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Scott C. Doney, Steve Yeager, Gokhan Danabasoglu, William G. Large, and James C. McWilliams

are neglected and therefore will contribute to the residual R . Figure 7 (bottom panel) shows the slope from linearly regressing the sum Q ′ + A ′ + E ′ + V  ′ on Δ H ′. Low values of this slope indicate that the resolved terms do not capture fully the variability in Δ H ′ and that the residual R is nonnegligible. Therefore, the following analysis is incomplete in certain isolated regions of the Southern and Arctic Oceans, in the central Indian Ocean, and at various near-coastal sites

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A. L. Ponte, G. Gutiérrez de Velasco, A. Valle-Levinson, K. B. Winters, and C. D. Winant

envelopes) is explored through a linear regression analysis of the EOF time series against τ x (along-bay wind stress), a 12 (the sea level envelope of the semidiurnal tide), and a 24 (the sea level envelope of the diurnal tide). The analysis is succinctly described in the appendix . The contribution of individual forcing functions to the circulation is identified by low p values. Regressions with the use of a lagged wind stress showed lower skills, hence the present choice of no lag

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Alexis Lugo-Fernández and Robert R. Leben

comparable to the linear correlation reported in Leben (2005) . Equation (5) will be tested as part of the data analysis. At this point, two aspects of Eq. (1) need to be discussed. First, we assumed that H and W are constants. The justification for this assumption comes from observations and theoretical considerations: 1) Sheinbaum et al. (2002) show that H ∼ 800 m and is set by the channel depth off Miami; 2) observations in the LC confirm the two-layer approximation with an interface

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Francesco Fedele and Felice Arena

b are realizations of two random variables, say, A and B , respectively. Then, the storm peak probability density function (pdf) p A ( a ) = Pr[ A ∈ ( a , a + da )] is not fitted directly to the observed storm peak data via ad hoc regressions, but it follows analytically by requiring that the average times spent by the equivalent and actual storm sequences above any threshold h be identical. So, the significant wave height history or the actual storm sequence is stochastically

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W. Timothy Liu

. Thesystematic dependences of the difference between scatterometer winds and ship winds on sea surface temperatureand atmospheric stability are identified. The quality of ship reports is not ideal but should not depend onalmospheric stability or sea surface temperature. The systematic dependences, therefore, may reflect the characteristics of scatterometer winds. Multivariate regressions are used to extract the independent effects of different factors on the wind speeddifferences. The difference between

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A. D. Kirwan Jr. and W. J. Merrell Jr.

margin for error. Fig. 7c shows themost reliable result.$. Summary In their purported analyses of relative diffusion,Kao et al. (1968) fail to make clear that their basicassumption of a stationary separation process is validonly if the particle motions are independent. In thatlimit relative diffusion, in effect, is absolute diffusion and the analysis of Kao et al. (1968) is inappropriately titled. Kirwan et al. (1978) add to the confusion. They do not state that a stationary separationprocess is

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Frederick C. Jackson and Robert E. Jensen

timescale, τ f = 1/(2 πbf  ). (2) The relaxation parameter may or may not be a function of nondimensional wave frequency or of wave age, c / U, where c = g /( 2π f  ) is the wave phase speed and U is the wind speed ( g being acceleration of gravity). Hasselmann et al. (1980) applied the above relaxation model to several wind turning events in which the wind shifts could be described as either more or less steplike or continuously rotating in nature. From a one parameter regression analysis

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Motoyasu Miyata and Gordon W. Groves

',v',p') at Honolulu wereadded and a similar regression analysis made. In thiscase the unbiased residual sea-level spectrum was lowerJULY1971 MOTOYASU MIYATA AND GORDON W. GROVES 207180'1.00'8i0 0,00,2 0.4 O.b FREQUENCY (c~d)a. North-south surface ~vind.180'1.o ~08 - - J 0 00'w Q6uZw~ 0.4u - !iI It

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Pearn P. Niiler and Jeffrey D. Paduan

. It is ourfirm belief that the same model would also apply to thelower-frequency motions except for the fact that themesoscale eddies introduce a large, incoherent signalinto our analysis. The reason for filtering high frequencies out of our simple regression model is that phase isa function of frequency for periods shorter than 5 days.Thus a simple regression model, independent of timelag, would not apply to these motions. The results in Figs. 3a and 3b show that, for thecoherent 5 to 20 day

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Birgit Klein and Gerold Siedler

the solution vector xallowing only positive elements of x; that is, water massTABLE 1. Definition of source water types and standard deviations obtained from regression analysis.NACW SACWParameter Upper Middle Lower Ui~per Lower SDT 18.972 13.893 10

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