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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
Type of publication
Peer-reviewed
Publikationsform
Original article (peer-reviewed)
Author
Valler Veronika, Franke Jörg, Brönnimann Stefan,
Project
Reconstructing Climate Using Ensemble Kalman Fitting (REUSE)
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Original article (peer-reviewed)
Journal
Climate of the Past
Volume (Issue)
15(4)
Page(s)
1427 - 1441
Title of proceedings
Climate of the Past
DOI
10.5194/cp-15-1427-2019
Open Access
URL
http://doi.org/10.5194/cp-15-1427-2019
Type of Open Access
Publisher (Gold Open Access)
Abstract
Abstract. Data assimilation has been adapted in paleoclimatology to reconstruct past climate states. A key component of some assimilation systems is the background-error covariance matrix, which controls how the information from observations spreads into the model space. In ensemble-based approaches, the background-error covariance matrix can be estimated from the ensemble. Due to the usually limited ensemble size, the background-error covariance matrix is subject to the so-called sampling error. We test different methods to reduce the effect of sampling error in a published paleoclimate data assimilation setup. For this purpose, we conduct a set of experiments, where we assimilate early instrumental data and proxy records stored in trees, to investigate the effect of (1) the applied localization function and localization length scale; (2) multiplicative and additive inflation techniques; (3) temporal localization of monthly data, which applies if several time steps are estimated together in the same assimilation window. We find that the estimation of the background-error covariance matrix can be improved by additive inflation where the background-error covariance matrix is not only calculated from the sample covariance but blended with a climatological covariance matrix. Implementing a temporal localization for monthly resolved data also led to a better reconstruction.
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