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Abstract This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.
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Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts
Abstract Despite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic‐dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean‐atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO‐dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal‐to‐noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.
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- PAR ID:
- 10453628
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 46
- Issue:
- 16
- ISSN:
- 0094-8276
- Page Range / eLocation ID:
- p. 9980-9990
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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