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

提供资金发展定量方法，帮助进行与环境变量间接有关的孤立点测量，并最大限度地利用这些方法表征过去气候条件的能力。这种研究策略类似于气象研究人员的数据同化方法。具体来说，研究人员将使用集成卡尔曼滤波器的扩展来吸收由代理数据表示的长期平均值。该算法将使用简化参数化原始方程动力学（SPEEDY）模型来同化过去1000年的冰芯积累。个别冰芯记录数据的空间影响将被量化，恢复的气候状态对观测的平均周期的敏感性也将被量化。然后，将对重建气候状态的结果估计进行年代际气候变率分析，包括作为时间函数的变率的主要模态。未来观测的潜在地点也将使用古气候网络设计的综合方法来确定。更广泛的影响包括为古气候数据分析带来不同的定量视角，并为研究生教育提供支持。

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.