Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation
| dc.contributor.author | Song, Hua | |
| dc.contributor.author | Tian, Jing | |
| dc.contributor.author | Huang, Jingfeng | |
| dc.contributor.author | Wang, Jianwu | |
| dc.contributor.author | Zhang, Zhibo | |
| dc.date.accessioned | 2020-07-29T17:59:55Z | |
| dc.date.available | 2020-07-29T17:59:55Z | |
| dc.description.abstract | Numerous 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 teleconnections. | en_US |
| dc.description.sponsorship | This 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). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/CT2018Team4.pdf | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2uupu-lpfp | |
| dc.identifier.citation | Hua Song et al., Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation, http://hpcf-files.umbc.edu/research/papers/CT2018Team4.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19275 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.relation.ispartofseries | HPCF–2018–14; | |
| dc.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. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.title | Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation | en_US |
| dc.type | Text | en_US |