Causal Feedback Discovery using Convergence Cross Mapping from Sea Ice Data

Abstract

The Arctic region is experiencing accelerated warming, largely driven by complex and nonlinear interactions among time series atmospheric variables such as, sea ice extent, short-wave radiation, temperature, and humidity. These interactions significantly alter sea ice dynamics and atmospheric conditions, leading to increased sea ice loss. This loss further intensifies Arctic amplification and disrupts weather patterns through various feedback mechanisms. Although stochastic methods such as Granger causality, PCMCI, and VarLiNGAM estimate causal interactions among atmospheric variables, they are limited to unidirectional causal relationships and often miss weak causal interactions and feedback loops in nonlinear settings. In this study, we show that Convergent Cross Mapping (CCM) can effectively estimate nonlinear causal coupling, identify weak interactions and causal feedback loops among atmospheric variables. CCM employs state space reconstruction (SSR) which makes it suitable for complex nonlinear dynamic systems. While CCM has been successfully applied to a diverse range of systems, including fisheries and online social networks, its application in climate science is under-explored. Our results show that CCM effectively uncovers strong nonlinear causal feedback loops and weak causal interactions often overlooked by stochastic methods in complex nonlinear dynamic atmospheric systems.