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Edward D. Zaron and Shane Elipot

taken here uses variance reduction statistics to evaluate model performance. Thus, a partial tide prediction is computed for a single constituent, say, M 2 , and this prediction is subtracted from the observations. The variance of the residual, and the difference in variance compared to the original, may then be compared among the models. An advantage of this approach is that it permits the use of more types of data for model intercomparison than could be used for comparisons of harmonic constants

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Lars Umlauf

threshold Ri c . Exactly this model property motivated JHL to include the diffusive term on the right-hand side of (11) , hence creating the possibility for nonzero mixing resulting from the “diffusion of diffusivity” from neighboring regions even if Ri > Ri c locally. It should be noted that the form of (11) and R (Ri) entirely follow from ad-hoc assumptions and involve internal variables that cannot be measured. 5. Model evaluation a. Model behavior for strong stratification Interesting insight

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J. A. Mattias Green and Jonas Nycander

accurate parameterization of the conversion from barotropic tides to baroclinic (internal) tides is therefore essential, especially since abyssal vertical mixing is poorly represented in global ocean circulation models (e.g., Zickfeld et al. 2007 ). Here, we implement three different parameterizations of the tidal conversion in a tidal model, and evaluate their performance using satellite observations of the tides. The dissipation of the barotropic tide in the abyssal ocean amounts to about 1 TW, of

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Xuefeng Zhang, Peter C. Chu, Wei Li, Chang Liu, Lianxin Zhang, Caixia Shao, Xiaoshuang Zhang, Guofang Chao, and Yuxin Zhao

until the wave-induced mixing (especially the non-breaking-wave-induced mixing) is incorporated into the ocean model ( KC04 ; Qiao et al. 2004 ; Huang and Qiao 2010 ; Kantha et al. 2010 ). For evaluating LT-affected modeling performance under the supertyphoon, vertical temperature profiles from CTRL and SPWAVE are compared against the Argo observations at (22.98°N, 123.87°E) on 17 July 2005 ( Fig. 12a ) and at (25.97°N, 126.15°E) on 16 July 2005 ( Fig. 12b ) during the passage of Haitang. The

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Lauren B. Kuntz and Daniel P. Schrag

. Oceanogr. , 36 , 592 – 605 , . 10.1175/JPO2870.1 Jochum , M. , G. Danabasoglu , M. Holland , Y. O. Kwon , and W. Large , 2008 : Ocean viscosity and climate . J. Geophys. Res. , 113 , C06017 , . 10.1029/2007JC004515 Johns , T. C. , and Coauthors , 2006 : The new Hadley Centre climate model HadGEM1: Evaluation of coupled simulations . J. Climate , 19 , 1327 – 1353 , . 10

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Luigi Cavaleri

last decade, the results of the operational wave models have reached high levels of performance. Nowadays, the average error of an advanced wave model is easily down to a few, typically negative, percents, with a bias of the order of 10 cm or fewer. However, when we analyze the statistics with respect to wave height (see Fig. 1 ), it is immediately evident that the average error, both as bias and rms, is strongly dependent on H s , the largest wave heights suffering underestimates up to 1 m or

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Paola Cessi

introduces the bottom drag r as one of the controlling parameters of the eddy diffusivity. This result is consistent with the findings of Thompson and Young (2006) in the context of a quasigeostrophic model. The velocity estimate needs to be supplemented by an eddy length estimate in order to be used for evaluating the streamfunction. Lacking any explicit constraint on the mixing length, l e is estimated from eddy-resolving simulations. Assumptions used in the literature, such as the equipartition

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Shenfu Dong and Kathryn A. Kelly

-assimilation process alters the coupling between atmosphere and ocean. In section 2 we describe the observations used to calculate formation and EDW volume, and the models evaluated in this study. The time mean and variability of the EDW and formation as well as their spatial distribution are examined in section 3 . Results from the models are compared with estimates from observations in this section to evaluate the performance of the models. Discussion and conclusions are presented in sections 4 and 5

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Friederike Pollmann, Carsten Eden, and Dirk Olbers

this study is to evaluate the model IDEMIX through a comparison with observations. Measurements that resolve turbulence, however, are sparse, especially on longer spatial and temporal scales. We therefore estimate the turbulent kinetic energy (TKE) dissipation rate and diapycnal diffusivity from finestructure data. The underlying concept is that turbulent mixing is the consequence of a nonlinear energy transfer through the internal wave spectrum and that it can thus be described in terms of

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Tianze Peng and David Richter

.g., when γ > 0). Since we calculate each term in Eq. (1) explicitly in DNS, we shift our focus to the total heat flux H T and discuss whether the bulk models provide a reasonable estimate for the total heat flux as well. We first evaluate whether one can add the spray-mediated heat fluxes to the interfacial heat fluxes estimated from spray-free conditions as F94 suggests. To test this assumption, we compute an unladen case (with subscripts 0) from DNS in which the total heat flux consists

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