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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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William Perrie and Bechara Toulany

response. The very lowest frequency, for example 0.10 Hz, did not see the changein wind direction during OP1 or OP2. This is a qualitative verification of the behavior for the relaxationtime ~, as presented by Young et al. (1987). Of course,nonuniformities in both the rate of change of winddirection and the wind speed as a function of positionand throughout the duration of OP1 and OP2 com- plicate both r and the wave responses shown inFigs. 2a-b. Results of a regression analysis of the data in

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Howard J. Freeland

.694 JOURNA.L OF PHYSICAL OCEANOGRAPHY VOLUME 18indicating the wavenumber estimated from the slopein Fig. 3. The error bars shown are 95% confidenceintervals. The sloping line through the box indicatesthe difference in wavenumber expected between O~ andK~ frequencies, based on the regression line of Fig. 4,i.e. the local slope of the dispersion curve.4. Conclusions An analysis of the ACE dataset in the diurnal tidalband indicates that the current

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John A. Church, Howard J. Freeland, and Robert L. Smith

orthogonal function analysis of currents near the Oregon coast. J. Phys. Oceanogr., 14,.25-46.Draper, N. R., and H. Smith, 1966: Applied Regression Analysis. Wiley and Sons, 407 pp. Forbes, A. M. G., 1985a: Sea level data from the Australian CoastalNOVEMBER 1986 CHURCH, FREELAND AND SMITH 1943 Experiment--a data report. CSIRO Mar. Lab. Rep. No. 171, 16 pp. , 1985b: Meteorological data from

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Yoshi N. Sasaki, Shoshiro Minobe, and Niklas Schneider

). To focus on interannual to decadal variability, a 9-month running mean filter is applied to all monthly data, unless noted otherwise. Similar results can be obtained, even if an 11-month or a 13-month running mean filter is used. b. Methods Dominant sea level variability in the KE region is identified by an EOF analysis for SLAs in the KE region (30°–40°N, 140°–170°E). Furthermore, the statistical significance of a correlation coefficient is estimated by a Monte Carlo test using 10 000 random

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N. A. Bray

, and a theoretical model of oceanic response to seasonally varying wind stress forcing is constructed to assist in the interpretation of the observations. The observations are historical conductivitytemperature-depth data from the Bay of Biscay region (2-20-W, 42-52-N), a series of eleven cndses overthe three years 1972 throuih 1974, spaced approximately three months apart. The analysis of the observationsutilizes a new technique for identifying the adiabatically leveled density field corresponding

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Adriana Huyer

.e., a decreasein the strength of the upwelling-favorable wind) resultsin higher surface temperatures, and lower surfacesalinities and densities, over the entire shelf. There isa hint that temperature in the bottom boundary layeralso increases when upwelling weakens. A similar regression analysis of these variables onday-number (Fig. 8) shows that a linear trend withtime accounts for more than 60% of the near-bottomtemperature and density variance over much of thecontinental shelf. In. contrast

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T. M. Shaun Johnston and Mark A. Merrifield

both EOF analyses, suggesting that these modes are robust and not an artifact of the tide gauge sampling. Since the combined modes are obtained from a correlation matrix analysis, the EOF spatial patterns do not necessarily scale with the amplitude of the modal variability. To correct for this, we regress the first and second mode temporal expansions ( Fig. 6c ) onto the separate datasets using a least squares fit ( Figs. 6 and 7 ). The modes 1 and 2 spatial patterns for HS400 are similar to sea

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Jose Henrique G. M. Alves, Michael L. Banner, and Ian R. Young

linear least squares regression through the cloud of collocated data gives a slope m = 0.97 at both observation sites. Although of secondary importance to our analysis of directional properties of wave spectra, it is also desirable to force the wave model with wind speeds consistent with observations. The majority of observed wind speeds in the BODC database during the period of interest were visual estimates based on the Beaufort scale made by oceanographic or merchant vessels. The use of visual

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M. Spaulding, T. Isaji, D. Mendelsohn, and A. C. Turner

FIG. 10, Model predictions o-winds, currents (Shpanberg and Bering straits) and sea elevation (Chukchi, Bering, Anadyr and Shpanberg), June 1982.(.3Bering Strait for each case. The response is symmetricwith respect to wind direction and gives a zero totaltransport when integrated over all simulations. Thelinear regression analysis for the eight-unit wind casescombined is T = -0.005 + 0.083 Wwith an R = 0.392.This simulation clearly illustrates the importance ofOCWOBER 1987 M. SPAULDING, T. ISAJI

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