Last updated: 06/03/26
The NINO-34 stochastic ensemble forecast by Empirical Model Reduction approach (Kondrashov et al. 2005) is based on SST data from January 1950 through May 20256(blue), and ensemble mean (red) predicts strong El-Nino conditions by the end of 2026. The error bars (black) correspond to one standard deviation of the ensemble plume. You can also check a multi-model plume of Nino-34 forecasts from different statistical and dynamical models maintained by IRI, and compare predictions for the past 22 months, including also the UCLA-TCD model. Independent analysis of real-time 2002-2024 forecast skill of IRI multi-model plume of Nino-34 forecasts shows that UCLA-TCD model is highly competitive, see see Barnston et al. (2012, Fig. 6) for skill during the 2002–2011 interval and Zhao et al. (2024, Fig. 2m) for 2002–2022, respectively.
References
- Kondrashov, D., S. Kravtsov, A. W. Robertson and M. Ghil, 2005: A hierarchy of data-based ENSO models. J. Climate, 18, 4425–4444, doi: 10.1175/JCLI3567.1
- 2. Kravtsov S., D. Kondrashov, and M. Ghil, 2005: Multi-level regression modeling of nonlinear processes: Derivation and applications to climatic variability. J. Climate, 18, 4404–4424, doi: 10.1175/JCLI3544.1
- S. Kravtsov, D. Kondrashov and M. Ghil, 2009: Empirical Model Reduction and the Modeling Hierarchy in Climate Dynamics, invited chapter in Stochastic Physics and Climate Modeling, (T. Palmer and P. Williams, Eds.) Cambridge Univ. Press, pp. 35–72.