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

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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.