Assimilation of Time - averaged Pseudoproxies for Climate 1 Reconstruction

4 We examine the efficacy of a novel ensemble data assimilation (DA) technique in climate field 5 reconstructions (CFR) of surface temperature. We perform four pseudoproxy experiments 6 with both general circulation model (GCM) and 20th Century Reanalysis (20CR) data by 7 reconstructing surface temperature fields from a sparse network of noisy pseudoproxies. We 8 compare the DA approach to a conventional CFR approach based on Principal Component 9 Analysis (PCA) for experiments on global domains. DA outperforms PCA in reconstructing 10 global-mean temperature in all four experiments, and is more cosistent across experiments, 11 with a range of time-series correlations of 0.69–0.94 compared to 0.19–0.87 for the PCA 12 method. DA improvements are even more evident in spatial reconstruction skill, especially in 13 sparsely sampled pseudoproxy regions and for a 20CR experiment. We hypothesize that DA 14 improves spatial reconstructions because it relies on local temperature correlations. These 15 relationships appear to be more robust than orthogonal patterns of variability, which can 16 be non-stationary. Additionally, comparing results for GCM and 20CR data indicates that 17 pseudoproxy experiments that rely solely on GCM data may give a false impression of 18 reconstruction skill. 19