Understanding the critical instability in the climate of North Atlantic Region using Data Science

A negative (or positive) value of the North Atlantic Oscillation (NAO) index, which measures the variability in sea-level atmospheric pressure, would imply an increase (or decrease) in intense cold air outbreaks and the number of storms in the eastern parts of North America and Northern Europe. The NAO may be influenced by several climate factors. Using a data science approach, here we aim to study the complex dynamics that NAO has with the sea surface temperature (SST) and sea ice extent (SIE), and show that there exists a critical instability (through positive feedback loops) in the complex dynamics of the climate variables of melting Arctic SIE, rising SST, and NAO index. Our statistical machine learning approach shows that the melting SIE and increasing SST significantly affect the NAO, resulting in the changing weather pattern of the North Atlantic region. We also develop a Bayesian Granger-causal dynamic linear model to establish the relationship between the predictor and dependent variable. Our study indicates that there would be a critical instability with more frequent bouts of very cold climate in eastern North America and northern Europe than previously seen, marking a significant climate change.