Climate models today disagree on how much the planet will warm, and a big reason is that they miss a key piece of physics: the way clouds respond to shifting patterns of surface temperature, not just the global average. Current climate feedback theory assumes that processes like cloud formation depend only on how hot the world is overall. But recent work shows that low clouds also change when the *pattern* of warming shifts—for instance, when the tropics heat up differently than the poles. This alters how much sunlight the planet reflects, which can either amplify or dampen warming. Because models get these patterns wrong, their feedbacks drift unrealistically over time, making long-term projections unreliable. REFRAME will build a new mathematical framework that explicitly accounts for this pattern dependence. The team will then use it to test whether climate models produce realistic feedbacks during natural variability, and to narrow the range of possible future warming—especially the risk of strongly amplifying feedbacks that could push the planet toward high warming levels. This is fundamental science aimed at fixing a core theoretical gap in climate dynamics. If successful, it could reduce uncertainties in carbon budgets and climate projections that directly inform UK and international policy over the next two decades.
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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.
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