Understanding climate dynamics through linear response theory: from causality to the pattern effect

Abstract

We present a data-driven framework for dimensionality reduction and causal inference in climate fields. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of regionally constrained patterns. Causal relations among such patterns are then inferred in the interventional sense through the fluctuation-response formalism. To distinguish between true and spurious responses, we propose an analytical null model for the fluctuation-dissipation relation, therefore allowing us for uncertainty estimation at a given confidence level. The framework is then applied to understand the relation between sea surface temperature warming patterns and changes in the net radiative flux at the top of the atmosphere, the so-called "pattern effect". We present a set of new results on the pattern effect and discuss the role of different processes, active at different spatiotemporal scales, in establishing the causal linkages between warming at the surface and radiat...