Prospects for estimating Transient Climate Response to Greenhouse Gases using the Fluctuation Dissipation Theorem

One of the core metrics for climate change is the steady-state increase in global surface air temperature with doubled concentrations of CO₂ known as the Equilibrium Climate Sensitivity (ECS).  Best estimates of ECS remain uncertain to factors of O(3) despite intensive research since the first comprehensive assessment of global warming due to CO₂ over forty years ago (Charney et al., NAP, 1979).  The large range in ECS propagates into projections of the future physical state of the climate system, and it introduces considerable incertitude into policy responses designed to mitigate global warming.  The uncertainty in ECS stems largely from multiscale and multiphysics feedbacks introduced by components of the climate system, especially by clouds and the cryosphere, for which we lack first-principles theories.

 

In this talk, we discuss the prospects for estimating a related metric, the Transient Climate Response (TCR), using the Fluctuation Dissipation Theorem (FDT). TCR is the global mean temperature change under a hypothetical 1%/year increase of CO₂ at the time when atmospheric CO₂ concentrations have doubled.  TCR and ECS are closely related through the First Law of Thermodynamics.  Following concepts first advanced by Goody et al. (1998), we explore whether TCR could be reliably estimated by applying the FDT to satellite observations of the Earth’s spectral radiation.  The forcing, response, and feedbacks of the climate system can be readily detected and attributed in these spectra, and equator-to-pole gradients in the spectra govern the primary energy transports in the climate system.  This estimation process can be tested and validated using a multimodel ensemble of climate simulations recently assessed by the Intergovernmental Panel on Climate Change.  Recent theoretical developments have eliminated questionable assumptions (e.g., Gaussianity) that have compromised prior attempts to apply FDT to climate.  We show how these developments will reduce uncertainties in the estimation process and discuss the impact of this reduction on climate science going forward.