Forecasting the magnitude and onset of El Nino based on climate network
arXiv:1703.09138 · doi:10.1088/1367-2630/aabb25
Abstract
El Nino is probably the most influential climate phenomenon on interannual time scales. It affects the global climate system and is associated with natural disasters and serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Nino are still not accurate, at least more than half a year in advance. Here, we introduce a new forecasting index based on network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the El Nino 3.4 region. We find that significant upward trends and peaks in this index forecast with high accuracy both the onset and magnitude of El Nino approximately 1 year ahead. The forecasting procedure we developed improves in particular the prediction of the magnitude of El Nino and is validated based on several, up to more than a century long, datasets.
References in corpus (4)
Cited by in corpus (10)
- Social physics
- Statistical physics approaches to the complex Earth system
- Complexity based approach for El Nino magnitude forecasting before the "spring predictability barrier"
- Climate network percolation reveals the expansion and weakening of the tropical component under global warming
- Spatiotemporal data analysis with chronological networks
- Percolation Framework of the Earth's Topography
- Eigen Microstates and Their Evolution of Global Ozone at Different Geopotential Heights
- From spatio-temporal data to chronological networks: An application to wildfire analysis
- Evaluation of the Real-time El Niño Forecasts by the Climate Network Approach between 2011 and Present
- Dynamic Community Detection into Analyzing of Wildfires Events