Valerio Lucarini – University of Reading
Wednesday, July 26, 10:45AM EDT (Zoom meeting starts at 10:30 EDT)
Statistical Mechanical Foundations of the Detection and Attribution of Climate Change
Detection and attribution studies have played a major role in shaping contemporary climate science and have provided key motivations supporting global climate policy negotiations. The goal of such studies is to associate observed patterns of climate change with acting forcings - both anthropogenic and natural ones - with the goal of making statements on the acting drivers of climate change. The statistical inference is usually performed using regression methods referred to as optimal fingerprinting . We show here how linear response theory for nonequilibrium systems provides the physical and mathematical foundations behind the statistical optimal fingerprinting approach for climate change detection and attribution. This allows one to clearly frame assumptions, strengths and potential pitfalls of the method. We revisit the notion of causality, criticality assess the computation of the fingerprints, and propose a novel definition of the natural variability based on the concepts of snapshot attractor. Special insight is gained by considering an explicit representation of the response operators obtained by considering the spectral properties of the Kolmogorov operator describing the evolution of observables. Finally, we are able to clarify the mathematical framework behind the degenerate fingerprinting method that is used for defining early warning indicators of critical transitions in the climate system, the well-known tipping points.