A data-driven framework for dimensionality reduction and causal inference in climate fields

Abstract

We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of regionally constrained patterns. Time-dependent causal links are then inferred in the interventional sense through the fluctuation-response formalism, as shown in Baldovin et al. (2020). These two steps allow to explore how regional climate variability can influence remote locations. To distinguish between true and spurious responses, we propose a novel analytical null model for the fluctuation-dissipation relation, therefore allowing for uncertainty estimation at a given confidence level. Finally, we select a set of metrics to summarize the results, offering a useful and simplified approach to explore climate dynamics. We showcase the methodology on the monthly sea surface temperature field at global scale. We demonstrate the usefulness of the proposed framework by studying few individual links as well as "link maps", visualizing the cumulative degree of causation between a given region and the whole system. Finally, each pattern is ranked in terms of its "causal strength", quantifying its relative ability to influence the system's dynamics. We argue that the methodology allows to explore and characterize causal relationships in high-dimensional spatiotemporal fields in a rigorous and interpretable way.

References in corpus (9)

• Maps of random walks on complex networks reveal community structure
• An information-theoretic framework for resolving community structure in complex networks
• Fluctuation-Dissipation: Response Theory in Statistical Physics
• Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
• The backbone of the climate network
• A review of linear response theory for general differentiable dynamical systems
• Quantification and interpretation of the climate variability record
• Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data
• Extracting causation from millennial-scale climate fluctuations in the last 800 kyr

Cited by in corpus (2)

• Conceptual and practical approaches for investigating irreversible processes
• Assimilative Causal Inference