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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

基本信息

批准号：
2102829
负责人：
Gerard Roe
金额：
$ 47.14万
依托单位：
University of Washington
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=2102829&HistoricalAwards=false
关键词：
Collaborative Research P2C2 Fingerprinting Forced

项目摘要

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.

区分自然气候变率和强迫气候变率对于解释过去的变化和准确预测未来都有很大的意义。气候模式和古气候观测不同意在年代际和更长的时间尺度上的变化幅度。这就造成了对平衡气候敏感性（大气中二氧化碳浓度加倍导致的温度升高）的估计和对长期气候变异性的模型预测的不确定性。调和这一矛盾的关键在于理解古气候记录中强迫和非强迫变化的相对重要性。如果古气候重建是正确的，那么缺失的物理必须被识别，无论是对外部强迫缺乏敏感性（例如火山爆发，太阳变化，温室气体和气溶胶浓度的变化），还是对内部变化的代表性不足（气候系统内部机制）。该项目提出了一个框架，指纹强迫和非强迫气候变率在全新世古气候记录的基础上，不同的空间和时间统计。具体而言，研究人员将开发强迫和非强迫气候变化的时空指纹，使用全球环流模型（GCM）模拟和基本物理原理的组合，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代表性，并能够分解强迫和非强迫变化。代理系统模型（生物或化学机械过程的前向模型，通过这种模型，气候变异性被记录在珊瑚或冰芯等气候档案中）将被用来评估这些指纹如何在稀疏、嘈杂并可能改变气候信号统计数据的代理记录中表达。鉴于GCM在模拟观测和替代物中低频变率的能力方面存在着有据可查的缺陷，基于GCM的指纹将与物理统计随机模型相补充。潜在的更广泛影响包括更好地理解全新世气候变率和模型预测，方法是：（1）建立控制温度、水文、气候系统中的水文气候代用指标;（2）改进对古气候记录中低频气候变率的物理解释。这项研究将可能阐明全新世气候变化的性质和来源，以及负责的物理机制。这对解释最近的观测记录和气候预测的推断有很大的影响。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地球科学和所有地质时标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研究生提供科学培训和专业发展。本科生将参与研究及其出版物，如研究人员的跟踪记录所示。模型模拟的输出、使用公共可用的社区地球系统模型（CESM）建立模拟的代码以及使用公共可用的数据和模型输出再现诊断的代码都将公开提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。