Back to overview

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)
Show all

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

Type of Open Access Publisher (Gold Open Access)


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.