ArticleProceedings of the National Academy of Sciences of the United States of America2020
Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- El Niño-Southern Oscillation and Diarrhea Risk among Young Children in Low- and Middle-Income Countries.Environmental health perspectives · 2026Article
- Warming-driven rise in soil moisture entropy signals growing instability risk in the Asian Water Tower.Nature communications · 2026Article
- Complexity and entropy of natural patterns.PNAS nexus · 2024Article
- Early warning of the Indian Ocean Dipole using climate network analysis.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Statistical physics approaches to the complex Earth system.Physics reports · 2021Review
- Robustly forecasting maize yields in Tanzania based on climatic predictors.Scientific reports · 2020Article
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8 authors.
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Abstract
The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the "spring predictability barrier" remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year's SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11±0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.
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