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Strong 2023–2024 El Niño generated by ocean dynamics

Nature Geoscience volume 18, pages 471–478 (2025)Cite this article

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Abstract

Globally, 2023 was the hottest year on record and saw the development of a strong El Niño with widespread impacts. This El Niño event was unusual for its strong oceanic warming yet muted Southern Oscillation and wind anomalies over the tropical Pacific. This discrepancy is perplexing given the historically close coupling of El Niño and the Southern Oscillation. Atmospheric model experiments show that warming in the Atlantic and Indian Oceans in 2023 and the slow background sea surface temperature trend reduced the surface wind response over the tropical Pacific by modulating the Walker circulation. We develop a hindcast system that reproduces 87% of the June–December El Niño warming even without wind stress feedback after April 2023. The intense oceanic warming was primarily driven by the strong build-up of western Pacific heat content during the preceding prolonged La Niña. This indicates that the 2023–2024 El Niño primarily arose from oceanic processes, independent of the classic positive Bjerknes feedback mechanism. Due to the strong ocean memory, this event was highly predictable at long time leads. Climate model simulations suggest that such 2023-like El Niños may become more frequent in a warming climate.

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Fig. 1: Climate states for the 2023–2024 El Niño.
Fig. 2: Evolution of the 2023–2024 El Niño and the composite El Niño based on comparable events in 1982–1983, 1997–1998 and 2015–2016.
Fig. 3: Atmospheric response from the AGCM experiments.
Fig. 4: The impacts of ocean initial conditions and wind stress anomalies on the 2023–2024 El Niño and the other three comparable El Niños.

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Data availability

The OISSTv2 dataset is available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html; ERA5 reanalysis data at https://cds.climate.copernicus.eu/; GPCP at https://psl.noaa.gov/data/gridded/data.gpcp.html; GODAS at https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html and CESM-LENS2 at https://www.cesm.ucar.edu/community-projects/lens2/data-sets.

Code availability

The code is available via Zenodo at https://doi.org/10.5281/zenodo.15074285 (ref. 59).

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Acknowledgements

S.-P.X. was supported by the National Science Foundation (NSF, AGS 2105654) and NASA (80NSSC24M0010). A.M. was supported in part by the Japanese Ministry of Education, Culture, Sports, Science and Technology programmes for the advanced studies of climate change projection (JPMXD0722680395). M.T.L. was supported by NASA FINESST Graduate Fellowship 80NSSC22K1528 and the Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA20OAR4320271, contribution number 2025-1440. The National Center for Atmospheric Research (NCAR) is sponsored by the NSF under Cooperative Agreement 1852977. We would like to acknowledge high-performance computing support from the Derecho system (https://doi.org/10.5065/qx9a-pg09) provided by the NSF NCAR, sponsored by the NSF.

Author information

Authors and Affiliations

  1. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Qihua Peng, Shang-Ping Xie, Ayumu Miyamoto, Pengcheng Zhang & Matthew T. Luongo

  2. National Center for Atmospheric Research, Boulder, CO, USA

    Clara Deser

  3. Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA, USA

    Matthew T. Luongo

  4. School of Oceanography, University of Washington, Seattle, USA

    Matthew T. Luongo

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  1. Qihua Peng
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  2. Shang-Ping Xie
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Contributions

Q.P. and S.-P.X. conceived the study. A.M., Q.P. and M.T.L. performed numerical experiments. Q.P. and A.M. conducted the analysis. Q.P. and S.-P.X. drafted the paper. Q.P., S.-P.X., A.M., C.D., P.Z. and M.T.L. contributed to interpreting the results and improving the manuscript.

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Correspondence to Qihua Peng or Shang-Ping Xie.

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Nature Geoscience thanks Michelle L’Heureux and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Evolution of SSTA and 10-m wind anomalies during 2023-2024.

Observed SSTA (°C, color shading) and 10-m wind anomalies (m/s, vectors) averaged in (a) February-April (FMA), (b) May-July (MJJ), (c) August-October (ASO), (d) November-January (1) (NDJ), and (e) FMA 2024. Time series of (f) upper-300 m OHC anomalies (1022J) in the western Pacific Ocean (WPAC, 130˚E–180˚, 10˚S–10˚N) and (g) percentage of 2023–2024 observed CWP u10 and SOI compared to their expected values derived from regression analysis. All anomalies are defined relative to the period 1982-2022.

Extended Data Fig. 2 Decomposition of observed SSTA for the AGCM experiments.

The June-December 2023 averaged (a) SSTA (°C), (b) detrended SSTA component, and (c) the background SST trend component for the period 1982-2023, obtained by subtracting (b) from (a). All anomalies are calculated with reference to the 1982–2022 climatological values. Time series of (d) detrended and trended Niño3 index as well as (e) SSTAs averaged over the tropical North Atlantic (0-70°W, equator-30°N) and tropical western Indian Ocean (40°E-70°E, 10°S-10°N).

Extended Data Fig. 3 The mixed layer heat budget for the 2023-2024 El Niño.

(a) Heat budget terms of the 2023-2024 El Niño (10−7°C/s, averaged in Niño 3 region) (see Materials and Methods). (b) The relative importance of thermocline feedback (TH, red line), Ekman feedback (EK, magenta line), and the nonlinear term (blue line) in modulating the vertical advection term. These results are derived from reanalysis data.

Extended Data Fig. 4 Comparison of the observed and simulated ocean temperature anomalies for the 2023-2024 event.

(a) Longitude-time diagram of observed equatorial SSTAs (color shading; °C) and SLAs (contours with an interval of 3 cm; positive black and negative gray). (b)-(e) shows the evolution of observed equatorial ocean temperature anomalies (°C, color shading) for the 2023-2024 El Niño averaged over (a) January-March (JFM), (b) April-June (AMJ), (c) July-September (JAS), and (d) October-December (OND). (f)-(j) Similar to the upper panels but from the CTRL run. The black (grey) line represents the 2023 (climatological) 20 °C isotherm. All meridionally averaged over 2˚S–2˚N.

Extended Data Fig. 5 Observed climate state during 2020-2023.

Longitude-time evolution of the (a) equatorial SLA (m), (b) SSTA (°C), and (c) 10-m zonal wind anomalies (m/s) during January 2020-December 2023. All meridionally averaged over 2˚S–2˚N. (d)-(f) Observed SLA (m, color shading), SSTA (contours with a 0.2 °C interval), and surface wind anomalies (m/s, vectors) averaged over June–December for the years 2020, 2021, and 2022, respectively. The lower panels are similar to the middle panels but averaged in (g) JFM, (h) AMJ, and (i) JAS 2023, respectively.

Extended Data Fig. 6 The impacts of initial condition on the 2023-2024 and other comparable El Niños.

Simulated SLA (m, color shading) and SSTA (contours with an interval of 0.5 °C; positive black and negative gray) in (a) January-March (JFM) 2023 from the CTRL Run, which roughly describes the initial condition for the InitApr2023. (c), (e), and (g) same as (a) but averaged in (c) AMJ, (e) JAS, and (g) OND from the InitApr2023 experiment. The right panels are similar to the left panels but for other comparable El Niño composites.

Extended Data Fig. 7 Equatorial temperature changes from the InitApr2023 and InitAprOther experiments.

(a) The JFM ocean temperature anomalies from the CTRL run, which generally describes the initial condition for the InitApr2023. (b)-(d) The evolutions of equatorial ocean temperature anomalies averaged over (a) JFM, (b) AMJ, (c) JAS, and (d) OND from InitApr2023. (e)-(h) Similar to upper panels but for the other comparable El Niño composite (InitAprOther). The black (grey) line represents the 2023 (climatological) 20 °C isotherm. All panels are meridionally averaged over 2˚S–2˚N.

Extended Data Fig. 8 Future changes in 2023-like El Niños.

Hovmöller diagram of equatorial (a) SLA (m, color shading) and SSTA (°C, contours with an interval of 0.2 °C; positive black and negative gray), and (b) zonal wind stress (color shading; N/m2) for the 2023-like El Niño composites during 1900-1990. (c)-(d) Same as (a)-(b) but for other non-2023-like El Niños. (e) Comparison of the ensemble mean (bars) number of 2023-like El Niño (see Materials and Methods) during 1900-1990 and 2000-2090, along with their differences. The error bars indicate inter-member ±1 standard deviation (n = 99). (f) Scatter plot for changes in the number of strong positive SLA events in the WPAC region during JFM (SLA > 4.5 cm) and changes in the number of 2023-like El Niño events. (g) Regression (sign reversed) of WPAC JFM SLA (m) onto the preceding August (−1)-January equatorial (averaged over 130˚E–90˚W and 2˚S–2˚N) zonal wind stress anomalies (Taux) during 1900–1990 and 2000–2090, along with their differences. Bars (errorbars) are the ensemble mean (inter-member ±1 standard deviation) (n = 99). (a)-(g) are derived from the CESM-LENS2 simulations. (h) Simulated JFM interannual SLA s.d. percentage changes due to global warming, calculated as the difference between the WarmingCESM and CTRLCESM experiments (see Methods).

Extended Data Table 1 Description of the AGCM experiments
Full size table
Extended Data Table 2 Summary of the wind stress-prescribed CGCM experiments
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Peng, Q., Xie, SP., Miyamoto, A. et al. Strong 2023–2024 El Niño generated by ocean dynamics. Nat. Geosci. 18, 471–478 (2025). https://doi.org/10.1038/s41561-025-01700-9

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