%0 Journal Article %1 meng2019complexitybased %A Meng, Jun %A Fan, Jingfang %A Ludescher, Josef %A Agarwal, Ankit %A Chen, Xiaosong %A Bunde, Armin %A Kurths, Jürgen %A Schellnhuber, Hans Joachim %D 2019 %I Proceedings of the National Academy of Sciences %J Proceedings of the National Academy of Sciences %K ENSO decadal seasonal skill statistics %N 1 %P 177--183 %R 10.1073/pnas.1917007117 %T Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier %U https://doi.org/10.1073/pnas.1917007117 %V 117 %X Although El Niño events characterized by anomalous episodic warmings of the eastern equatorial Pacific can trigger disasters in various parts of the globe, reliable forecasts of their magnitude are still limited to about 6 mo ahead. A significant extension of this prewarning time would be instrumental for mitigating some of the worst damages. Here we introduce an approach relying on information entropy, which achieves some doubling of the prewarning time. The approach is based on our finding that the entropy in one calendar year exhibits a strong correlation with the magnitude of an El Niño that starts in the following year and thus allows us to forecast the onset and the magnitude of an El Niño event 1 y in advance.

@article{meng2019complexitybased, abstract = {Although El Niño events characterized by anomalous episodic warmings of the eastern equatorial Pacific can trigger disasters in various parts of the globe, reliable forecasts of their magnitude are still limited to about 6 mo ahead. A significant extension of this prewarning time would be instrumental for mitigating some of the worst damages. Here we introduce an approach relying on information entropy, which achieves some doubling of the prewarning time. The approach is based on our finding that the entropy in one calendar year exhibits a strong correlation with the magnitude of an El Niño that starts in the following year and thus allows us to forecast the onset and the magnitude of an El Niño event 1 y in advance.}, added-at = {2020-08-13T10:06:28.000+0200}, author = {Meng, Jun and Fan, Jingfang and Ludescher, Josef and Agarwal, Ankit and Chen, Xiaosong and Bunde, Armin and Kurths, Jürgen and Schellnhuber, Hans Joachim}, biburl = {https://www.bibsonomy.org/bibtex/2b8848f1392a316936a3e7bdc27ce50fa/pbett}, doi = {10.1073/pnas.1917007117}, interhash = {9c60c31e3c8d68536184e995ca3d16d7}, intrahash = {b8848f1392a316936a3e7bdc27ce50fa}, journal = {Proceedings of the National Academy of Sciences}, keywords = {ENSO decadal seasonal skill statistics}, month = dec, number = 1, pages = {177--183}, publisher = {Proceedings of the National Academy of Sciences}, timestamp = {2020-08-13T10:06:28.000+0200}, title = {Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier}, url = {https://doi.org/10.1073/pnas.1917007117}, volume = 117, year = 2019 }