Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes
Research Assistant: Pei Guo Faculty Mentor: Jianwu Wang : Identification of causal networks in atmospheric teleconnection patterns has applications in many climate studies. We evaluate and compare three data-driven causal discovery methods in locating and linking causation of well-known climatic osc...
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ftdatacite:10.13016/m2arfa-fzkx 2023-05-15T17:35:22+02:00 Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes Hussung, Steve Mahmud, Suhail Sampath, Akila Wu, Mengxi Guo, Pei 2019 https://dx.doi.org/10.13016/m2arfa-fzkx http://mdsoar.org/handle/11603/16929 unknown HPCF UMBC This 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. causal networks atmospheric teleconnection patterns data-driven causal discovery methods UMBC High Performance Computing Facility HPCF CreativeWork article 2019 ftdatacite https://doi.org/10.13016/m2arfa-fzkx 2021-11-05T12:55:41Z Research Assistant: Pei Guo Faculty Mentor: Jianwu Wang : Identification of causal networks in atmospheric teleconnection patterns has applications in many climate studies. We evaluate and compare three data-driven causal discovery methods in locating and linking causation of well-known climatic oscillations. Four climate variables in the ERA-Interim reanalysis data (1979-2018) were examined in the study. We first employ dimension reduction to derive the the time-series for selected climate variables. Then timeseries of dominant modes were processed using three different causal discovery methods: Granger causality discovery, Convergent cross-mapping (CCM), and PCMCI. Discovered causal links were different for different methods as well as for different variables. However, slightly similar causal links were observed between the Granger causality and CCM methods. Comparison of these three methods is discussed based on the El Ni˜no-Southern Oscillation (ENSO) and its connection with other oscillations. Causal discovery methods were able to capture the linkage between the ENSO, North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), for some of the variables. Overall, this study identifies the usage of these statistical models in locating the direct and indirect causal links among the oscillations. Application of these data-driven causal discovery methods, both in terms of mediation and direct relationships between the observed teleconnection patterns, suggests that the data-driven statistical methods are efficient in locating the regimes of climate patterns and their 12 observed real connections to some extent. We present and provide our explanation of the evaluation results for each of the three causal discovery methods. Article in Journal/Newspaper North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) Pacific |
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collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
causal networks atmospheric teleconnection patterns data-driven causal discovery methods UMBC High Performance Computing Facility HPCF |
spellingShingle |
causal networks atmospheric teleconnection patterns data-driven causal discovery methods UMBC High Performance Computing Facility HPCF Hussung, Steve Mahmud, Suhail Sampath, Akila Wu, Mengxi Guo, Pei Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
topic_facet |
causal networks atmospheric teleconnection patterns data-driven causal discovery methods UMBC High Performance Computing Facility HPCF |
description |
Research Assistant: Pei Guo Faculty Mentor: Jianwu Wang : Identification of causal networks in atmospheric teleconnection patterns has applications in many climate studies. We evaluate and compare three data-driven causal discovery methods in locating and linking causation of well-known climatic oscillations. Four climate variables in the ERA-Interim reanalysis data (1979-2018) were examined in the study. We first employ dimension reduction to derive the the time-series for selected climate variables. Then timeseries of dominant modes were processed using three different causal discovery methods: Granger causality discovery, Convergent cross-mapping (CCM), and PCMCI. Discovered causal links were different for different methods as well as for different variables. However, slightly similar causal links were observed between the Granger causality and CCM methods. Comparison of these three methods is discussed based on the El Ni˜no-Southern Oscillation (ENSO) and its connection with other oscillations. Causal discovery methods were able to capture the linkage between the ENSO, North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), for some of the variables. Overall, this study identifies the usage of these statistical models in locating the direct and indirect causal links among the oscillations. Application of these data-driven causal discovery methods, both in terms of mediation and direct relationships between the observed teleconnection patterns, suggests that the data-driven statistical methods are efficient in locating the regimes of climate patterns and their 12 observed real connections to some extent. We present and provide our explanation of the evaluation results for each of the three causal discovery methods. |
format |
Article in Journal/Newspaper |
author |
Hussung, Steve Mahmud, Suhail Sampath, Akila Wu, Mengxi Guo, Pei |
author_facet |
Hussung, Steve Mahmud, Suhail Sampath, Akila Wu, Mengxi Guo, Pei |
author_sort |
Hussung, Steve |
title |
Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
title_short |
Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
title_full |
Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
title_fullStr |
Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
title_full_unstemmed |
Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes |
title_sort |
evaluation of data-driven causality discovery approaches among dominant climate modes |
publisher |
HPCF UMBC |
publishDate |
2019 |
url |
https://dx.doi.org/10.13016/m2arfa-fzkx http://mdsoar.org/handle/11603/16929 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_rights |
This 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. |
op_doi |
https://doi.org/10.13016/m2arfa-fzkx |
_version_ |
1766134521523077120 |