Detecting and Attributing Change in Climate and Complex Systems: Foundations, Green's Functions, and Nonlinear Fingerprints

TL;DR

This paper links the optimal fingerprinting method in climate change detection to response theory for nonequilibrium systems, clarifying its assumptions, extending it to nonlinear regimes, and demonstrating broad applicability.

Contribution

It provides a physical basis for the optimal fingerprinting method using response theory, extends it to nonlinear responses, and explores its application to diverse stochastic systems.

Findings

Response theory underpins the optimal fingerprinting method.

Extension of OFM to nonlinear response regimes.

Application to climate models and potential relevance to other fields.

Abstract

Detection and attribution (D&A) studies are cornerstones of climate science, providing crucial evidence for policy decisions. Their goal is to link observed climate change patterns to anthropogenic and natural drivers via the optimal fingerprinting method (OFM). We show that response theory for nonequilibrium systems offers the physical and dynamical basis for OFM, including the concept of causality used for attribution. Our framework clarifies the method's assumptions, advantages, and potential weaknesses. We use our theory to perform D&A for prototypical climate change experiments performed on an energy balance model and on a low-resolution coupled climate model. We also explain the underpinnings of degenerate fingerprinting, which offers early warning indicators for tipping points. Finally, we extend the OFM to the nonlinear response regime. Our analysis shows that OFM has broad applicability across diverse stochastic systems influenced by time-dependent forcings, with potential relevance to ecosystems, quantitative social sciences, and finance, among others.