Collaborative Research: P2C2--Fingerprinting Forced and Unforced Variability in Holocene Paleoclimate Record

合作研究：P2C2——全新世古气候记录中受迫和非受迫变异的指纹识别

• 批准号: 2102936
• 负责人: Cristian Proistosescu
• 金额: $ 30.45万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102936&HistoricalAwards=false
• 关键词: Collaborative; Research; P2C2; Fingerprinting; Forced

Distinguishing between natural and forced climate variability has large implications for both interpreting past variations and for making accurate predictions of the future. Climate models and paleoclimate observations disagree about the magnitude of variability at interdecadal-and-longer time scales. This creates uncertainty in estimates of equilibrium climate sensitivity (temperature increase that result from a doubling of atmospheric carbon dioxide concentration) and in model projections of long-term climate variability. The key to reconciling this contradiction lies in understanding the relative importance of forced and unforced variability in the paleoclimate records. If the paleoclimate reconstructions are correct, then the missing physics must be identified, whether it be a lack of sensitivity to external forcing (e.g. volcanic eruption, solar variability, changing concentrations of greenhouse gases and aerosols), or an underrepresentation of internal variability (internal mechanisms within the climate system). This project proposes a framework for fingerprinting forced and unforced climate variability in Holocene paleoclimate records, based on their different spatial and temporal statistics. Specifically, the researchers will develop spatio-temporal fingerprints for forced and unforced climate variability, using a combination of Global Circulation Models (GCMs) simulations and fundamental physical principles, and apply them to the multiproxy paleoclimate record of Holocene climate variability. The researchers will build the fingerprints starting from GCMs, which provide a complete representation of the climate system and have the ability to disaggregate forced and unforced variability. The proxy-system models (forward modelling of mechanistic processes either biological or chemical, by which climate variability is recorded in a climate archive such as corals or ice cores) will be used to evaluate how these fingerprints are expressed in proxy records that are sparse, noisy, and can alter the statistics of climate signals. Given well-documented deficiencies in the ability of GCMs to simulate low-frequency variability in both observations and proxies, the GCM-based fingerprints will be complemented with physical-statistical stochastic models.The potential Broader Impacts include a greater understanding of Holocene climate variability and modeled projections by (1) building a single coherent picture of the factors controlling the response of temperature, hydrology, and hydroclimate proxies in the climate system; and (2) improving the physical interpretation of the low-frequency climate variability recorded in paleoclimate records. This research will potentially illuminate both the nature and sources of climate variability over the Holocene, and the physical mechanisms responsible. This has large implications for interpreting the recent observational record, and for inferences of climate projections. The statistical frameworks, model simulations, and simple dynamical models developed in this project will serve as a conceptual framework for climate-related research across the Earth Sciences and over all geologic timescales.The project will provide scientific training and professional development for two undergraduate and two graduate students. The undergraduate students will be involved in the research and its publication as demonstrated by the track record of the researchers. Output from the model simulations, Code for setting up the simulations with the publicly available Community Earth System Model (CESM), and Code for reproducing the diagnostics using the publicly available data and model output will be all made publicly available.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.

区分自然气候变化和强迫气候变化对于解释过去的变化和准确预测未来具有重大意义。气候模型和古气候观测对于年代际和更长时间尺度的变化幅度存在分歧。这给平衡气候敏感性（大气二氧化碳浓度加倍导致温度升高）的估计和长期气候变化的模型预测带来了不确定性。调和这一矛盾的关键在于理解古气候记录中受迫和非受迫变化的相对重要性。如果古气候重建是正确的，那么必须确定缺失的物理现象，无论是对外部强迫缺乏敏感性（例如火山喷发、太阳变率、温室气体和气溶胶浓度的变化），还是对内部变率（内部变率）的代表性不足气候系统内的机制）。该项目提出了一个框架，根据不同的空间和时间统计数据，对全新世古气候记录中的强迫和非强迫气候变化进行指纹识别。具体来说，研究人员将结合全球环流模型（GCM）模拟和基本物理原理，开发强迫和非强迫气候变化的时空指纹，并将其应用于全新世气候变化的多代理古气候记录。研究人员将从 GCM 开始构建指纹，GCM 提供了气候系统的完整表示，并且能够分解受迫和非受迫的变化。代理系统模型（生物或化学机械过程的正向建模，通过该模型将气候变化记录在珊瑚或冰芯等气候档案中）将用于评估这些指纹如何在稀疏的代理记录中表达，嘈杂，并且可以改变气候信号的统计数据。鉴于 GCM 在模拟观测和代理中低频变化的能力方面存在有据可查的缺陷，基于 GCM 的指纹将得到物理统计随机模型的补充。潜在的更广泛影响包括对全新世气候变化和通过以下方式进行建模预测：(1) 构建控制气候系统中温度、水文和水文气候代理响应的因素的单一连贯图景； (2) 改进对古气候记录中记录的低频气候变率的物理解释。这项研究将有可能阐明全新世气候变化的性质和来源，以及造成这种变化的物理机制。这对于解释最近的观测记录以及气候预测的推论具有重大意义。该项目开发的统计框架、模型模拟和简单的动力学模型将作为整个地球科学和所有地质时间尺度的气候相关研究的概念框架。该项目将为两名本科生和两名学生提供科学培训和专业发展。研究生。正如研究人员的记录所证明的那样，本科生将参与研究及其出版。模型模拟的输出、使用公开的社区地球系统模型 (CESM) 设置模拟的代码以及使用公开的数据和模型输出重现诊断的代码都将公开。该奖项反映了 NSF 的法定要求使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。