Assimilation of time-averaged pseudoproxies for climate reconstruction

Nathan J. Steiger*, Gregory J. Hakim, Eric J. Steig, David S. Battisti, Gerard H. Roe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

Abstract

The efficacy of a novel ensemble data assimilation (DA) technique is examined in the climate field reconstruction (CFR) of surface temperature. A minimalistic, computationally inexpensive DA technique is employed that requires only a static ensemble of climatologically plausible states. Pseudoproxy experiments are performed with both general circulation model (GCM) and Twentieth Century Reanalysis (20CR) data by reconstructing surface temperature fields from a sparse network of noisy pseudoproxies. TheDAapproach is compared to a conventional CFR approach based on principal component analysis (PCA) for experiments on global domains. DA outperforms PCA in reconstructing global-mean temperature in all experiments and is more consistent across experiments, with a range of time series correlations of 0.69-0.94 compared to 0.19- 0.87 for the PCA method. DA improvements are even more evident in spatial reconstruction skill, especially in sparsely sampled pseudoproxy regions and for 20CR experiments. It is hypothesized that DA improves spatial reconstructions because it relies on coherent, spatially local temperature patterns, which remain robust even when glacial states are used to reconstruct nonglacial states and vice versa. These local relationships, as utilized by DA, appear to be more robust than the orthogonal patterns of variability utilized by PCA. Comparing results for GCMand 20CR data indicates that pseudoproxy experiments that rely solely on GCM data may give a false impression of reconstruction skill.

Original languageAmerican English
Pages (from-to)426-441
Number of pages16
JournalJournal of Climate
Volume27
Issue number1
DOIs
StatePublished - Jan 2014
Externally publishedYes

Keywords

  • Data assimilation
  • Kalman filters
  • Paleoclimate
  • Principal components analysis

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