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| Titel |
Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations |
| VerfasserIn |
A. Carrassi, R. J. T. Weber, V. Guemas, F. J. Doblas-Reyes, M. Asif, D. Volpi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 2 ; Nr. 21, no. 2 (2014-04-23), S.521-537 |
| Datensatznummer |
250120911
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| Publikation (Nr.) |
 copernicus.org/npg-21-521-2014.pdf |
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| Zusammenfassung |
Initialization techniques for seasonal-to-decadal climate predictions fall
into two main categories; namely full-field initialization (FFI) and anomaly
initialization (AI). In the FFI case the initial model state is replaced by
the best possible available estimate of the real state. By doing so the
initial error is efficiently reduced but, due to the unavoidable presence of
model deficiencies, once the model is let free to run a prediction, its
trajectory drifts away from the observations no matter how small the initial
error is. This problem is partly overcome with AI where the aim is to
forecast future anomalies by assimilating observed anomalies on an estimate
of the model climate.
The large variety of experimental setups, models and observational networks
adopted worldwide make it difficult to draw firm conclusions on the respective
advantages and drawbacks of FFI and AI, or to identify distinctive lines for
improvement. The lack of a unified mathematical framework adds an additional
difficulty toward the design of adequate initialization strategies that fit
the desired forecast horizon, observational network and model at hand.
Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and
use the notation and concepts of data assimilation theory to highlight their
error scaling properties. This analysis suggests better performances using FFI
when a good observational network is available and reveals the direct
relation of its skill with the observational accuracy. The skill of AI
appears, however, mostly related to the model quality and clear
increases of skill can only be expected in coincidence with model upgrades.
We have compared FFI and AI in experiments in which either the full system or
the atmosphere and ocean were independently initialized. In the former case
FFI shows better and longer-lasting improvements, with skillful predictions
until month 30. In the initialization of single compartments, the best
performance is obtained when the stabler component of the model (the ocean)
is initialized, but with FFI it is possible to have some predictive skill
even when the most unstable compartment (the extratropical atmosphere) is
observed.
Two advanced formulations, least-square initialization (LSI) and
exploring parameter uncertainty (EPU), are introduced. Using LSI the
initialization makes use of model statistics to propagate information from
observation locations to the entire model domain. Numerical results show that
LSI improves the performance of FFI in all the situations when only a portion
of the system's state is observed. EPU is an online drift correction method
in which the drift caused by the parametric error is estimated using a
short-time evolution law and is then removed during the forecast run. Its
implementation in conjunction with FFI allows us to improve the prediction skill
within the first forecast year.
Finally, the application of these results in the context of realistic climate
models is discussed. |
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