Identifying global patterns of stochasticity and nonlinearity in the Earth System

Abstract

By comparing time-series of surface air temperature (SAT, monthly reanalysis data from NCEP CDAS1 and ERA Interim) with respect to the top-of-atmosphere incoming solar radiation (the insolation), we perform a detailed analysis of the SAT response to solar forcing. By computing the entropy of SAT time-series, we also quantify the degree of stochasticity. We find spatial coherent structures which are characterized by high stochasticity and nearly linear response to solar forcing (the shape of SAT time-series closely follows that of the isolation), or vice versa. The entropy analysis also allows to identify geographical regions in which there are significant differences between the NCEP CDAS1 and ERA Interim datasets, which are due to the presence of extreme values in one dataset but not in the other. Therefore, entropy maps are a valuable tool for anomaly detection and model inter-comparisons.