Parameter Optimization for Real World ENSO Forecastin an Intermediate Coupled Model
Abstract
We performed parameter estimation in the Zebiak and Cane model for the real-world scenario using the approach of Ensemble Kalman Filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real world data assimilation in the coupled model, our study shows that model parameters converge towards stable values. Furthermore, the new parameters improve the real world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity. The improved prediction skill is found to be contributed by the improvement in both the initial field and the coupled model itself. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real world climate predictions in the future.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A23I2982Z
- Keywords:
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- 3315 Data assimilation;
- ATMOSPHERIC PROCESSESDE: 3336 Numerical approximations and analyses;
- ATMOSPHERIC PROCESSESDE: 3372 Tropical cyclones;
- ATMOSPHERIC PROCESSESDE: 0520 Data analysis: algorithms and implementation;
- COMPUTATIONAL GEOPHYSICS