Journal cover Journal topic
Climate of the Past An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/cp-2017-140
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
20 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Climate of the Past (CP).
Towards High Resolution Climate Reconstruction Using an Off-line Data Assimilation and COSMO-CLM 5.00 Model
Bijan Fallah1, Walter Acevedo2, Emmanuele Russo1, Nico Becker1, and Ulrich Cubasch1 1Institute of Meteorology, Freie Universität Berlin, Carl-Heinrich-Becker Weg 6–10, 12165 Berlin, Germany
2Institute of Mathematics, Universität Potsdam, Karl-Liebknecht-Str. 24/25, D-14476 Potsdam, Germany
Abstract. Paleo-proxy observations have been recently used to constrain the climate models through data assimilation (DA). However, both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the assimilation period 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 the 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-initialisation cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo and real observations into an ensemble of COSMO-CLM high resolution regional climate model (RCM) simulations over Europe, where 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 does linearly impact the DA skill. Such experiments can serve as a tool for selection of proxy records, which can potentially reduce the background error when they are assimilated in the model. Additionally, the sensibility of the 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. The dependency of the DA skill to different seasons is investigated. Furthermore, the effect of the spurious correlations within the observation space is studied and the optimal correlation length, within which the observations are assumed to be correlated, is detected. Finally, the real yearly-averaged observations are assimilated into the RCM and the performance is evaluated against a gridded observation 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 the climate field reconstruction efforts by combining high resolution paleo-climate simulations and the available proxy observations.

Citation: Fallah, B., Acevedo, W., Russo, E., Becker, N., and Cubasch, U.: Towards High Resolution Climate Reconstruction Using an Off-line Data Assimilation and COSMO-CLM 5.00 Model, Clim. Past Discuss., https://doi.org/10.5194/cp-2017-140, in review, 2017.
Bijan Fallah et al.
Bijan Fallah et al.

Video supplement

Towards High Resolution Climate Reconstruction Using an Off-line Data Assimilation and COSMO-CLM 5.00 Model-JJA
B. Fallah
https://doi.org/10.5446/33917
Towards High Resolution Climate Reconstruction Using an Off-line Data Assimilation and COSMO-CLM 5.00 Model-DJF
B. Fallah
https://doi.org/10.5446/33918
Bijan Fallah et al.

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Short summary
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 the proxies. This is done via Data Assimilation (DA), a method which 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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