CAREER: The State Dependency of Climate Sensitivity during Cenozoic Warm Intervals

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Despite decades of satellite observations and climate model development, the fundamental question of “How much does Earth’s surface warm due to a doubling of atmospheric CO2 concentration?” remains unanswered. Constraining this critical climate metric, known as Equilibrium Climate Sensitivity (ECS), is important for climate science and for robust decision making based on climate models. Past climate data and simulations are instrumental to estimating ECS. Yet, ECS likely changes with different climate background states. This state-dependency must be accounted for when deriving future-climate relevant ECS from past climate data and simulations. In addition to CO2 driver, climate is also altered by perturbations of the earth energy balance from changes in geography, topography, vegetation, and ice sheets, which modify climate background states, and consequently, ECS. This project aims to identify the physical mechanisms driving the state-dependency of ECS and quantify the role of non-CO2 drivers in altering climate and ECS. This will be achieved by combining existing model simulation, proxy data, new simulations, and statistical diagnostic tools for three past warm intervals (3.3 – 3.0; ~16.9– 14.7 and ~50 million years) than can serve as analogous to future climate states. Concurrently, a series of education and outreach activities aimed at advancing climate education for students and the public are proposed. These activities include developing new teaching materials for a high school enrichment course on climate modeling, creating undergraduate and graduate research opportunities, and facilitating the sharing of knowledge within academia and between academia and the public through a workshop and a virtual seminar series.By developing a non-linear framework to analyze paleoclimate model simulations of three well-studied Cenozoic warm intervals: the mid-Piacenzian (3.3 – 3.0 Ma), mid-Miocene (~16.9– 14.7 Ma), and early-Eocene (~50 Ma) -- to 1), this project will transform our understanding of ECS by quantifying feedbacks that are responsible for the climate-state dependency of ECS and the roles of non-CO2 forcings in modulating ECS. The selected warm intervals bear many similarities to future climate, and therefore, are particularly useful for studying feedbacks that are relevant to climate projections. Furthermore, by combining multi-model simulations and proxy data within a state-of-the-art Bayesian framework, this project will develop proxy constraints for the state dependency of ECS and assess model skill. This line of inquiry is particularly timely as researchers strive to narrow the uncertainty of ECS of today’s climate. More generally, this research will provide new insights into the feedbacks that maintain greenhouse and icehouse climates. The proposed Bayesian method will also allow for the identification of regions where proxy data are particularly influential for constraining the ECS, which will help guide future data collection efforts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.