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.
- Authors:
-
[1];
[2];
[3]
- Nanjing Univ. of Information Science and Technology, Nanjing (China). CIC‐FEMD/ILCEC, Key Lab. of Meteorological Disaster of Ministry of Education, and College of Atmospheric Science; Univ. of Hawaii at Manoa, Honolulu, HI (United States)
- Univ. of Hawaii at Manoa, Honolulu, HI (United States)
- Center for Climate Physics, Inst. for Basic Science, Busan (South Korea); Pusan National Univ., Busan (South Korea)
- Publication Date:
- Research Org.:
- Univ. of Hawaii, Honolulu, HI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Science Foundation (NSF)
- OSTI Identifier:
- 1610804
- Alternate Identifier(s):
- OSTI ID: 1559379
- Grant/Contract Number:
- SC0005110
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Volume: 46; Journal Issue: 16; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; Geology; IOD; prediction; ENSO combination mode; predictability
Citation Formats
Zhao, Sen, Jin, Fei‐Fei, and Stuecker, Malte F. Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts. United States: N. p., 2019.
Web. doi:10.1029/2019gl084196.
Zhao, Sen, Jin, Fei‐Fei, & Stuecker, Malte F. Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts. United States. https://doi.org/10.1029/2019gl084196
Zhao, Sen, Jin, Fei‐Fei, and Stuecker, Malte F. Thu .
"Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts". United States. https://doi.org/10.1029/2019gl084196. https://www.osti.gov/servlets/purl/1610804.
@article{osti_1610804,
title = {Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts},
author = {Zhao, Sen and Jin, Fei‐Fei and Stuecker, Malte F.},
abstractNote = {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.},
doi = {10.1029/2019gl084196},
journal = {Geophysical Research Letters},
number = 16,
volume = 46,
place = {United States},
year = {2019},
month = {8}
}
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