Explainable El Niño predictability from climate mode interactions

Sen Zhao, Fei-Fei Jin (), Malte F. Stuecker, Philip R. Thompson, Jong-Seong Kug, Michael J. McPhaden, Mark A. Cane, Andrew T. Wittenberg and Wenju Cai
Additional contact information
Sen Zhao: University of Hawai‘i at Mānoa
Fei-Fei Jin: University of Hawai‘i at Mānoa
Malte F. Stuecker: University of Hawai‘i at Mānoa
Philip R. Thompson: University of Hawai‘i at Mānoa
Jong-Seong Kug: Seoul National University
Michael J. McPhaden: National Oceanic and Atmospheric Administration (NOAA)/Pacific Marine Environmental Laboratory
Mark A. Cane: Lamont Doherty Earth Observatory of Columbia University
Andrew T. Wittenberg: NOAA/OAR/Geophysical Fluid Dynamics Laboratory
Wenju Cai: Ocean University of China

Nature, 2024, vol. 630, issue 8018, 891-898

Abstract: Abstract The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1–3, yet, quantifying the sources of skilful predictions is a long-standing challenge4–7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible8–10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-024-07534-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:630:y:2024:i:8018:d:10.1038_s41586-024-07534-6

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-024-07534-6

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().