--- MITgcm_contrib/articles/ceaice/ceaice.tex	2008/02/26 17:21:48	1.15
+++ MITgcm_contrib/articles/ceaice/ceaice.tex	2008/02/26 19:14:36	1.16
@@ -1,4 +1,4 @@
-% $Header: /home/ubuntu/mnt/e9_copy/MITgcm_contrib/articles/ceaice/ceaice.tex,v 1.15 2008/02/26 17:21:48 mlosch Exp $
+% $Header: /home/ubuntu/mnt/e9_copy/MITgcm_contrib/articles/ceaice/ceaice.tex,v 1.16 2008/02/26 19:14:36 mlosch Exp $
 % $Name:  $
 \documentclass[12pt]{article}
 
@@ -75,26 +75,26 @@
 \section{Introduction}
 \label{sec:intro}
 
-The availability of an adjoint model as a powerful research
-tool complementary to an ocean model was a major design
-requirement early on in the development of the MIT general
-circulation model (MITgcm) [Marshall et al. 1997a,
-Marotzke et al. 1999, Adcroft et al. 2002]. It was recognized
-that the adjoint permitted very efficient computation
-of gradients of various scalar-valued model diagnostics,
-norms or, generally, objective functions with respect
-to external or independent parameters. Such gradients
-arise in at least two major contexts. If the objective function
-is the sum of squared model vs. obervation differences
-weighted by e.g. the inverse error covariances, the gradient
-of the objective function can be used to optimize this measure
-of model vs. data misfit in a least-squares sense. One
-is then solving a problem of statistical state estimation.
-If the objective function is a key oceanographic quantity
-such as meridional heat or volume transport, ocean heat
-content or mean surface temperature index, the gradient
-provides a complete set of sensitivities of this quantity
-with respect to all independent variables simultaneously.
+The availability of an adjoint model as a powerful research tool
+complementary to an ocean model was a major design requirement early
+on in the development of the MIT general circulation model (MITgcm)
+[Marshall et al. 1997a, Marotzke et al. 1999, Adcroft et al. 2002]. It
+was recognized that the adjoint model permitted computing the
+gradients of various scalar-valued model diagnostics, norms or,
+generally, objective functions with respect to external or independent
+parameters very efficiently. The information associtated with these
+gradients is useful in at least two major contexts. First, for state
+estimation problems, the objective function is the sum of squared
+differences between observations and model results weighted by the
+inverse error covariances. The gradient of such an objective function
+can be used to reduce this measure of model-data misfit to find the
+optimal model solution in a least-squares sense.  Second, the
+objective function can be a key oceanographic quantity such as
+meridional heat or volume transport, ocean heat content or mean
+surface temperature index. In this case the gradient provides a
+complete set of sensitivities of this quantity to all independent
+variables simultaneously. These sensitivities can be used to address
+the cause of, say, changing net transports accurately.
 
 References to existing sea-ice adjoint models, explaining that they are either
 for simplified configurations, for ice-only studies, or for short-duration
@@ -103,24 +103,27 @@
 Traditionally, probably for historical reasons and the ease of
 treating the Coriolis term, most standard sea-ice models are
 discretized on Arakawa-B-grids \citep[e.g.,][]{hibler79, harder99,
-kreyscher00, zhang98, hunke97}. From the perspective of coupling a
+  kreyscher00, zhang98, hunke97}. From the perspective of coupling a
 sea ice-model to a C-grid ocean model, the exchange of fluxes of heat
 and fresh-water pose no difficulty for a B-grid sea-ice model
 \citep[e.g.,][]{timmermann02a}. However, surface stress is defined at
 velocities points and thus needs to be interpolated between a B-grid
-sea-ice model and a C-grid ocean model. While the smoothing implicitly
-associated with this interpolation may mask grid scale noise, it may
-in two-way coupling lead to a computational mode as will be shown. By
-choosing a C-grid for the sea-ice model, we circumvent this difficulty
-altogether and render the stress coupling as consistent as the
-buoyancy coupling.
+sea-ice model and a C-grid ocean model. Smoothing implicitly
+associated with this interpolation may mask grid scale noise and may
+contribute to stabilizing the solution. On the other hand, by
+smoothing the stress signals are damped which could lead to reduced
+variability of the system. By choosing a C-grid for the sea-ice model,
+we circumvent this difficulty altogether and render the stress
+coupling as consistent as the buoyancy coupling.
 
 A further advantage of the C-grid formulation is apparent in narrow
 straits. In the limit of only one grid cell between coasts there is no
 flux allowed for a B-grid (with no-slip lateral boundary counditions),
-whereas the C-grid formulation allows a flux of sea-ice through this
-passage for all types of lateral boundary conditions. We
-demonstrate this effect in the Candian archipelago.
+and models have used topographies artificially widened straits to
+avoid this problem \citep{holloway07}. The C-grid formulation on the
+other hand allows a flux of sea-ice through narrow passages if
+free-slip along the boundaries is allowed. We demonstrate this effect
+in the Candian archipelago.
 
 Talk about problems that make the sea-ice-ocean code very sensitive and
 changes in the code that reduce these sensitivities.
