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The North Atlantic Oscillation (NAO) index, a measure of sea-levelatmospheric pressure variability, holds significant influence over weatherpatterns in North America and Northern Europe. A negative (positive) NAO valuesignifies increased cold air outbreaks and storm occurrences (reducedoccurrences) in these regions. NAO, a product of multiple climate factors,demonstrates intricate dynamics with sea surface temperature (SST) and sea iceextent (SIE). In this study, we adopt a data-driven approach to explore thecomplex interplay between NAO, SST, and SIE, revealing a critical instabilityrooted in positive feedback loops among these climate variables. Ourstatistical machine learning methodology examines the impacts of melting ArcticSIE and rising SST on NAO, thereby understanding the weather patterns acrossthe North Atlantic region. The skewness analysis yields a negative skewness inNAO across various time intervals--daily, weekly, and monthly. This skewness,coupled with NAO's mean zero stationary nature, accentuates system instability.To capture these dynamics, we formulate a Bayesian Granger-causal dynamiclinear model, which effectively updates the predictor-dependent variablerelationship over time. The findings underscore an impending criticalinstability, indicative of more frequent occurrences of intensely cold climatesin eastern North America and northern Europe, theory signifies a notableclimate shift. By delving into the intricate feedback mechanisms of NAO, SST,and SIE, our study enhances our comprehension of climate variability, fosteringa more informed perspective on the imminent climate changes that lie ahead.

Understanding North Atlantic Climate Instabilities and Complex Interactions using Data Science