Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere ...

• Main Authors: Huang, Yiyi, Kleindessner, Matthäus, Munishkin, Alexey, Varshney, Debvrat, Guo, Pei, Wang, Jianwu
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers 2021
• Subjects: UMBC Big Data Analytics Lab; Arctic; Sea ice
• Online Access: https://dx.doi.org/10.13016/m2erjo-fmbu; https://mdsoar.org/handle/11603/25888

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 ...