Journal article

Publication year: 2024

Pages: 6727-6736

Journal: Theoretical and Applied Climatology

Volume number: 155

Issue number: 7

ISSN: 0177-798X

eISSN: 1434-4483

Open access status: Hybrid

DOI Link: https://doi.org/10.1007/s00704-024-05035-0

Publisher: Springer

Abstract: 
El Ni & ntilde;o episodes are part of the El Ni & ntilde;o-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Ni & ntilde;o events about 1 year ahead. Here we evaluate the real-time forecasts of this approach between 2011 and 2022. We find that the approach correctly predicted (in 2013 and 2017) the onset of both El Ni & ntilde;o periods (2014-2016 and 2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach correctly forecasted the onset of an El Ni & ntilde;o event in 2023. For determining the p-value of the 12 real-time forecasts, we consider 2 null hypotheses: (a) random guessing where we assume that El Ni & ntilde;o onsets occur randomly, and (b) correlated guessing where we assume that in the year an El Ni & ntilde;o ends, no new El Ni & ntilde;o will start. We find p a congruent to 0.005 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_a\cong 0.005$$\end{document} and p b congruent to 0.015 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_b\cong 0.015$$\end{document} , this way rejecting both the null hypotheses that the same quality of the forecast can be obtained by chance. We also discuss how the network algorithm can be further improved by systematically reducing the number of false alarms. For 2024, the method indicates the absence of a new El Ni & ntilde;o event.

Harvard Citation style: Bunde, A., Ludescher, J. and Schellnhuber, H. (2024) Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present, Theoretical and Applied Climatology, 155(7), pp. 6727-6736. https://doi.org/10.1007/s00704-024-05035-0

APA Citation style: Bunde, A., Ludescher, J., & Schellnhuber, H. (2024). Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present. Theoretical and Applied Climatology. 155(7), 6727-6736. https://doi.org/10.1007/s00704-024-05035-0