Linking preconditioning to extreme ENSO events and reduced ensemble spread

Sarah M. Larson, Ben P. Kirtman

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)7417-7433
Number of pages17
JournalClimate Dynamics
Issue number12
StatePublished - Jun 1 2019

ASJC Scopus subject areas

  • Atmospheric Science


Dive into the research topics of 'Linking preconditioning to extreme ENSO events and reduced ensemble spread'. Together they form a unique fingerprint.

Cite this