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| Titel |
Improved Modelling of Sea-Level Patterns by Incorporating Loading and Self-Attraction |
| VerfasserIn |
Julian Kuhlmann, Henryk Dobslaw, Maik Thomas |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250046562
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| Zusammenfassung |
| While global sea level has been rising since the onset of the industrialization, regional sea
level shows far more variable patterns on multiple scales in space and time. During the last
two decades, regional sea-level distributions have been measured by means of satellite
altimetry; these measurements can be compared to the output of numerical global ocean
models.
We investigate the skill that oceanic general circulation models, in particular the Ocean
Model for Circulation and Tides (OMCT), show in reconstructing sea-level patterns. We find
that coarse-resolution, non-eddy-resolving models succeed in reconstructing slow, large-scale
sea-level variations such as major ocean currents, ENSO, and the seasonal cycle, while they
lack skill in simulating local extremes and fast changes that show up in the higher moments
of the local statistical distributions.
In addition, we implement a routine into the model that computes sea-level changes due
to the Loading and Self-Attraction of the seawater (LSA) using degree-dependent
Love numbers. LSA takes into account the interactions of seawater with the solid
Earth as well as the gravitational attraction that the seawater exerts on itself. Up
to now, only tidal models have considered this effect, and only in a rather basic
manner.
The impact of LSA on sea-level fields and ocean dynamics simulated with a baroclinic
circulation model is still rather unclear. In previous OMCT simulations, for instance, LSA has
been parameterized by including an additional potential proportional to the mass of the local
water column. Considering the effect by applying degree-dependent Love numbers is
expected to reduce the differences between modelled sea levels and observations
considerably. We discuss the difference that our approach makes for sea-level variability on
various temporal and spatial scales. Since the exact computation at every time step is costly in
terms of computing power, we also investigate possible trade-offs between physical
accurateness and computational effectiveness. |
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