Collaborative Research: P2C2--Quantifying Holocene Climate Variations through Data Assimilation using Proxies and General Circulation Models (GCMs) Output

合作研究：P2C2——使用代理和大气环流模型（GCM）输出通过数据同化量化全新世气候变化

• 批准号: 1903548
• 负责人: Michael Erb
• 金额: $ 44.19万
• 依托单位: Northern Arizona University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903548&HistoricalAwards=false
• 关键词: Collaborative; Research; P2C2; Quantifying; Holocene

This project aims to develop a proxy-model assimilation framework for the Holocene period by combining proxy data and model simulations. The developed framework will allow to address critical questions about the temperature trends during the Holocene which show a discrepancy between observations and models. In addition, the climate dynamics underlying major hydrological changes-Drying- in western North America and Northern Africa will be investigated. To improve the model-data comparison, the researchers will use a proxy-system modeling approach in order to improve the interpretation of the climate signal recorded in the Holocene proxy-records, including seasonality of temperature and precipitation.The potential Broader Impacts include a greater understanding of the Holocene climate dynamics, investigating the mismatch between model simulations and observation of temperature trends during the Holocene; and major hydroclimate shifts in northwestern US and North Africa. A Holocene multi-variable reconstruction at annual resolution and a curated Holocene proxy-record database will be developed and made available for the broader climate community. Two workshops on paleoclimate data assimilation will be organized for graduate students and early career scientists to train the next generation of paleoclimate scientists. The project will support three early career scientists and a graduate student. The Holocene multi-variables reconstruction and proxy-network will be useful products that will benefit the paleoclimate community at large.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.

本项目旨在通过结合代理数据和模型模拟，开发全新世代理模型同化框架。所开发的框架将能够解决有关全新世期间温度趋势的关键问题，这些问题显示了观测结果和模型之间的差异。此外，还将调查北美西部和北非主要水文变化（干燥）背后的气候动态。为了改进模型数据比较，研究人员将使用代理系统建模方法来改进对全新世代理记录中记录的气候信号的解释，包括温度和降水的季节性。潜在的更广泛影响包括更好地了解全新世气候动态，调查模型模拟与全新世期间温度趋势观测之间的不匹配；美国西北部和北非的主要水文气候变化。将开发年度分辨率的全新世多变量重建和精心策划的全新世代理记录数据库，并提供给更广泛的气候界。将为研究生和早期职业科学家组织两次关于古气候数据同化的研讨会，以培训下一代古气候科学家。该项目将支持三名早期职业科学家和一名研究生。全新世多变量重建和代理网络将成为有益于整个古气候界的有用产品。该奖项反映了 NSF 的法定使命，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。

项目成果

A global database of Holocene paleotemperature records

• DOI: 10.1038/s41597-020-0445-3
• 发表时间: 2020-04-14
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Kaufman, Darrell;McKay, Nicholas;Zhilich, Snezhana
• 通讯作者: Zhilich, Snezhana

A multiproxy database of western North American Holocene paleoclimate records

• DOI: 10.5194/essd-13-1613-2021
• 发表时间: 2020-09
• 期刊: Earth System Science Data
• 影响因子: 11.4
• 作者: C. Routson;D. Kaufman;N. McKay;M. Erb;S. Arcusa;K. Brown;M. Kirby;J. Marsicek;R. Anderson;G. Jiménez-Moreno;J. Rodysill;M. Lachniet;S. Fritz;J. Bennett;M. Goman;S. Metcalfe;J. Galloway;G. Schoups;D. Wahl;J. Morris;F. Staines-Urías;A. Dawson;B. Shuman;D. Gavin;J. Munroe;B. Cumming

Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation

• DOI: 10.5194/cp-18-2599-2022
• 发表时间: 2022-12
• 期刊: Climate of the Past
• 影响因子: 4.3
• 作者: M. Erb;N. McKay;N. Steiger;S. Dee;Chris Hancock;R. Ivanović;L. Gregoire;P. Valdes

Global Synthesis of Regional Holocene Hydroclimate Variability Using Proxy and Model Data

• DOI: 10.1029/2022pa004597
• 发表时间: 2023-05
• 期刊: Paleoceanography and Paleoclimatology
• 影响因子: 3.5
• 作者: Chris Hancock;N. McKay;M. Erb;D. Kaufman;Cody R. Routson;R. Ivanović;L. Gregoire;P. Valdes

Holocene global mean surface temperature, a multi-method reconstruction approach

• DOI: 10.1038/s41597-020-0530-7
• 发表时间: 2020-06-30
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Kaufman, Darrell;McKay, Nicholas;Davis, Basil
• 通讯作者: Davis, Basil