Robust machine learning methods for climate research

神经网络等机器学习方法已被引入气候研究，从而更好地理解气候现象的非线性特性，如厄尔Niño-Southern涛动（ENSO）。不幸的是，热带外气候现象，如北极涛动（AO）、太平洋-北美（PNA）遥相关、热带外ENSO效应和太平洋年代际涛动（PDO），由于其弱信噪比和/或相对较短的数据记录，给当前一代机器学习方法带来了很大的困难。提出了新的鲁棒方法，这些方法将显著改善这些气候现象的非线性分析（在某些情况下预测）。这些强大的方法不仅可以用于分析观测数据，还可以用于分析一般环流模式（GCM）气候模拟，从而使我们不仅可以了解气候系统的非线性行为，还可以了解全球变暖情景下非线性行为的变化。

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.