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Data Assimilation in Different Models of Complexity for Deglaciation

Climate and Earth system models are widely used to evaluate the impact of anthropogenic emissions on future climate. The models are clearly unrivaled in their ability to simulate a broad range of large-scale phenomena on seasonal to decadal time scales. However, the reliability of models to simulate climate variability on multidecadal and longer time scales requires additional evaluation. Climate records derived from paleoenvironmental parameters facilitate the testing of models out of the “comfort zone of present-day climate (Lohmann et al., 2020).

The fundamental idea behind paleoclimate data assimilation (PDA) is to constrain a climate model trajectory using proxy data and an observation operator (e.g., a forward model) and consequently optimally estimate past climate. It is also possible to quantitatively estimate uncertainties of proxies and simulations. Although the PDA and regular data assimilation root in the same statistical theory, the PDA has some specific characteristics. First, PDA observations, including proxies and proxy-based reconstructions, are time-averaged and continuous in time. Second, Long-term integration is an important feature in PDA. The target of PDA is to reconstruct interannual, decadal or centennial climate change over hundreds of years or longer. Therefore, PDA requires high computing performance and is more time consuming than the regular data assimilation. Third, initial conditions have minor impacts on the PDA results because the effects of initial conditions will slowly attenuate in time due to the chaotic nature of the atmosphere.

Considering the importance of climate models with different complexity and based on the information above, this PhD project aims to apply a suitable Ensemble Kalman Filter (EnKF) data assimilation method (Kalman, 1960; Nerger and Hiller, 2013) over the last 22ka years using three climate system models of different complexity. In order to obtain this goal, the project is divided into three phases:

• Applying data assimilation using a two-dimensional energy balance model for the last deglaciation
• An earth system model of intermediate complexity is employed using PDA to reconstruct the last deglaciation
• Running a data assimilation system using a coupled general circulation model for time slices

In all phases, proxy-based temperature data sets are used as observations in the PDA systems. Moreover, the EnKF algorithms will be executed utilizing Parallel Data Assimilation Framework (PDAF) (Nerger and Hilller, 2005).

Kalman, R. E., 1960: A New Approach to Linear Filtering and Prediction Problems. ASME. J. Basic Eng. 82(1), 35–45. https://doi.org/10.1115/1.3662552

Lohmann, G., M. Butzin, N. Eissner, X. Shi, C. Stepanek, 2020: Abrupt climate and weather changes across timescales. Paleoceanography and Paleoclimatology 35 (9), e2019PA003782, DOI:10.1029/2019PA003782, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003782

Nerger, L., W. Hiller, 2013: Software for Ensemble-based DA Systems – Implementation and Scalability. Computers and Geosciences 55 (2013) 110-118, https://www.sciencedirect.com/science/article/pii/S0098300412001215