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Ariaan Purich
,
Matthew H. England
,
Wenju Cai
,
Arnold Sullivan
, and
Paul J. Durack

Lavergne et al. 2014 ; Morrison et al. 2015 ). Early model studies of the influence of surface freshening on Antarctic sea ice found freshening to be associated with an increase in ice coverage in ocean–ice models ( Marsland and Wolff 2001 ; Beckmann and Goosse 2003 ; Hellmer 2004 ; Aiken and England 2008 ). However, more recent studies using global coupled climate models with additional freshwater applied around the Antarctic margins to simulate increased ice sheet melt and runoff have found

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Paul Spence
,
John C. Fyfe
,
Alvaro Montenegro
, and
Andrew J. Weaver

appears to be due to anthropogenic forcing, with increasing atmospheric concentrations of both ozone-depleting gases ( Gillett and Thompson 2003 ) and greenhouse gases ( Fyfe 2006 ) identified as playing critical roles. Global climate models are remarkably consistent in simulating poleward intensifying surface winds through the past and present centuries ( Fyfe and Saenko 2006 ). While Southern Ocean sea surface temperatures (SSTs) and the Antarctic Circumpolar Current (ACC) are observed to respond to

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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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Shengpeng Wang
,
Zhao Jing
,
Qiuying Zhang
,
Ping Chang
,
Zhaohui Chen
,
Hailong Liu
, and
Lixin Wu

1. Introduction Oceanic eddies are ubiquitous in the upper ocean, as evidenced by the satellite observations and eddy-resolving model simulations over the past two decades. About 70% of the ocean kinetic energy is stored in the eddy field ( von Storch et al. 2012 ). Many efforts have been devoted to understanding the generation, propagation, and dissipation of eddies, as well as their impacts on the transport of mass, nutrients, salt, and heat (e.g., Stammer 1998 ; Chelton et al. 2011

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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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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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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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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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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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