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| Titel |
Improvement of climate predictions and reduction of their uncertainties using learning algorithms |
| VerfasserIn |
E. Strobach, G. Bel |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 15 ; Nr. 15, no. 15 (2015-08-04), S.8631-8641 |
| Datensatznummer |
250119946
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| Publikation (Nr.) |
 copernicus.org/acp-15-8631-2015.pdf |
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| Zusammenfassung |
| Simulated climate dynamics, initialized with observed conditions, is expected
to be synchronized, for several years, with the actual dynamics. However, the
predictions of climate models are not sufficiently accurate. Moreover, there
is a large variance between simulations initialized at different times and
between different models. One way to improve climate predictions and to
reduce the associated uncertainties is to use an ensemble of climate model
predictions, weighted according to their past performances. Here, we show
that skillful predictions, for a decadal time scale, of the
2 m temperature can be achieved by applying a sequential learning
algorithm to an ensemble of decadal climate model simulations. The
predictions generated by the learning algorithm are shown to be better than
those of each of the models in the ensemble, the better performing simple
average and a reference climatology. In addition, the uncertainties
associated with the predictions are shown to be reduced relative to those
derived from an equally weighted ensemble of bias-corrected predictions. The
results show that learning algorithms can help to better assess future
climate dynamics. |
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