Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes

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dc.contributor.authorHussung, Steve
dc.contributor.authorMahmud, Suhail
dc.contributor.authorSampath, Akila
dc.contributor.authorWu, Mengxi
dc.contributor.authorGuo, Pei
dc.contributor.authorWang, Jianwu
dc.date.accessioned2019-12-20T15:39:39Z
dc.date.available2019-12-20T15:39:39Z
dc.date.issued2019
dc.descriptionResearch Assistant: Pei Guo Faculty Mentor: Jianwu Wangen_US
dc.description.abstractIdentification 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.en_US
dc.description.sponsorshipThis work is supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC).en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/CT2019Team2.pdfen_US
dc.format.extent18 pagesen_US
dc.genretechnical reportsen_US
dc.identifierdoi:10.13016/m2arfa-fzkx
dc.identifier.citationHussung, Steve; Mahmud, Suhail; Sampath, Akila; Wu, Mengxi; Guo, Pei; Wang, Jianwu; Evaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modes; http://hpcf-files.umbc.edu/research/papers/CT2019Team2.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/16929
dc.language.isoen_USen_US
dc.publisherHPCF UMBCen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofseriesHPCF Technical Reports;HPCF-2019-12
dc.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.
dc.subjectcausal networksen_US
dc.subjectatmospheric teleconnection patternsen_US
dc.subjectdata-driven causal discovery methodsen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleEvaluation of Data-Driven Causality Discovery Approaches among Dominant Climate Modesen_US
dc.typeTexten_US

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