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Collaborative Research: Improved Characterization of Internal Decadal-Multidecadal Climate Variability Using Paleoclimate Archives, Observational Climate Data and Model Simulations

合作研究：利用古气候档案、观测气候数据和模型模拟改进内部十年-数十年气候变化的表征

基本信息

批准号：
1748115
负责人：
Byron Steinman
金额：
$ 21.1万
依托单位：
University of Minnesota Duluth
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748115&HistoricalAwards=false
关键词：
Collaborative Research Improved Characterization Internal

项目摘要

This project aims to build upon past research investigating interdecadal and multidecadal climate oscillations through the analysis of expanded paleoclimate proxy data, updated instrumental data, and extensive multi-model simulation archives that have recently become available. The complementary set of proposed analyses may provide a more comprehensive understanding of the nature of internal climate variability, allowing assessment of whether there are distinct modes of internal variability on decadal and longer timescales that (a) are oscillatory in nature, as opposed to being simply part of the red noise spectral continuum, (b) persist back in time based on evidence from long-term paleoclimate data and (c) are consistent, in both timescale and spatial pattern, with modes of variability identified in long, state-of-the-art model control simulations. Such analyses could inform assessments of the potential for decadal and longer-term climate predictability.The methodology will principally focus on application of the multi-taper method singular value decomposition (MTM-SVD) to detect and characterize narrowband spatially-coherent signals in spatiotemporal instrumental, proxy, and model-generated climate datasets. The MTM-SVD methodology will first be applied to up-to-date global surface temperature data dating back through the mid-19th century to reevaluate the observational evidence for oscillatory spatiotemporal modes of decadal-to-multidecadal climate variability and to reconstruct the time-evolving patterns of the associated signals. The signals will be projected onto other fields (sea level pressure, sub-surface ocean heat content and circulation, and upper-atmosphere data) to obtain a more comprehensive view of the associated ocean-atmosphere dynamics. The next step will be to analyze paleoclimate proxy records spanning the past millennium to establish the long-term robustness and persistence of signals and to address potential changes in the character of signals during the transition into the anthropogenic era. These analyses will build upon past frequency-domain analyses of global climate proxy data by employing the considerably more extensive paleoclimate data archives now available spanning the past millennium. A further mechanistic understanding will be sought through parallel analysis of the (a) control, (b) last millennium, (c) historical and (d/e) RCP 4.5/8.5 future projection experiments from the Coupled Model Inter-Comparison Project Phase 5 (CMIP5) and CMIP6 projects. These comparisons will assess whether consistent evidence exists across a diverse selection of models for spatiotemporal oscillatory climate signals with similar timescale and spatial characteristics to those isolated in the observations and paleoclimate data. Comparisons of control, last millennium, historical, and projected future simulations will allow assessment of whether and how changes in forcing impact or interact with the characteristics of the internal variability.The complementary set of proposed analyses should provide a more comprehensive understanding of the nature of internal climate variability, allowing assessment of whether there are distinct modes of internal variability on decadal and longer timescales that (a) are oscillatory in nature, as opposed to being simply part of the red noise spectral continuum, (b) persist back in time based on evidence from long-term paleoclimate data and (c) are consistent, in both timescale and spatial pattern, with modes of variability identified in long, state-of-the-art model control simulations. Such analyses will, furthermore, inform assessments of the potential for decadal and longer-term climate predictability.The potential Broader Impacts include a more definitive assessment of evidence for narrowband interdecadal and multidecadal climate signals to improve decadal timescale forecasting. More robust predictive skill in decadal and longer-term climate forecasting and a better physical understanding of the underlying mechanisms, or origin of that skill, could aid an array of stakeholders, including the public at large, benefit from improved climate forecasts. The project will also support an early career scientist and graduate students.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.

该项目旨在通过分析扩展的古气候代用数据，更新的仪器数据和最近可用的广泛的多模式模拟档案，建立在过去的研究调查年代际和年代际气候振荡的基础上。所提议的一套补充分析可使人们更全面地了解内部气候变率的性质，从而能够评估在十年期和较长时间尺度上是否存在不同的内部变率模式，这些模式（a）具有振荡性质，而不仅仅是红噪声频谱连续统的一部分，（B）基于长期古气候数据的证据，在时间上持续存在，（c）在时间尺度和空间模式上与长期、最先进的模型控制模拟中确定的变异模式一致。这种分析可以为评估十年期和长期气候可预测性的潜力提供信息，方法将主要侧重于应用多锥度方法奇异值分解（MTM-SVD），以检测和表征时空仪器、代用品和模式生成的气候数据集中的窄带空间相干信号。 MTM-SVD方法将首先应用于最新的全球地表温度数据，可追溯到19世纪中期，重新评估十年到几十年气候变率振荡时空模式的观测证据，并重建相关信号的时间演变模式。这些信号将投射到其他领域（海平面压力、次表层海洋热含量和环流以及高层大气数据），以更全面地了解相关的海洋-大气动态。下一步将是分析过去一千年的古气候代用记录，以确定信号的长期稳健性和持久性，并解决在向人类时代过渡期间信号特征的潜在变化。这些分析将建立在过去全球气候代用数据的频域分析的基础上，采用目前可获得的跨越上一个千年的相当广泛的古气候数据档案。通过对耦合模型相互比较项目第5阶段（CMIP 5）和CMIP 6项目的（a）控制、（B）上个千年、（c）历史和（d/e）RCP 4.5/8.5未来预测实验的平行分析，将寻求进一步的机理理解。这些比较将评估是否存在一致的证据，在不同的选择模型的时空振荡气候信号具有类似的时间尺度和空间特征的观察和古气候数据中孤立的。对照、上个千年、历史和预测未来模拟的比较将有助于评估强迫变化是否以及如何影响内部变率的特征或与内部变率的特征相互作用。拟议的一组补充分析应提供对内部气候变率性质的更全面的了解，允许评估在十年和更长时间尺度上是否存在不同的内部变率模式，这些模式（a）本质上是振荡的，与仅仅是红噪声光谱连续体的一部分相反，（B）基于来自长期古气候数据的证据而在时间上持续存在，以及（c）在时间尺度和空间模式上与在长期的、最先进的模型控制模拟中识别的变化模式一致。这些分析还将为评估十年期和长期气候可预测性的潜力提供信息，潜在的更广泛影响包括更明确地评估窄带十年期和多年期气候信号的证据，以改进十年期时间尺度的预测。在十年期和长期气候预测方面，更强有力的预测技能以及对基本机制或这种技能的来源有更好的实际了解，可以帮助包括广大公众在内的一系列利益攸关方从改进的气候预测中受益。该奖项反映了NSF的法定使命，并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，被认为值得支持。