m 3 s −1 ) is centered on the mean discharge value (688 m 3 s −1 ) and is one standard deviation wide; the low discharge (<492 m 3 s −1

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Emma M. Giunipero and Allan J. Clarke

edge In the previous subsection we described qualitative changes in the interannual flow based on slopes in interannual SSH deduced from a correlation and regression analysis. Here we attempt to estimate those interannual flows quantitatively. In particular, to verify the theoretical relationships (7) and (8) for box 1, we will need to estimate for this box. Since the satellite tracks 101 and 177 do not cross the shelf edge at right angles ( Fig. 5 ), we cannot calculate directly from the

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S. J. Lentz, J. H. Churchill, and K. A. Davis

orbital velocity from pressure u w are highly correlated with the current burst standard deviations with regression slopes approximately equal to the expected value of and intercepts that are less than 1 cm s −1 ( Table 1 ). Table 1. Median percentage of burst variance in the wave band (periods 2 to 15 s) and the regression slope, intercept, and correlation from a linear regression analysis of the form between magnitude of burst standard deviations of the depth-average current and the wave

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Karl Bumke, U. Karger, and K. Uhlig

improve our understanding of the processes leading to and resulting from deep convection the Labrador Sea Experiment ( Lab Sea Group 1998 ) took place. Part of that experiment was the cruise of the R/V Knorr in February/March 1997. A series of oceanographical and meteorological measurements was carried out to investigate air–sea interaction and its role in the development of deep convection. The objective of the present paper is the analysis of turbulence measurements to obtain the turbulent fluxes

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Yvonne L. Firing, Mark A. Merrifield, Thomas A. Schroeder, and Bo Qiu

temperature variability in the upper 500 m. Deser et al. (1996) found a decadal-scale spatial pattern similar to that of Fig. 5 in the upper-400-m WOA data from 1970 to 1991. A regression covering only the time period 1970–95 (not shown) produces stronger values in most positive areas but an almost identical spatial pattern. The zero-time-lag regression analysis does not provide information about the phase of the signal. For example, a westward-propagating Rossby wave could account for the progression

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