An Online Paleoclimate Data Assimilation With a Deep Learning-Based Network

Abstract

An online paleoclimate data assimilation (PDA) that utilizes climate forecasts from a deep learning-based network (NET) along with assimilation of proxies to reconstruct surface air temperature, is investigated here. The NET is trained on ensemble simulations from the Community Earth System Model-Last Millennium Ensemble. Due to the nonlinear features with high-dimensional input, NET gains better predictive skills compared to the linear inverse model (LIM) in a reduced empirical orthogonal function (EOF) space. Thus, an alternative for online PDA is to couple the NET with the integrated hybrid ensemble Kalman filter (IHEnKF). Moreover, an analog blending strategy is proposed to increase ensemble spread and mitigate filter divergence, which blends the analog ensembles selected from climatological samples based on proxies and cycling ensembles advanced by NET. To account for the underestimated uncertainties of real proxy data, an observation error inflation method is applied, which inflates the proxy error variance based on the comparison between the estimated proxy error variance and its climatological innovation. Consistent results are obtained from the pseudoproxy experiments and the real proxy experiments. The more informative ensemble priors from the online PDA using NET enhance the reconstructions than the online PDA using LIM, and both outperform the offline PDA with randomly sampled climatological ensemble priors. The advantages of online PDA with NET over the online PDA with LIM and offline PDA become more pronounced, as the proxy data become sparser.