Abstract
The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.
Highlights
Warming in the Arctic has been much faster than in the rest of the world in both observations and model simulations, a phenomenon known as the Arctic amplification (Holland and Bitz, 2003; Serreze and Barry, 2011)
We work with the normalized Hamming distance and compare all graphs to the domain knowledge graph (Figure 1)
5.1 Causality Discovery Results Based on Temporal Causality Discovery Framework Approach
Summary
Warming in the Arctic has been much faster than in the rest of the world in both observations and model simulations, a phenomenon known as the Arctic amplification (Holland and Bitz, 2003; Serreze and Barry, 2011). Sea ice decline in turn exerts large influence on the atmosphere This will further alter the climate patterns in both Arctic and mid-latitudes, which results in more frequent extreme weather events (Cohen et al, 2014; Simmonds and Govekar, 2014; Sun et al, 2016; Yao et al, 2017; Luo et al, 2018; Luo et al, 2019a; Luo et al, 2019b). It is vital to analyze both the sea ice retreat’s influence on the atmosphere and vice versa
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