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Learning ENSO Dynamics from Data
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DOI：10.1175/JCLI-D-25-0053.1.png)
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Abstract
En 中文
Much of the predictability of seasonal climate anomalies around the globe is due to the predictability of tropical El Ni & ntilde;o-Southern Oscillation (ENSO) events and their global teleconnections. Despite decades of research, however, the relative roles of the dominant positive and negative feedbacks on predictable ENSO event magnitudes, patterns, and durations remain unclear. Here, an attempt is made to estimate these feedbacks directly from observational data. A 15-component linear inverse model (LIM) of the coupled Indo-Pacific atmosphere-ocean climate system is constructed for this purpose using reanalysis data for 1979-2017, and its predictable dynamics are clarified through a series of feedback-denial experiments. This approach yields, for the first time from data, a clear picture of the dominant competition between the destabilization of ENSO by positive near-surface zonal wind and subsurface oceanic feedbacks and its stabilization by a negative surface shortwave flux feedback associated with cloud shielding in cloudy areas. The results suggest that it is primarily this negative feedback that renders ENSO asymptotically stable. They also suggest that its underrepresentation is likely behind the tendency of climate models to extend the equatorial Pacific warming during El Ni & ntilde;o (and cooling during La Ni & ntilde;a) too far west into the western Pacific, compromising seasonal and longer-term predictions around the globe. An underrepresentation of this negative feedback over the Maritime Continent is also consistent with the mean sea surface temperature (SST) cold tongue and easterly trade wind biases of many climate models over the western equatorial Pacific.
Keywords:
Atmosphere-ocean interaction
Model errors
Stochastic models
Tropical variability
Journal
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4
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1.4W
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5.9W
