Cargando…

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

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 invest...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Yiyi, Kleindessner, Matthäus, Munishkin, Alexey, Varshney, Debvrat, Guo, Pei, Wang, Jianwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421796/
https://www.ncbi.nlm.nih.gov/pubmed/34505056
http://dx.doi.org/10.3389/fdata.2021.642182
_version_ 1783749162201776128
author Huang, Yiyi
Kleindessner, Matthäus
Munishkin, Alexey
Varshney, Debvrat
Guo, Pei
Wang, Jianwu
author_facet Huang, Yiyi
Kleindessner, Matthäus
Munishkin, Alexey
Varshney, Debvrat
Guo, Pei
Wang, Jianwu
author_sort Huang, Yiyi
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8421796
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84217962021-09-08 Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere Huang, Yiyi Kleindessner, Matthäus Munishkin, Alexey Varshney, Debvrat Guo, Pei Wang, Jianwu Front Big Data Big Data 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. Frontiers Media S.A. 2021-08-24 /pmc/articles/PMC8421796/ /pubmed/34505056 http://dx.doi.org/10.3389/fdata.2021.642182 Text en Copyright © 2021 Huang, Kleindessner, Munishkin, Varshney, Guo and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Huang, Yiyi
Kleindessner, Matthäus
Munishkin, Alexey
Varshney, Debvrat
Guo, Pei
Wang, Jianwu
Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title_full Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title_fullStr Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title_full_unstemmed Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title_short Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere
title_sort benchmarking of data-driven causality discovery approaches in the interactions of arctic sea ice and atmosphere
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421796/
https://www.ncbi.nlm.nih.gov/pubmed/34505056
http://dx.doi.org/10.3389/fdata.2021.642182
work_keys_str_mv AT huangyiyi benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere
AT kleindessnermatthaus benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere
AT munishkinalexey benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere
AT varshneydebvrat benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere
AT guopei benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere
AT wangjianwu benchmarkingofdatadrivencausalitydiscoveryapproachesintheinteractionsofarcticseaiceandatmosphere