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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 9 | Copyright

Special issue: Paleoclimate data synthesis and analysis of associated uncertainty...

Clim. Past, 14, 1345-1360, 2018
https://doi.org/10.5194/cp-14-1345-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Sep 2018

Research article | 24 Sep 2018

Towards high-resolution climate reconstruction using an off-line data assimilation and COSMO-CLM 5.00 model

Bijan Fallah1, Emmanuele Russo1, Walter Acevedo2, Achille Mauri3, Nico Becker1, and Ulrich Cubasch1 Bijan Fallah et al.
  • 1Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker Weg 6–10, 12165 Berlin, Germany
  • 2Deutscher Wetterdienst, Offenbach, Frankfurt am Main, Germany
  • 3European Commission, Joint Research Centre, Directorate D – Sustainable Resources – Bio-Economy Unit, 21027 Ispra, VA, Italy

Abstract. Data assimilation (DA) methods have been used recently to constrain the climate model forecasts by paleo-proxy records. Both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the time step of consequence for observations is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of recent paleoclimate studies using DA have performed low- or intermediate-resolution global simulations along with an off-line DA approach. In an off-line DA, the re-initialization cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo-observations and real observations into an ensemble of COSMO-CLM high-resolution regional climate model (RCM) simulations over Europe, for which the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations linearly impacts the DA skill. Such experiments can serve as a tool for the selection of proxy records, which can potentially reduce the state estimation error when they are assimilated. Additionally, the sensitivity of COSMO-CLM to the boundary conditions is addressed. The geographical regions where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. Furthermore, the effect of the spurious correlations within the observation space is studied and a optimal correlation radius, within which the observations are assumed to be correlated, is detected. Finally, the pollen-based reconstructed quantities at the mid-Holocene are assimilated into the RCM and the performance is evaluated against a test dataset. We conclude that the DA approach is a promising tool for creating high-resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve climate field reconstruction efforts by combining high-resolution paleoclimate simulations and the available proxy records.

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We try to test and evaluate an approach for using two main sources of information on the climate of the past: climate model simulations and proxies. This is done via data assimilation (DA), a method that blends these two sources of information in an intelligent way. However, DA and climate models are computationally very expensive. Here, we tested the ability of a computationally affordable DA to reconstruct high-resolution climate fields.
We try to test and evaluate an approach for using two main sources of information on the climate...
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