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Franklin B. Schwing and Jackson O. Blanton

relationship between each wind seriesand current data can be further examined using thesimple linear regression of Vy on ry. The form of theequation is V~ = 0o +/~r~. (3)Writing Eq. (1) in this form, the intercept 00 is equalto -kogh(On/Oy) and the slope 0, is k, where k = l/(Co' Vrms), the inverse of the drag coefficient times theroot-mean-squared tidal velocity. In the regressionanalysis, inputs are actual velocity and stress values.Using the same constants for o,g, h and Co as in

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Peter A. E. M. Janssen

affected by errors in observed windspeed and wave spectrum. To that end, an allowancewas made for a 5% relative error in wind speed and a10% relative error in the wave spectrum, and theregression analysis was redone. The results are presented in Table 1. It is concluded from Table I that although modeledstresses deviate from the observations, this deviationRRESPONDENCE TABLE 1. Results of regression analysis.1603 CorrelationSlope Intercept coef.Quasi-linear (QL

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W. Rockwell Geyer, John H. Trowbridge, and Melissa M. Bowen

the Hudson. The estimated offset of the pressure gradient was determined from the linear regression analysis of the integral estimate of bottom stress against the drag-law estimate ( Fig. 5 ), chosen to bring the y intercept of the regression to zero. Based on this analysis, the mean surface pressure gradient was 0.9 × 10 −2 Pa m −1 , directed seaward. There was a very small difference between springs and neaps. This compares to a mean landward-directed pressure gradient at the bottom of 3

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T. P. Barnett, R. W. Preisendorfer, L. M. Goldstein, and K. Hasselmann

1150 JOURNAL OF PHYSICAL OCEANOGRAPHY Vot. vM- 11NOTES AND CORRESPONDENCE Significance Tests for Regression Model HierarchiesT. P. ]~ARNETT, R. W. PR-ISENDORFER, L. M. GOLDSTEIN AND K. HASSELMANNClimate Research Group, Scripps Institution of Oceanography, La Jolla, CA 9209329 May 1981ABSTRACT Methods of estimating the significance of optimal regression models selected from a model hierarchyproposed by Barnett and Hasselmann

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Andreas Münchow

the first CEOF mode A 1 ( t ) is scaled to fit the same graph as the wind stress. The wind stress at EB23 explains 79% of the variance of the first CEOF mode through the regression A 1 ( t ) = −0.9 + 6.8 τ, where τ is the observed wind stress in Pa along 56° clockwise from east. The constant offset −0.9 represents the temporal mean wind stress (0.13 Pa) corresponding to the mean wind field ( Fig. 6 ) that is excluded from the CEOF analysis. A measured wind stress at EB23 of 0.2 Pa thus adds

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Marcelo Dottori and Allan J. Clarke

). Clarke and Dottori (2008) showed observationally that westward propagation from the coast does indeed occur. In their analysis they first grouped the 63 frequently sampled CalCOFI hydrographic stations off southern California into six bins according to their zonal distance from the coast ( Fig. 2 ) to obtain six long (1949–2001) gappy records of dynamic height relative to 500 m. At each hydrographic station the temporal trend in dynamic height was removed. Then the 12-point annual cycle (average

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Susan Wijffels and Gary Meyers

interannual pressure changes in the coastal waveguides on either side of the throughflow, and thus transport changes. However, they lacked sea level observations on the northern side of the throughflow to help confirm their theory. An analysis of variation in depth of the thermocline between Australia and Indonesia was consistent with the theory ( Meyers 1996 ) but did not identify the waves. In addition, it is not understood how far east Indian Ocean wind-driven energy penetrates along the broken

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Donald T. Resio, Val R. Swail, Robert E. Jensen, and Vincent J. Cardone

.275, significant at the 0.05 level. The slope of the regression line for the stratified dataset is −0.0028, while with the entire dataset the slope is −0.0014, which is a reduction of a factor of 2 from the stratified dataset. All regression lines in subsequent plots will also be based only on cases with wind speed greater than 10 m s −1 , in order to maximize the fit to that part of the distribution. However, all of the data will be shown on each plot for completeness. From the preceding analysis, we see that

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Alexander G. Ostrovskii and Leonid I. Piterbarg

, wind-driven mixing, and Ekman pumping and transport (for review see Frankignoul 1985 ; Piterbarg and Ostrovskii 1997 ) resulting in timescales of the oceanic thermal response being substantially longer than the limited periods of strong weather forcing. Comparative analysis of the air–sea interactions in different seasons shows that the atmosphere generally drives the midlatitude SST anomaly in the winter season ( Cayan 1992 ). Recently, Alexander and Deser (1995) drew attention to the

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Ryuichiro Inoue, Michio Watanabe, and Satoshi Osafune

series of are the principal component of the EOF first mode (thick solid) and second mode (dashed) from (e) Π 50m and (f) Π Clim . Year 1980 represents an average over November 1979 to March 1980. Because the EOF analysis was applied to data averaged between November and March, we cannot specify whether the pattern was created by very strong rare events or continuous events over several months. To detect a contribution from monthly variability, we made use of a regression of the principal

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