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Christopher Garrett and Brian Petrie

financial support of the National Science andEngineering Research Council of Canada. APPENDIX , Statistical Considerations In this paper we have used linear regression analysis extensively to test dynamical balances and establish circulation patterns. Two points need somediscussion: 1) the value assigned to a regression coefficient depends on assumptions about the noise, and2) the confidence limits attached to correlation coefficients and regression slopes depend on the

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Sung Yong Kim, Bruce D. Cornuelle, and Eric J. Terrill

: The drift current from observations made on the Bouee Laboratoire. Cah. Oceanogr. , 23 , 1 – 15 . Gonella , J. , 1972 : A rotary-component method for analysis in meteorological and oceanographic vector time series. Deep-Sea Res. , 19 , 833 – 846 . Ha , E. C. , 1979 : High-frequency radar measurements of coastal ocean surface currents. Ph.D. thesis, Stanford University, 134 pp . Hoerl , A. E. , and R. W. Kennard , 1970 : Ridge regression: Biased estimation for nonorthogonal

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F. Anctil and M. A. Donelan

simultaneous momentum flux observations from four towers placed at different depthsalong a shore-normal line at the west end of Lake Ontario, Canada. The towers were at nominal depths of 12,8, 4, and 2 m, in order to study the influence of shoaling waves on the air-water momentum flux, namely theeffect of wave steepness and celerit~ on the aerodynamic roughness of the water surface. Analysis of the datahave shown that the momentum fluxes cannot be explailaed by mean downwind speed alone (U~0), in

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Edward D. Zaron

candidates for mapping; section 3 describes the approach taken to reduce nontidal signals and improve the precision of the tidal analysis; section 4 outlines the methodology, which combines harmonic analysis with spatial regression, for identifying the tides and dealing with the limitations imposed by the long-repeat period of the CryoSat-2 orbit; section 5 presents cotidal charts derived from CryoSat-2 and assesses their accuracy by comparing them with in situ data and other tide models; and

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Peter Müller

an observational point of view, one only has the cross-covariance function C pF (Δ x, Δ y ) and the autocovariance function C FF (Δ x, Δ y ) of the wind forcing. If one postulates a linear relationship between the response at the mooring location and the forcing at other locations via a Green’s function, as in (3.1) , then one can perform a linear multiple regression analysis in order to estimate the Green’s function. The best estimate G̃ ( x, y ) for the Green’s function, in a least

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Bin Li and Allan J. Clarke

to the forcing being strongerin the western part of the equatorial region (see Fig.3). If the wind would have been uniform east-west,then 0 ~ 1/2 (see appendix A). From our analysis in appendix A we expect that theregression coefficient B found empirically in (2.4)should approximately match the theoretical value B~+ B2 [ see (2.6) and (2.3) ]. Calculations using measuredstratification near the date line and Hmix = 50 m gaveB~ + B2 = 4.72 + 3.00 = 7.72 m2 s2 kg-l. The regression value is 6.82 m

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Georgy V. Shevchenko, Alexander B. Rabinovich, and Richard E. Thomson

Sakhalin Island, we applied a two-dimensional regression model based on the regional wind. Such models are widely used in oceanography and meteorology for analysis and prediction of ocean currents and other vector processes (cf. Emery and Thomson 2001 ). Specifically, u ( t ) = 𝗔( τ ) V ( t − τ ) + E ( t ), (7) where a ij are the regression (response) coefficients linking the cross-shore ( u ) and alongshore ( υ ) components of ice drift with the corresponding components ( U, V ) of wind

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S. Daniel Jacob and Lynn K. Shay

process by which stratified fluid below is entrained into a turbulent ML. As a result of this process, the ML depth increases and its temperature decreases. Based on the observational analysis ( JSMB ) and previous studies ( Elsberry et al. 1976 ; Chang and Anthes 1978 ; Price 1981 ; Black 1983 ; Shay et al. 1992 ), up to 80%–90% of ML cooling is due to entrainment mixing events during a tropical cyclone passage. Thus, accurate estimates of the rate at which the turbulent ML fluid entrains the

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Michal Vanicek and Gerold Siedler

(CFCs, δ 3 He) to identify further details of water mass distributions in the South Atlantic with the aim of providing an improved layer selection for the subsequent inverse analysis. This study will then deal with the deep water masses in the South Atlantic. We will first present the selected dataset and will then concentrate on the deep water (NADW + CDW). 2. Dataset Data from a total of 19 high-quality hydrographic sections are used ( Fig. 1 , Table 2 ). They include the majority of WOCE one

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John F. Middleton, Craig Arthur, Paul Van Ruth, Tim M. Ward, Julie L. McClean, Mathew E. Maltrud, Peter Gill, Andrew Levings, and Sue Middleton

, multiple regressions made using these variables (and summertime sea level) showed that only 28% of the total SST variance was explained and the analysis (just) not statistically significant at the 95% level. No clear relation was also found between SST and strong wind-forced upwelling events. Our third hypothesis implies that wintertime intrusions of the warm (light) Leeuwin Current water will be reduced (enhanced) during periods of weaker (stronger) eastward advection during El Niño (La Niña) events

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