Causality Analysis of ENSO’s Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation
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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
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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.