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Richard M. Yablonsky and Isaac Ginis

recent studies suggest that coupling a 1D ocean model to a hurricane model may be sufficient for capturing the storm-induced sea surface temperature (SST) cooling in the region providing heat energy to the hurricane ( Emanuel et al. 2004 ; Lin et al. 2005 , 2008 ; Bender et al. 2007 ; Davis et al. 2008 ). If in fact a 1D model is sufficient, valuable computational resources can be saved as compared to coupled models that employ a fully three-dimensional (3D) ocean component. The purpose of this

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Carsten Eden, Lars Czeschel, and Dirk Olbers

R o Rossby radius. Also shown are surface gravity waves. Different solid lines denote different vertical modes or vertical wavenumbers. Small-scale turbulence is separated from the waves by the Ozmidov scale L o . Gray boxes denote scales currently covered by non-eddy-resolving (dark) or eddy-permitting (light) ocean models. The expected gain in computer power in the next 10 yr allows the extension of the ocean boxes by the dashed lines. Adopted from Olbers et al. (2012) . On the other hand

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P. Berloff, W. Dewar, S. Kravtsov, and J. McWilliams

1. Introduction We study the dynamic role of the mesoscale oceanic eddies in an idealized coupled ocean–atmosphere model of midlatitude climate ( Kravtsov et al. 2006 , 2007 ). The model components are placed in a highly nonlinear regime by an appropriate choice of spatial resolution and frictional parameters and are characterized by vigorous intrinsic variability. The oceanic flow is in the classical double-gyre circulation regime, which has been considered previously with prescribed wind

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J. C. Muccino, H. Luo, H. G. Arango, D. Haidvogel, J. C. Levin, A. F. Bennett, B. S. Chua, G. D. Egbert, B. D. Cornuelle, A. J. Miller, E. Di Lorenzo, A. M. Moore, and E. D. Zaron

1. Introduction The Inverse Ocean Modeling (IOM) system is a modular system for constructing and running weak-constraint, four-dimensional variational data assimilation (W4DVAR) for any linear or nonlinear functionally smooth dynamical model and observing array. Details of the IOM are described in a companion paper ( Bennett et al. 2008 ) and only briefly summarized here. The objective of this paper is to demonstrate the flexibility, power, and usefulness of the IOM. Implementation of the IOM

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Riccardo Farneti

1. Introduction In recent years there has been a growing interest in the scientific community in studying ocean–atmosphere coupled models ( Liu 1993 ; Frankignoul et al. 1997 ; Barsugli and Battisti 1998 ; Goodman and Marshall 1999 , hereinafter GM99 ; Ferreira et al. 2001 ; White et al. 1998 ; Neelin and Weng 1999 ; White 2000a ; Gallego and Cessi 2000 ; Cessi and Paparella 2001 ; Colin de Verdière and Blanc 2001 ; Kravtsov and Robertson 2002 , to mention a few). Different

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Gaëlle de Coëtlogon, Claude Frankignoul, Mats Bentsen, Claire Delon, Helmuth Haak, Simona Masina, and Anne Pardaens

involves the interaction of the wind-driven circulation with changes in the thermohaline circulation. This can be investigated using oceanic general circulation models (OGCMs). However, the GS transport is much too weak in non-eddy-resolving OGCMs, and the GS does not separate from the coast at Cape Hatteras, but follows the continental shelf until the Grand Banks, leaving no space for the slope sea and the observed northern cyclonic circulation cell. This happens because inertial effects and the

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George L. Mellor, Mark A. Donelan, and Lie-Yauw Oey

priori, vertically integrated, rendering them unsuitable for coupling with depth-dependent numerical ocean circulation models. Now, as a consequence of (the revised) M03 , it is possible to couple three-dimensional circulation models with wave models; the coupling includes depth-dependent wave radiation stress terms, Stokes drift, vertical transfer of wave-generated pressure transfer to the mean momentum equation, wave dissipation as a source term in the turbulence kinetic energy equation, and mean

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Kristopher B. Karnauskas, Gregory C. Johnson, and Raghu Murtugudde

means are often used (e.g., Solomon et al. 2007 ; Clement et al. 2010 ). Multimodel ensemble means often yield a more realistic solution than any single model ( Reichler and Kim 2008 ). However, assessments often do not consider subsurface ocean characteristics of relevance to climate change. Strong regional coupling between the ocean and atmosphere implies that climate change over the coming decades and centuries will depend on spatial variations in sea surface warming ( Saravanan 1998 ; Hurrell

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Vinu Valsala, Shamil Maksyutov, and Ikeda Motoyoshi

1. Introduction An offline model for ocean tracer transport is devised and applied to a conservative tracer and results are validated. The ocean tracer transports are an order of magnitude slower than that of atmospheric transports. It requires relatively longer runs to investigate the life cycle of trace materials in oceans such as chlorofluorocarbon (CFC) or dissolved inorganic carbon (DIC). A typical example of such a slow transport is an intrusion of anthropogenic atmospheric CO 2 in the

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A. F. Bennett, B. S. Chua, B. L. Pflaum, M. Erwig, Z. Fu, R. D. Loft, and J. C. Muccino

to develop great amounts of software beyond that involved in a conventional forward model. The mostly commonly cited labor is the development and coding of the so-called adjoint model, but many other algorithmic elements must also be developed, coded, and intricately linked. The Inverse Ocean Modeling (IOM) system minimizes the effort required for W4DVAR. The IOM minimization algorithm is an iterated implementation of the “indirect representer method” ( Bennett and Thorburn 1992 ; Egbert et al

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