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| Titel |
Inclusion of tide gauge sea level and filter improvements in the SODA reanalysis |
| VerfasserIn |
James Carton, Steve Penny, Gennady Chepurin |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250099848
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| Publikation (Nr.) |
 EGU/EGU2014-15682.pdf |
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| Zusammenfassung |
| The Simple Ocean Data Assimilation (SODA) reanalysis of global ocean climate relies
heavily on the historical hydrographic data set of some nine million temperature and salinity
profiles, together with historical SST observations using OI-based sequential estimation. Here
we describe two types of algorithm improvements. The first are the upgrades needed to
include the 100 to 1000 historical coast and island tide gauge sea level time series in the
reanalysis. The second are the upgrades associated with replacing the current OI-based
sequential estimation with an ensemble filter.
Relative sea level is a mixed oceanographic-geophysical variable that includes effects of
land motion and shifts in ocean mass as well as thermal expansion. The variable is of interest
on its own because of the impact of sea level rise on coastal areas, but also because of the
information it contains on the changing thermal structure of the ocean when combined with
hydrography. In this study we are primarily interested in extracting the latter information.
Inclusion of sea level time series in SODA is also complicated by the complex structure of the
forecast error covariance whose spatial scale grows at periods longer than a year. Here we
discuss the structure of the error covariance showing examples for particular gauge locations
and estimate the impact of the observations through gauge comparisons to reanalyses in
which the tide gauge time series are withheld. The results show that the greatest
benefit from the inclusion of sea level observations will occur in the subtropical and
midlatitude North Atlantic and Pacific where the gauges contain unique information
about interannual to decadal variability. We propose an algorithm to handle the
hydrographic and sea level data separately in order to take best advantage of their different
characteristics.
In the second part of the talk we describe efforts to upgrade the assimilation
algorithm used in SODA to an ensemble filter based on the Local Ensemble Transform
Kalman Filter (LETKF). This type of upgrade is appealing for several reasons,
not least of which is that it brings SODA into alignment with many atmospheric
reanalyses, a prerequisite for coupled assimilation, and the presence of ensemble
members provides a straightforward way to estimate uncertainty. Results are presented
comparing parallel experiments with SODA-OI and SODA-LETKF, illustrating the
tradeoffs between the old and new approach, along with a discussion of some of the
outstanding problems. Finally we discuss the possible advantages of a hybrid approach. |
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