A new climate feedback framework (REFRAME)

Substantial uncertainties remain in global climate change projections, and reducing these is an urgent requirement for policymaking. At the heart of these uncertainties are climate feedbacks - processes that can amplify or dampen global warming in response to an external climate forcing, e.g. due to greenhouse gases. A major challenge in assessing these feedbacks is their inconstancy: present-day feedbacks differ from those in the future, and while we can only observe the former, we need the latter to constrain future climate projections.

I propose that the apparent inconstancy of climate feedbacks is due to missing physics in the current feedback framework, which assumes feedback processes to solely depend on global-mean surface temperature. Instead, recent work has shown that the feedbacks also respond to patterns of surface temperature, through changes in low cloud amount and hence solar reflection. REFRAME will thus introduce a new feedback framework that quantitatively accounts for this missing physics. It will then exploit this new framework to (1) quantify the coupling between climate variability and the feedbacks, which observations suggest is unrealistic in climate models; and (2) constrain future climate change, focusing on the risk of strongly amplifying feedbacks and high global warming levels.

REFRAME will leverage my prior work elucidating the physical linkages between surface temperature pat- terns and the feedbacks, thus making optimal use of my expertise. The effort is timely and novel: I have recently demonstrated the power of statistical learning to constrain cloud feedback, creating new opportunities to constrain climate change from the short observational record which this project will exploit. By addressing a fundamental problem in the theory of climate dynamics, the project has the potential to reduce uncertainties in climate projections and carbon budgets on policy-relevant scales, thereby impacting climate policy over the next 10-20 years.