MLCS aims to understand the interactions between different components of the climate system based on present-day and paleoclimatic data sets used along with the output of climate models. Climatic phenomena of interest include the El Niño Southern Oscillation (ENSO), the Global Monsoon (GM), and the Inter-Tropical Convergence Zone (ITCZ). The changes in the state of these climatic systems are consequential to people all around the planet, and they inform socio-economic decisions at all levels of societal organization. The ideas we use to infer hidden structure in climate data fall broadly under the category of Machine Learning (ML) approaches. ML concepts are inherently suited to the task as they are designed to identify, classify, and predict complex patterns.
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