TY - JOUR
T1 - Simple stochastic dynamical models capturing the statistical diversity of El Niño Southern Oscillation
AU - Chen, Nan
AU - Majda, Andrew J.
N1 - Funding Information:
The research of A.J.M. is partially supported by the Office of Naval Research Grant Office of Naval Research, Multidisciplinary University Research Initiative (ONR MURI) N00014-16-1-2161 and the New York University Abu Dhabi Research Institute. N.C. is supported as a postdoctoral fellow through A.J.M.'s ONR MURI Grant. The authors thank the National Oceanic and Atmospheric Administration for providing Niño indexes.
Publisher Copyright:
© 2017, National Academy of Sciences. All rights reserved.
PY - 2017/2/14
Y1 - 2017/2/14
N2 - The El Niño Southern Oscillation (ENSO) has significant impact on global climate and seasonal prediction. A simple modeling framework is developed here that automatically captures the statistical diversity of ENSO. First, a stochastic parameterization of the wind bursts including both westerly and easterly winds is coupled to a simple ocean-atmosphere model that is otherwise deterministic, linear, and stable. Second, a simple nonlinear zonal advection with no ad hoc parameterization of the background sea-surface temperature (SST) gradient and a mean easterly trade wind anomaly representing the multidecadal acceleration of the trade wind are both incorporated into the coupled model that enables anomalous warm SST in the central Pacific. Then a three-state stochastic Markov jump process is used to drive the wind burst activity that depends on the strength of the western Pacific warm pool in a simple and effective fashion. It allows the coupled model to simulate the quasi-regular moderate traditional El Niño, the super El Niño, and the central Pacific (CP) El Niño as well as the La Niña with realistic features. In addition to the anomalous SST, the Walker circulation anomalies at different ENSO phases all resemble those in nature. In particular, the coupled model succeeds in reproducing the observed episode during the 1990s, where a series of 5-y CP El Niños is followed by a super El Niño and then a La Niña. Importantly, both the variance and the non-Gaussian statistical features in different Niño regions spanning from the western to the eastern Pacific are captured by the coupled model.
AB - The El Niño Southern Oscillation (ENSO) has significant impact on global climate and seasonal prediction. A simple modeling framework is developed here that automatically captures the statistical diversity of ENSO. First, a stochastic parameterization of the wind bursts including both westerly and easterly winds is coupled to a simple ocean-atmosphere model that is otherwise deterministic, linear, and stable. Second, a simple nonlinear zonal advection with no ad hoc parameterization of the background sea-surface temperature (SST) gradient and a mean easterly trade wind anomaly representing the multidecadal acceleration of the trade wind are both incorporated into the coupled model that enables anomalous warm SST in the central Pacific. Then a three-state stochastic Markov jump process is used to drive the wind burst activity that depends on the strength of the western Pacific warm pool in a simple and effective fashion. It allows the coupled model to simulate the quasi-regular moderate traditional El Niño, the super El Niño, and the central Pacific (CP) El Niño as well as the La Niña with realistic features. In addition to the anomalous SST, the Walker circulation anomalies at different ENSO phases all resemble those in nature. In particular, the coupled model succeeds in reproducing the observed episode during the 1990s, where a series of 5-y CP El Niños is followed by a super El Niño and then a La Niña. Importantly, both the variance and the non-Gaussian statistical features in different Niño regions spanning from the western to the eastern Pacific are captured by the coupled model.
KW - Atmospheric wind bursts
KW - Easterly trade wind
KW - Non-Gaussian statistical features
KW - Nonlinear zonal advection
KW - Three-state stochastic Markov jump process
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U2 - 10.1073/pnas.1620766114
DO - 10.1073/pnas.1620766114
M3 - Article
C2 - 28137886
AN - SCOPUS:85012905425
SN - 0027-8424
VL - 114
SP - 1468
EP - 1473
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 7
ER -