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

职业生涯：新生代温暖时期气候敏感性的状态依赖性

• 批准号: 2238875
• 负责人: Ran Feng
• 金额: $ 71.6万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238875&HistoricalAwards=false
• 关键词: CAREER; State; Dependency; Climate; Sensitivity

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.

尽管数十年来的卫星观测和气候模型发展，但“由于大气中的二氧化碳浓度增加了一倍，地球表面表面变暖了多少？”仍然没有得到答复。限制这种关键的气候度量，称为平衡气候灵敏度（ECS）对于气候科学和基于气候模型的强大决策很重要。过去的气候数据和仿真对估计ECS有用。但是，EC像不同的气候背景状态一样变化。从过去的气候数据和仿真中获取未来气候相关的EC时，必须考虑这种状态依赖性。除二氧化碳驱动器外，气候还会因地理，地形，植被和冰盖的变化而改变地球能量平衡，从而改变气候背景状态，从而改变了ECS。该项目旨在确定推动EC的状态依赖性的物理机制，并量化非CO2驱动因素在改变气候和EC中的作用。这将通过将现有模型模拟，代理数据，新模拟和统计诊断工具结合起来，以实现三个过去的温暖间隔（3.3 - 3.0; 〜16.9-14.7和〜5000万年）来实现。同时，提出了一系列旨在推进学生和公众气候教育的教育和外展活动。这些活动包括开发新的教学材料，以通过研讨会和虚拟的SEMIAR系列和一个虚拟的半段系列来开发学术界以及学术界和公众之间的知识共享，并支持非线性框架，以开发一个非线性框架来分析三个良好的cenozoic Inters – 3. 3. 3. 3. 3. 3. 3. 3.中新世中期（〜16.9–14.7 Ma）和欧新新世（〜50 mA） - 至1），该项目将通过量化负责EC的气候依赖性的反馈以及非CO2强迫在调制ECS中的气候依赖性的反馈来改变我们对EC的理解。所选的温暖间隔与未来的气候具有许多相似之处，因此对于研究与气候预测相关的反馈特别有用。此外，通过将多模型仿真和代理数据结合在最先进的贝叶斯框架中，该项目将开发出对ECS的状态依赖性和评估模型技能的代理约束。当研究人员努力缩小当今气候的EC不确定性时，这种询问尤其及时。更一般而言，这项研究将为维持温室和冰屋气候的反馈提供新的见解。拟议的贝叶斯方法还将允许识别代理数据对限制EC特别有影响力的区域，这将有助于指导未来的数据收集工作。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准来评估通过评估来获得的支持。