Detecting and Attributing Change in Climate and Complex Systems: Foundations, Green's Functions, and Nonlinear Fingerprints
arXiv:2212.02628 · doi:10.1103/PhysRevLett.133.244201
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.
11 pages, 3 figures, Final accepted version by PRL
References in corpus (10)
- Fluctuation-Dissipation: Response Theory in Statistical Physics
- Applied Koopmanism
- A review of linear response theory for general differentiable dynamical systems
- Climate dynamics and fluid mechanics: Natural variability and related uncertainties
- Computation of extreme heat waves in climate models using a large deviation algorithm
- Data-driven non-Markovian closure models
- Quantification and interpretation of the climate variability record
- Theoretical tools for understanding the climate crisis from Hasselmann's program and beyond
- On Some Aspects of the Response to Stochastic and Deterministic Forcings
- Projections of the Transient State-Dependency of Climate Feedbacks