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

提供资金用于开发定量方法，以帮助进行与环境变量间接相关的孤立点测量，并最大限度地提高它们描述过去气候条件的能力。这一研究策略类似于气象研究人员的数据同化方法。具体地说，研究人员将使用集合卡尔曼滤波的扩展来同化由代理数据表示的长期平均。该算法将使用简化的参数化原始方程动力学(SPEEDY)模式来同化过去1000年的冰芯积累。将量化单个冰芯记录的数据的空间影响，以及恢复的气候状态对观测平均周期的敏感性。然后将对重建的气候状态的估计结果进行十年气候变率分析，包括作为时间函数的主要变率模式。还将利用古气候网络设计的综合办法确定未来可能进行观测的地点。更广泛的影响包括为古气候数据的分析带来不同的量化视角，并为研究生教育提供支助。

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.