Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
Links to Fileshttp://hpcf-files.umbc.edu/research/papers/CT2020Team6.pdf
MetadataShow full item record
Type of Work22 pages
Citation of Original PublicationHuang, Yiyi; Kleindessner, Matth¨aus; Munishkin, Alexey; Varshney, Debvrat; Guo, Pei; Wang, Jianwu; Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences (2020); http://hpcf-files.umbc.edu/research/papers/CT2020Team6.pdf
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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: TCDF, NOTEARS and DAGGNN. We find 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 wind fields are more important for driving sea ice variations. However, both of them produce some unrealistic edges. In comparison, the temporal graphs generated by the three methods are not physically meaningful enough. 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 for us to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.