Networks in Climate Science
The El Niño is a powerful but irregular climate cycle that has huge consequences for agriculture and perhaps global warming. Predicting its arrival more than 6 months ahead of time has been difficult. A recent paper by Ludescher et al caused a stir by using ideas from network theory to predict the start of an El Niño toward the end of 2014 with a 3-in-4 likelihood. We critically analyze their technique, related applications of network theory, and also attempts to use neural networks to help model the Earth’s climate.
John Baez is a professor of mathematics at U.C. Riverside. Until recently he worked on higher category theory and quantum gravity. His internet column “This Week’s Finds” dates back to to 1993 and is sometimes called the world’s first blog. In 2010, concerned about climate change and the future of the planet, he switched to working on more practical topics and started the Azimuth Project, a collaboration to create a focal point for scientists and engineers interested in environmental issues. One part of this project is developing the mathematics of networks of all kinds: chemical reaction networks, belief networks, signal-flow diagrams, and so on.