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.

尽管经过了几十年的卫星观测和气候模型的开发，但“由于大气中二氧化碳浓度翻倍，地球表面变暖了多少？”这一根本问题仍然存在。仍无人接听。约束这一被称为平衡气候敏感性(ECS)的关键气候指标，对于气候科学和基于气候模型的稳健决策非常重要。过去的气候数据和模拟有助于估计ECS。然而，ECS可能会随着不同的气候背景状态而变化。在从过去的气候数据和模拟中得出与未来气候相关的ECS时，必须考虑到这种状态依赖性。除了二氧化碳驱动因素外，由于地理、地形、植被和冰盖的变化，地球能量平衡的扰动也改变了气候，从而改变了气候背景状态，从而改变了ECS。该项目旨在确定推动环境影响的物理机制，并量化非二氧化碳驱动因素在改变气候和环境影响方面的作用。这将通过结合现有的模型模拟、代理数据、新的模拟和过去三个暖期(3.3-3.0年、~16.9-14.7年和~5000万年)的统计诊断工具来实现，这些间隔期可以用作未来气候状态的类似物。同时，还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。这些活动包括为高中气候模型强化课程开发新的教材，创造本科生和研究生的研究机会，并通过研讨会和虚拟研讨会系列促进学术界和学术界与公众之间的知识共享。通过开发一个非线性框架来分析三个研究较好的新生代温暖时段：皮亚森中期(3.3-3.0 Ma)、中中新世(~16.9-14.7 Ma)和早始新世(~50 Ma)的古气候模式模拟。该项目将通过量化与环境影响有关的反馈，以及非二氧化碳作用力在调节环境影响方面的作用，来改变我们对环境影响的理解。选定的温暖间隔与未来气候有许多相似之处，因此，对于研究与气候预测相关的反馈特别有用。此外，通过在最先进的贝叶斯框架内结合多模型模拟和代理数据，该项目将为ECS的状态依赖开发代理约束并评估模型技能。在研究人员努力缩小当今气候的ECS不确定性之际，这一调查路线尤其及时。更广泛地说，这项研究将为维持温室和冰库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约束ECS特别有影响力的地区，这将有助于指导未来的数据收集工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。