Robust machine learning methods for climate research

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Machine learning methods such as neural networks have been introduced to climate research, leading to a better understanding of the nonlinear properties of climate phenomena, such as the El Niño-Southern Oscillation (ENSO). Unfortunately, extra-tropical climate phenomena such the Arctic Oscillation (AO), the Pacific-North American (PNA) teleconnection, extra-tropical ENSO effects, and the Pacific Decadal Oscillation (PDO), with their weak signal-to-noise ratio and/or relatively short data records, pose great difficulties for the current generation of machine learning methods. New robust methods are proposed which should significantly improve on the nonlinear analysis (and in some cases prediction) of these climate phenomena. These robust methods will be useful in analyzing not only observed data, but also the general circulation model (GCM) climate simulations, thereby allowing us to understand not only the nonlinear behaviour of our climate system, but also how the nonlinear behaviour changes under global warming scenarios.