P2C2: Dynamical Climate Reconstruction Using Paleoclimate Data and Ensemble State Estimation

P2C2：使用古气候数据和集合状态估计进行动态气候重建

• 批准号: 0902500
• 负责人: Gregory Hakim
• 金额: $ 37万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0902500&HistoricalAwards=false
• 关键词: P2C2; Dynamical; Climate; Reconstruction; Using

Funding is provided to develop quantitative methods that could aid in taking isolated point measurements that are indirectly related to environmental variables and maximizing their ability to characterize past climatic conditions. This research strategy is analogous to the data assimilation approach of weather researchers. Specifically, the researchers will use an extension of the ensemble Kalman filter for assimilating the long-time averages represented by proxy data. The Simplied Parametrization primitivE-Equation DYnamics (SPEEDY) model will be used in this algorithm to assimilate ice-core accumulation for the past 1,000 years. The spatial influence of data for individual ice core records will be quantified, as will the sensitivity of the recovered climate states to the averaging period of the observations. The resulting estimates of the reconstructed climate states will then be analyzed for decadal climate variability, including the leading modes of variability as a function of time. Potential sites for future observations will also be identified using an ensemble approach to paleoclimate network design.The broader impacts involve strong bringing a different quantitative perspective to the analysis of paleoclimate data and providing support for graduate education.

提供了资金来开发定量方法，这些方法可以帮助进行与环境变量间接相关的孤立点测量结果，并最大程度地发挥其表征过去气候条件的能力。 该研究策略类似于天气研究人员的数据同化方法。具体而言，研究人员将使用集合卡尔曼过滤器的扩展来吸收代理数据代表的长期平均值。简化的参数化原始方程动力学（快速）模型将在该算法中使用，以吸收过去1000年的冰核积累。数据对单个冰核记录的空间影响将被量化，回收的气候状态对观测值的平均周期的敏感性也将进行量化。然后，将对重建的气候状态的结果估计值进行分析，以确保衰老的气候变异性，包括变异性的领先模式作为时间的函数。还将使用合奏方法来确定未来观察的潜在地点，以实现古气候网络设计。更广泛的影响涉及强烈的量化，将不同的定量视角带入对古气候数据的分析并为研究生教育提供支持。