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| Titel |
Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic
error model of an intermediate coupled model |
| VerfasserIn |
Fei Zheng, Jiang Zhu |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250139531
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| Publikation (Nr.) |
 EGU/EGU2017-2791.pdf |
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| Zusammenfassung |
| How to design a reliable ensemble prediction strategy with considering the major
uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In
this study, a new stochastic perturbation technique is developed to improve the prediction
skills of El Niño–Southern Oscillation (ENSO) through using an intermediate coupled model.
We first estimate and analyze the model uncertainties from the ensemble Kalman filter
analysis results through assimilating the observed sea surface temperatures. Then, based on
the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error
model to characterize the model uncertainties mainly induced by the missed physical
processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical
effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble
forecast at each step by the developed stochastic model-error model during the
12-month forecasting process, and add the zero-mean perturbations into the physical
fields to mimic the presence of missing processes and high-frequency stochastic
noises.
The impacts of stochastic model-error perturbations on ENSO deterministic predictions
are examined by performing two sets of 21-yr hindcast experiments, which are initialized
from the same initial conditions and differentiated by whether they consider the stochastic
perturbations. The comparison results show that the stochastic perturbations have a
significant effect on improving the ensemble-mean prediction skills during the entire
12-month forecasting process. This improvement occurs mainly because the nonlinear terms
in the model can form a positive ensemble-mean from a series of zero-mean perturbations,
which reduces the forecasting biases and then corrects the forecast through this nonlinear
heating mechanism. |
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