Hybrid Causality Analysis of ENSO’s Global Impacts on Climate Variables Based on Data-Driven Analytics and Climate Model Simulation

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dc.contributor.authorSong, Hua
dc.contributor.authorTian, Jing
dc.contributor.authorHuang, Jingfeng
dc.contributor.authorGuo, Pei
dc.contributor.authorZhang, Zhibo
dc.contributor.authorWang, Jianwu
dc.date.accessioned2019-10-14T14:33:06Z
dc.date.available2019-10-14T14:33:06Z
dc.date.issued2019-09-18
dc.description.abstractNumerous studies have indicated that El Niño and the southern oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however, cannot identify the cause-and-effect of such linkage, and ultimately determine a direction of causality. This study employs the vector auto-regressive (VAR) model estimation method with the long-term observational sea surface temperature (SST) data and the NCEP/NCAR reanalysis data to demonstrate the Granger causality between ENSO and other climate attributes. Results showed that ENSO as the modulating factor can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely, not vice versa. We also carry out the global climate model sensitivity simulations using the parallel computing techniques to double confirm the causality relations between ENSO and abnormal events in remote regions. Our statistical and climate model-based analyses may enrich our current understanding on the occurrences of extreme events worldwide caused by different ENSO strengths through teleconnectionsen_US
dc.description.sponsorshipThis work was supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics, and Atmospheric Sciences using Advanced Cyberin-frastructure Resources from the National Science Foundation (Grant No. OAC–1730250). The hardware in the UMBC High Performance Computing Facility (HPCF) was 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). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/feart.2019.00233/fullen_US
dc.format.extent15 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2l9aj-5xei
dc.identifier.citationSong H, Tian J, Huang J, Guo P, Zhang Z and Wang J (2019) Hybrid Causality Analysis of ENSO’s Global Impacts on Climate Variables Based on Data-Driven Analytics and Climate Model Simulation. Front. Earth Sci. 7:233. doi: 10.3389/feart.2019.00233en_US
dc.identifier.urihttps://doi.org/10.3389/feart.2019.00233
dc.identifier.urihttp://hdl.handle.net/11603/15859
dc.language.isoen_USen_US
dc.publisherFrontiersen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.relation.ispartofUMBC Faculty Collection
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.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectcausality analysisen_US
dc.subjectENSOen_US
dc.subjectdata-driven analyticsen_US
dc.subjectclimate model simulationen_US
dc.subjectteleconnectionen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.subjectUMBC Big Data Analytics Lab
dc.titleHybrid Causality Analysis of ENSO’s Global Impacts on Climate Variables Based on Data-Driven Analytics and Climate Model Simulationen_US
dc.typeTexten_US

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