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Craig M. Risien and Dudley B. Chelton

data record analyzed here, harmonic analysis is preferable to long-term averages because it is less susceptible to spurious effects from highly anomalous winds during one or more months. Moreover, harmonic analysis has the advantage that the seasonal value can easily be obtained from the regression coefficients at arbitrary time intervals, for example, the short integration time step used in numerical ocean circulation models. The SCOW climatology differs from our previous Climatology of Global

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Graig Sutherland, Chris Garrett, and Mike Foreman

this system is approximately 16 h, the same value that Crean et al. (1988) estimated to be the resonant period with their 2D model. 5. Summary The nonproximity of the diurnal and semidiurnal frequencies to resonance necessitated the generalization of the simple formula applied to the Bay of Fundy ( Garrett 1972 ). We have fitted the simple models for a rectangular bay and a Helmholtz oscillator using regression analysis for the elevation gain and phase. These models produced a resonant period of

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G. D. Quartly and M. A. Srokosz

). The investigation proceeds by first looking at estimates of mesoscale variability from Geosat measurements of sea surface topography. The data and themethod of analysis are described in section 2 of thepaper. This is followed in section 3 by a description ofthe analysis applied to the output from FRAM, and insection 4 the FRAM and Geosat results are compared(excluding seasonal effects). Section 5 describes thestatistical analysis of the Geosat mesoscale variabilityestimates to try to determine the

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C. Brock Woodson

offshore (onshore) bottom layer transport. Through continuity these flows can drive an upwelling (equatorward current) or downwelling (poleward current) response on the inner shelf that may contribute significantly to observed cross-shelf exchange. d. Multiple regression analysis To address the above forcing mechanisms, I conducted a multiple regression with each bin depth of the ADCP record as the response variable, and the expected contributors to cross-shelf exchange, regional, or local winds (along

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Bradford Butman and Robert C. Beardsley

, testing tosee if the sum of the squared residuals was reduced.The summation in (1) was truncated at the minimumN (usually N = I or 2) such that all higher coefficientswere not significantly different from zero. If all coefficients in the summation were not significantly different from zero, then the monthly time series was bestrepresented as a constant with no significant seasonalcycle. The results of the regression analysis are tabulatedin Table 3. This regression method of determining

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J. R. E. Lutjeharms

potential for the Southern Ocean referenced to 3000 m. Intervals are at 0.5 x 108 m3s-~ with the exception of an 1.25 x 108 m3 s-~ isoline inserted next to the Antarctic coastline to distinguish some featuresthere. Dashed lines indicate subjective contouring due to lack of data. Shaded areas are less than 3000 m in depth. Positionsof hydrographic stations used for the analysis are indicated.when these transport values are compared to thosederived by others. The Antarctic Circumpolar Current extends

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Edward D. Zaron and David A. Jay

close to the predicted slope during the 1982/83 period. In contrast, in the 2009/10 time period, there is no correlation between the phase modulations. Fig . 6. Phase modulations at Kahului computed by a response analysis over 5-day windows during (left) 1982/83 and (right) 2009/10 from the Kahului record. Phase modulations caused by timing error affect both constituents and would fall along the dashed black line. The heavy red line is computed by orthogonal regression of the K 1 phase onto the M 2

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B. B. Hicks and G. D. Hess

). Table 3 presents theresults of a correlation analysis performed in order toinvestigate this possibility, using data sets B, E and F.Although two of the regressions yield correlation coefficients that are statistically significant, the inferreddependencies are of opposite sign and hence the generalwind speed dependence is uncertain. For the present,no effect of wind speed on the Bowen ratio will beconsidered.Models for correcting the measured The relationship illustrated in Fig. ! can be used

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Andreas Münchow, Thomas J. Weingartner, and Lee W. Cooper

represents the deviation of the sample from an international standard such as Vienna-Standard Mean Ocean Water (V-SMOW), that is, δ 18 O(‰) = 1000( 18 O/ 16 O sample − 18 O/ 16 O V-SMOW ) /( 18 O/ 16 O V-SMOW ). (1) Cooper et al. (1997) describe details of the analysis methods, which have standard errors of about 0.1‰ in δ 18 O. Strain and Tan (1993) discuss how melting and freezing affects the distribution of δ 18 O as a function of depth and salinity. 3. Hydrography During our surveys and

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Thierry Delcroix and Catherine Gautier

numbers of XBT profiles used to calculate the heatcon. tent. (right panel) Regression analysis between the two heat content estimates ~(XBT) and T(SL). Re, rms. (~r.) and R~, rms. (~r.) aredefined in Table 1. FIG. 4b. (left panel) Comparison between 3-month-averaged XBT heat content estimates ~(XBT) in the (3-N-5-N, 155-W-170-W)rectangles (solid lines) and the heat content derived from sea level recorded in Fanning Island ~(SL) (broken lines). The bars denote thestandard deviations of ~(XBT) during

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