Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
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Date
2021-08-24
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Citation of Original Publication
Huang Y, Kleindessner M, Munishkin A, Varshney D, Guo P and Wang J (2021) Benchmarking of DataDriven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere. Front. Big Data 4:642182. doi: 10.3389/fdata.2021.642182
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
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 Noncombinatorial 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.