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Linking preconditioning to extreme ENSO events and reduced ensemble spread

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Abstract

The contribution of the subsurface precursor, defined as the buildup of heat content in the equatorial subsurface prior to El Niño-Southern Oscillation (ENSO) events, to ENSO amplitude and predictability has been unclear for some time. To address the issue, this study implements a careful experimental design to construct three March-initialized precursor ensembles using CCSM4, one ensemble with ENSO-neutral initial conditions, one with a warm precursor in the subsurface, and one with a cold precursor. The initial precursors within each respective ensemble, although generated via identical wind forcing, differ slightly due to intrinsic sources of “noise” in the ocean and atmosphere. The ensembles are then integrated fully-coupled to produce a distribution of outcomes per each type of initial condition. Results show that a precursor is not essential to produce moderate El Niño and the full range of La Niña events, whereas a warm precursor is a necessary condition to generate extreme El Niño. The findings imply that extreme El Niño and the coldest La Niña events are fundamentally different. Presence of a warm (cold) precursor in the initial condition results in a warm (cold) shift and narrowing of the distribution of outcomes, suggesting increased predictability of El Niño (La Niña). Although the cold precursor is not necessary to produce La Niña, its presence in the initial condition reduces La Niña spread more than the warm precursor reduces El Niño spread. Despite the smaller ensemble spread for La Niña, signal-to-noise ratios indicate that El Niño may be more predictable than La Niña.

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Acknowledgements

This research was supported by NSF grant AGS1450811. SL thanks Clara Deser, Michael McPhaden, and Sang-Ki Lee for comments on a proposal version of this study. We are thankful for the comments and suggestions from two anonymous reviewers that helped improve the manuscript. The authors also acknowledge computational support from the University of Miami Center for Computational Science. SODA reanalysis data can be found at http://dsrs.atmos.umd.edu/DATA/soda_2.2.4/.

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  1. Sarah M. Larson

    Present address: Nelson Institute, Center for Climatic Research, Atmospheric and Oceanic Sciences Department, University of Wisconsin, 1225 W. Dayton St., Madison, WI, 53706, USA

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  1. Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA

    Sarah M. Larson & Ben P. Kirtman

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  1. Sarah M. Larson
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Correspondence to Sarah M. Larson.

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This paper is a contribution to the special collection on ENSO Diversity. The special collection aims at improving understanding of the origin, evolution, and impacts of ENSO events that differ in amplitude and spatial patterns, in both observational and modeling contexts, and in the current as well as future climate scenarios. This special collection is coordinated by Antonietta Capotondi, Eric Guilyardi, Ben Kirtman and Sang-Wook Yeh.

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Larson, S.M., Kirtman, B.P. Linking preconditioning to extreme ENSO events and reduced ensemble spread. Clim Dyn 52, 7417–7433 (2019). https://doi.org/10.1007/s00382-017-3791-x

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