Application of an inverse model in the community modeling effort results
Citable URI
http://hdl.handle.net/1912/5625DOI
10.1575/1912/5625Keyword
Oceanic mixing; Ocean circulationAbstract
Inverse modeling activities in oceanography have recently been intensified, aided
by the oncoming observational data stream of WOCE and the advance of computer
power. However, interpretations of inverse model results from climatological
hydrographic data are far from simple. This thesis examines the behavior of an
inverse model in the WOCE CME (Community Modeling Effort) results where the
physics and the parameter values are known. The ultimate hypotheses to be tested
are whether the inferred circulations from a climatological hydrographic data set
(where limited time means and spatial smoothing are usually used) represent the
climatological ocean general circulations, and what the inferred "diffusion" coefficients
really are.
The inverse model is first tested in a noneddy resolving numerical GCM
ocean. Numerical/scale analyses are used to test whether the inverse model properly
represents the GCM ocean. Experiments show how biased answers could result from
an incorrect model, and how a correct model must produce the right answers. When the inverse model is applied to the timemean hydrographic data of
an eddyresolving GCM ocean in the fine grid resolution of the GCM, the estimated
horizontal circulation is statistically consistent with the EGCM time means in both
patterns and values. Although the flow patterns are similar, the uncertainties for
the GCM time means and the inverse model estimates are different. The former
are very large, such that the GCM timemean circulation has no significance in the
deep ocean. The latter are much smaller, and with them the estimated circulations
are well defined. This is consistent with the concept that ocean motions are
very energetic, while variations of tracers (temperature, salinity) are low frequency.
The inverse model succeeded in extracting the ocean general circulation from the
"climatological" hydrographic data. The estimated vertical velocities are also statistically indistinguishable from
the GCM time means. However, significant differences between the estimated "diffusion" coefficients and the EGCM eddy diffusion coefficients are found at certain
locations. These discrepancies are attributed to the differences in physics of the
inverse model and the EGCM ocean. The "diffusion" coefficients from the inversion
parameterize not only the eddy fluxes, but also (part of) the temporal variation
and biharmonic terms which are not explicitly included in the inverse model.
Given the essentially red spectrum of the ocean, it makes sense to look for
smooth solutions. Aliasing due to subsampling on a coarse grid and the effects of
spatial smoothing are addressed in the last part of this thesis. It is shown that this
aliasing could be greatly reduced by spatial smoothing. The estimated horizontal
circulation from the spatially smoothed timemean EGCM hydrographic data with
a coarse grid resolution (2.4° longitude by 2.0° latitude) is generally consistent
with the spatially smoothed EGCM time means. Significant differences only occur
at some grid points at great depths, where the GCM circulations are very weak.
The conclusions of this study are different from some previous studies. These
discrepancies are explained in the concluding chapter.
Finally, it should be pointed out that the issue of properly representing a
GCM ocean by an inverse model is not identical to the issue of represent ing the
real ocean by the same inverse model, since the GCM ocean is not identical to the
real ocean. Numerical calculations show that both the noneddy resolving and the
eddyresolving GCM oceans used in this work are evolving towards a statistical
equilibrium. In the real ocean, the importance of temporal variation terms in the
property conservation equations should also be analyzed when a steady mverse
model is applied to a limited timemean (the climatological) data set.
Description
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 1995
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