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Climate of the Past An interactive open-access journal of the European Geosciences Union
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CP | Articles | Volume 16, issue 4
Clim. Past, 16, 1493–1508, 2020
https://doi.org/10.5194/cp-16-1493-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Clim. Past, 16, 1493–1508, 2020
https://doi.org/10.5194/cp-16-1493-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Aug 2020

Research article | 11 Aug 2020

An empirical evaluation of bias correction methods for palaeoclimate simulations

Robert Beyer et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (22 Oct 2019) by Steven Phipps
AR by Anna Mirena Feist-Polner on behalf of the Authors (03 Feb 2020)  Author's response
ED: Referee Nomination & Report Request started (06 Feb 2020) by Steven Phipps
RR by Anonymous Referee #1 (22 Feb 2020)
RR by Anonymous Referee #3 (12 May 2020)
ED: Publish subject to minor revisions (review by editor) (18 May 2020) by Steven Phipps
AR by Anna Mirena Feist-Polner on behalf of the Authors (29 Jun 2020)  Author's response
ED: Publish subject to technical corrections (30 Jun 2020) by Steven Phipps
AR by Robert Beyer on behalf of the Authors (08 Jul 2020)  Author's response    Manuscript
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Short summary
Even the most sophisticated global climate models are known to have significant biases in the way they simulate the climate system. Correcting model biases is therefore essential for creating realistic reconstructions of past climate that can be used, for example, to study long-term ecological dynamics. Here, we evaluated three widely used bias correction methods by means of a global dataset of empirical temperature and precipitation records from the last 125 000 years.
Even the most sophisticated global climate models are known to have significant biases in the...
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