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revision 1.1 by dimitri, Tue Feb 26 19:27:26 2008 UTC revision 1.5 by mlosch, Wed Jun 4 13:32:49 2008 UTC
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1  \section{Introduction}  \section{Introduction}
2  \label{sec:intro}  \label{sec:intro}
3    
4    In the past five years, oceanographic state estimation has matured to the
5    extent that estimates of the evolving circulation of the ocean constrained by
6    in-situ and remotely sensed global observations are now routinely available
7    and being applied to myriad scientific problems \citep{wun07}.  Ocean state
8    estimation is the process of fitting an ocean general circulation model (GCM)
9    to a multitude of observations.  As formulated by the consortium Estimating
10    the Circulation and Climate of the Ocean (ECCO), an automatic differentiation
11    tool is used to calculate the so-called adjoint code of a GCM.  The method of
12    Lagrange multipliers is then used to render the problem one of unconstrained
13    least-squares minimization.  Although much has been achieved, the existing
14    ECCO estimates lack intercative sea ice.  This limits the ability of ECCO to
15    utilize satellite data constraints over sea-ice covered regions.  This also
16    limits the usefulness of the ECCO ocean state estimates for describing and
17    studying polar-subpolar interactions.
18    
19  The availability of an adjoint model as a powerful research tool  The availability of an adjoint model as a powerful research tool
20  complementary to an ocean model was a major design requirement early  complementary to an ocean model was a major design requirement early
21  on in the development of the MIT general circulation model (MITgcm)  on in the development of the MIT general circulation model (MITgcm)
# Line 29  studies to motivate the present work. Line 44  studies to motivate the present work.
44  Traditionally, probably for historical reasons and the ease of  Traditionally, probably for historical reasons and the ease of
45  treating the Coriolis term, most standard sea-ice models are  treating the Coriolis term, most standard sea-ice models are
46  discretized on Arakawa-B-grids \citep[e.g.,][]{hibler79, harder99,  discretized on Arakawa-B-grids \citep[e.g.,][]{hibler79, harder99,
47    kreyscher00, zhang98, hunke97}. From the perspective of coupling a    kreyscher00, zhang98, hunke97}, although there are sea ice models
48  sea ice-model to a C-grid ocean model, the exchange of fluxes of heat  diretized on a C-grid \citep[e.g.,][]{ip91, tremblay97,
49  and fresh-water pose no difficulty for a B-grid sea-ice model    lemieux09}. %
50  \citep[e.g.,][]{timmermann02a}. However, surface stress is defined at  \ml{[there is also MI-IM, but I only found this as a reference:
51  velocities points and thus needs to be interpolated between a B-grid    \url{http://retro.met.no/english/r_and_d_activities/method/num_mod/MI-IM-Documentation.pdf}]}
52  sea-ice model and a C-grid ocean model. Smoothing implicitly  From the perspective of coupling a sea ice-model to a C-grid ocean
53  associated with this interpolation may mask grid scale noise and may  model, the exchange of fluxes of heat and fresh-water pose no
54  contribute to stabilizing the solution. On the other hand, by  difficulty for a B-grid sea-ice model \citep[e.g.,][]{timmermann02a}.
55  smoothing the stress signals are damped which could lead to reduced  However, surface stress is defined at velocities points and thus needs
56  variability of the system. By choosing a C-grid for the sea-ice model,  to be interpolated between a B-grid sea-ice model and a C-grid ocean
57  we circumvent this difficulty altogether and render the stress  model. Smoothing implicitly associated with this interpolation may
58  coupling as consistent as the buoyancy coupling.  mask grid scale noise and may contribute to stabilizing the solution.
59    On the other hand, by smoothing the stress signals are damped which
60    could lead to reduced variability of the system. By choosing a C-grid
61    for the sea-ice model, we circumvent this difficulty altogether and
62    render the stress coupling as consistent as the buoyancy coupling.
63    
64  A further advantage of the C-grid formulation is apparent in narrow  A further advantage of the C-grid formulation is apparent in narrow
65  straits. In the limit of only one grid cell between coasts there is no  straits. In the limit of only one grid cell between coasts there is no
66  flux allowed for a B-grid (with no-slip lateral boundary counditions),  flux allowed for a B-grid (with no-slip lateral boundary counditions),
67  and models have used topographies artificially widened straits to  and models have used topographies with artificially widened straits to
68  avoid this problem \citep{holloway07}. The C-grid formulation on the  avoid this problem \citep{holloway07}. The C-grid formulation on the
69  other hand allows a flux of sea-ice through narrow passages if  other hand allows a flux of sea-ice through narrow passages if
70  free-slip along the boundaries is allowed. We demonstrate this effect  free-slip along the boundaries is allowed. We demonstrate this effect
# Line 54  in the Candian archipelago. Line 73  in the Candian archipelago.
73  Talk about problems that make the sea-ice-ocean code very sensitive and  Talk about problems that make the sea-ice-ocean code very sensitive and
74  changes in the code that reduce these sensitivities.  changes in the code that reduce these sensitivities.
75    
76  This paper describes the MITgcm sea ice  This paper describes the MITgcm sea ice model; it presents example
77  model; it presents example Arctic and Antarctic results from a realistic,  Arctic and Antarctic results from a realistic, eddy-permitting, global
78  eddy-permitting, global ocean and sea-ice configuration; it compares B-grid  ocean and sea-ice configuration; it compares B-grid and C-grid dynamic
79  and C-grid dynamic solvers in a regional Arctic configuration; and it presents  solvers and investigates further aspects of sea ice modeling in a
80  example results from coupled ocean and sea-ice adjoint-model integrations.  regional Arctic configuration; and it presents example results from
81    coupled ocean and sea-ice adjoint-model integrations.
82    
83    %%% Local Variables:
84    %%% mode: latex
85    %%% TeX-master: "ceaice"
86    %%% End:
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