Quantitative assessment of drivers of recent global temperature variability: an information theoretic approach

Abstract

Identification and quantification of possible drivers of recent global temperature variability remains a challenging task. This important issue is addressed adopting a non-parametric information theory technique, the Transfer Entropy and its normalized variant. It distinctly quantifies actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs, unlike correlation, mutual information etc. Measurements of greenhouse gases: CO 2, CH 4 and N 2 O; volcanic aerosols; solar activity: UV radiation, total solar irradiance (TSI) and cosmic ray flux (CR); El Niño Southern Oscillation (ENSO) and Global Mean Temperature Anomaly (GMTA) made during 1984–2005 are utilized to distinguish driving and responding signals of global temperature variability. Estimates of their relative contributions reveal that CO 2 (∼24%), CH 4 (∼19%) and volcanic aerosols (∼23%) are the primary contributors to the observed variations in GMTA. While, UV (∼9%) and ENSO (∼12%) act as secondary drivers of variations in the GMTA, the remaining play a marginal role in the observed recent global temperature variability. Interestingly, ENSO and GMTA mutually drive each other at varied time lags. This study assists future modelling efforts in climate science