Low-dimensional representations of Niño 3.4 evolution and the spring persistence barrier

The El Niño-Southern Oscillation (ENSO) is the dominant mode of climate variability on interannual time scales, and its temporal evolution can be summarized using the Niño 3.4 index. Here we used EOF analysis to construct low-dimensional representations of the 12-month evolution of Niño 3.4 during d...

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Bibliographic Details
Published in:NPJ climate and atmospheric science 2020-06, Vol.3 (1), Article 24
Main Authors: Tippett, Michael K., L’Heureux, Michelle L.
Format: Article
Language:English
Subjects:
639/705/531
704/106
Atmospheric models
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Autoregressive models
Climate Change/Climate Change Impacts
Climate models
Climate variability
Climatology
Covariance
Earth and Environmental Science
Earth Sciences
El Nino
Error correction
Evolution
Model matching
Representations
Southern Oscillation
Spring
Variance
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Summary:The El Niño-Southern Oscillation (ENSO) is the dominant mode of climate variability on interannual time scales, and its temporal evolution can be summarized using the Niño 3.4 index. Here we used EOF analysis to construct low-dimensional representations of the 12-month evolution of Niño 3.4 during different times of the year. The leading EOF explains more than 90% of the variance of the Niño 3.4 evolution from June to May, which means that the differences in evolution from one year to another are essentially differences in amplitude. Two EOFs explained 94% or more of the evolution variance for other 12-month periods of the year. The two-dimensional nature of the Niño 3.4 trajectories is a direct expression of the spring persistence barrier since the first EOF describes wintertime ENSO events, and the second EOF describes independent behavior during the antecedent spring. A periodic second-order autoregressive (AR2) model reproduced the observed properties, but a first-order model did not. Niño 3.4 EOFs in ocean-atmosphere coupled model forecasts matched observed EOFs with varying levels of fidelity depending on model and time of year. Forecast models with more accurate climatological covariance also have lower mean-squared error (MSE). Low-dimensional EOF-based statistical corrections reduced forecast MSE.
ISSN:2397-3722
2397-3722
DOI:10.1038/s41612-020-0128-y