A new climate feedback framework (REFRAME)

全球气候变化预测仍然存在很大的不确定性，减少这些不确定性是政策制定的迫切要求。这些不确定性的核心是气候反馈，即响应外部气候强迫（如温室气体）而放大或抑制全球变暖的过程。评估这些反馈的一个主要挑战是它们的不稳定性：当前的反馈不同于未来的反馈，虽然我们只能观察到前者，但我们需要后者来约束未来的气候预测。我认为，气候反馈的明显不稳定是由于当前反馈框架中缺少物理，该框架假设反馈过程仅取决于全球平均表面温度。相反，最近的研究表明，通过低云量和太阳反射的变化，这些反馈也会对地表温度的模式做出反应。因此，REFRAME将引入一个新的反馈框架，定量地解释这种缺失的物理。然后，它将利用这个新的框架来(1)量化气候变率和反馈之间的耦合，而观测表明，这种耦合在气候模式中是不现实的；(2)限制未来气候变化，重点关注强烈放大反馈和高全球变暖水平的风险。REFRAME将利用我之前的工作，阐明表面温度模式和反馈之间的物理联系，从而充分利用我的专业知识。这一努力是及时而新颖的：我最近展示了统计学习在限制云反馈方面的力量，创造了新的机会，通过这个项目将利用的短期观测记录来限制气候变化。通过解决气候动力学理论中的一个基本问题，该项目有可能在政策相关尺度上减少气候预测和碳预算的不确定性，从而影响未来10-20年的气候政策。

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.