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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/cp-2018-168
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-2018-168
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Dec 2018

Research article | 20 Dec 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Climate of the Past (CP).

Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation

Veronika Valler1,2, Jörg Franke1,2, and Stefan Brönnimann1,2 Veronika Valler et al.
  • 1Institute of Geography, University of Bern, Bern, Switzerland
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

Abstract. Data assimilation has been adapted in paleoclimatology to reconstruct past climate states. A key component of the assimilation system 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 paleo 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.

Veronika Valler et al.
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Veronika Valler et al.
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Short summary
In recent years, the data assimilation approach was adapted to the field of paleoclimatology to reconstruct past climate fields by combining model simulations and observations. To improve the performance of our paleo data assimilation system, we tested various techniques that are well established in weather forecasting, and evaluated their impact on assimilating instrumental data and proxy records (tree rings).
In recent years, the data assimilation approach was adapted to the field of paleoclimatology to...
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