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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 15, issue 4
Clim. Past, 15, 1275–1301, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Clim. Past, 15, 1275–1301, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Jul 2019

Research article | 05 Jul 2019

Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering

Nils Weitzel et al.

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Subject: Proxy Use-Development-Validation | Archive: Terrestrial Archives | Timescale: Holocene
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Cited articles

Altekar, G., Dwarkadas, S., Huelsenbeck, J. P., and Ronquist, F.: Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference, Bioinformatics, 20, 407–415, 2004. a
Anderson, J. L. and Anderson, S. L.: A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Mon. Weather Rev., 127, 2741–2758, 1999. a
Annan, J. D. and Hargreaves, J. C.: A new global reconstruction of temperature changes at the Last Glacial Maximum, Clim. Past, 9, 367–376,, 2013. a
Bartlein, P., Harrison, S., Brewer, S., Connor, S., Davis, B., Gajewski, K., Guiot, J., Harrison-Prentice, T., Henderson, A., Peyron, O., Prentice, I., Scholze, M., Seppä, H., Shuman, B., Sugita, S., Thompson, R., Viau, A., Williams, J., and Wu, H.: Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis, Clim. Dynam., 37, 775–802, 2011. a, b, c, d, e
Birks, H. J. B., Heiri, O., Seppä, H., and Bjune, A. E.: Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies, The Open Ecology Journal, 3, 68–110, 2010. a
Publications Copernicus
Short summary
A new method for probabilistic spatial reconstructions of past climate states is presented, which combines pollen data with a multi-model ensemble of climate simulations in a Bayesian framework. The approach is applied to reconstruct summer and winter temperature in Europe during the mid-Holocene. Our reconstructions account for multiple sources of uncertainty and are well suited for quantitative statistical analyses of the climate under different forcing conditions.
A new method for probabilistic spatial reconstructions of past climate states is presented,...