Improving forecasts of El Niño diversity: a nonlinear forcing singular vector approach
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Authors
Tao, Lingjiang
Duan, Wansuo
Vannitsem, Stéphane
Discipline
Earth and related Environmental sciences
Audience
Scientific
Date
2020-05-20Publisher
Springer
Metadata
Show full item recordDescription
Observations indicate that two types of El Niño events exist: one is the EP-El Niño with a warming center in the eastern tropical Pacific, and the other is the CP-El Niño with large positive SST anomalies in the central tropical Pacific. Most current numerical models are not able to accurately identify the different types of El Niño. The present study examines the dynamic
properties of the ENSO forecast system NFSV-ICM which combines an intermediate-complexity ENSO model (ICM) with a nonlinear forcing singular vector (NFSV)-based tendency perturbation forecast model. This system is able to distinguish the different types of El Niño in predictions. Hindcasts show that the NFSV-ICM system is able to capture the horizontal
distribution of the SST anomalies and their amplitudes in the mature phase of not only EP-El Niño events but also CP-El Niño events. The NFSV-ICM is also able to describe the evolution of SST anomalies associated with the two types of El Niño up to at least two-season lead times, while the corresponding forecasts with the ICM are limited to, at most, one-season
lead times. These improvements are associated with the modifications of the atmospheric and ocean processes described by the ICM through the NFSV-based tendency perturbations. In particular, the thermocline and zonal advection feedback are strongly modified, and the conditions of the emergence of both EP- and CP-El Niño events are improved. The NFSV-ICM
therefore provides a useful platform for studying ENSO dynamics and predictability associated with El Niño diversities.
Citation
Tao, L., Duan, W. & Vannitsem, S. Improving forecasts of El Niño diversity: a nonlinear forcing singular vector approach. Clim Dyn 55, 739–754 (2020). https://doi.org/10.1007/s00382-020-05292-5
Identifiers
Type
Article
Peer-Review
Yes
Language
eng