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

Hua Song,Pei Guo,Jing Tian,Jianwu Wang,Zhibo Zhang,Jingfeng Huang

doi:10.3389/feart.2019.00233

Abstract

Numerous studies have indicated that El Nino 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.

Highlights

El Niño and the southern oscillation (ENSO) is a local phenomenon of the variation in sea surface temperature (SST) and air pressure across the equatorial eastern Pacific Ocean
We determine the cause-and-effect relation between ENSO and Surface Air Temperature (SAT) on the global scale using the vector auto-regressive (VAR) method for Granger causality model
This indicates SAT changes are Granger caused by ENSO, and ENSO is attributable for SAT anomalies, such as extreme heat or cold events, in remote regions such as South America, northwest North America, equatorial South Africa, and northern Australia

Summary

Introduction

El Niño and the southern oscillation (ENSO) is a local phenomenon of the variation in sea surface temperature (SST) and air pressure across the equatorial eastern Pacific Ocean. Over the past several decades, ENSO has been found as one of the most dominating climate factors that impacts remote weather and climate through the atmospheric “teleconnection” using the conventional correlation-based methods (Gu and Adler, 2011; Mokhov et al, 2011; Kumar et al, 2012). These methods are useful to establish how they are linked or correlated in the spatio-temporal pattern, but cannot identify the cause-andeffect of such linkage and determine a direction of causality. This method may lead to non-accurate results when one or more of the variables have high memory or autocorrelation (Runge et al, 2014; Kretschmer et al, 2016)

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Conclusion