EMS Annual Meeting Abstracts
Vol. 21, EMS2024-868, 2024, updated on 05 Jul 2024
https://doi.org/10.5194/ems2024-868
EMS Annual Meeting 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Oral | Tuesday, 03 Sep, 14:30–14:45 (CEST)| Aula Joan Maragall (A111)

Long-Lead ENSO Predictability: the 2023/24 El Niño

Desislava Petrova1, Xavier Rodó1,2, Siem Jan Koopman3, Vassil Tzanov4, and Ivana Cvijanovic1
Desislava Petrova et al.
  • 1Barcelona Institute for Global Health (ISGLOBAL), Climate and Health Group, Barcelona, Spain (desislava.petrova@isglobal.org)
  • 2ICREA, Barcelona, Catalonia, Spain
  • 3Vrije Universiteit, Department of Econometrics, Amsterdam, the Netherlands
  • 4Universitat Rovira I Virgili, the Public University of Tarragona, Department of Inorganic Chemistry, Tarragona, Catalonia, Spain

Long-lead climate predictions are increasingly in demand due to climate change. Through its well-known atmospheric teleconnections El Niño Southern Oscillation (ENSO) is a leading source of seasonal and interannual climate predictability, but currently operational ENSO forecasts are limited to about two seasons in advance. At the same time the scientific literature pointing to the feasibility of ENSO forecasts one year and even more in advance is increasing. The early anticipation of ENSO could prepare vulnerable communities around the world and help mitigate its most devastating impacts such as droughts, floods, poor harvests, and the spread of infectious diseases. Here we showcase real-time forecasts of the recent 2023/24 El Niño at lead times up to 1.5 years in advance of an expected peak in December 2023. We use a previously validated statistical ENSO model that relies on surface and subsurface ocean temperatures and zonal wind stress as predictors, and includes various dynamic components in the form of time-varying cyclical components. Real-time early forecasts with the same model were also issued for the 2015/16 and 2018/19 El Niños, when the long-lead forecasts were also coupled to an impact dengue fever model for the city of Machala in Ecuador to predict the probability of a dengue outbreak in the region up to 11 months in advance. In both cases the dengue predictions were successful indicating an outbreak in 2016 and a low dengue season in 2019. Therefore, such longer-lead ENSO forecasts could directly facilitate decision-making in the health sector, but also other key sectors of society.

How to cite: Petrova, D., Rodó, X., Koopman, S. J., Tzanov, V., and Cvijanovic, I.: Long-Lead ENSO Predictability: the 2023/24 El Niño, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-868, https://doi.org/10.5194/ems2024-868, 2024.