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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-253
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-253
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 01 Oct 2020

Submitted as: development and technical paper | 01 Oct 2020

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This preprint is currently under review for the journal GMD.

A Markov chain method for weighting climate model ensembles

Max Kulinich1, Yanan Fan1, Spiridon Penev1, and Jason P. Evans2 Max Kulinich et al.
  • 1School of Mathematics and Statistics, UNSW Sydney, Australia
  • 2Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Australia

Abstract. Climate change is typically modelled using sophisticated mathematical models (Climate Models) of physical processes taking place over long periods of time. Multi-model ensembles of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble in an optimal way. We present a novel approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data. The Markov chain method showed improved performance over those methods when measured by the root mean squared error and the R-squared metrics. The results of this comparative analysis should serve to motivate further studies in Markov chain and other nonlinear methods application, that address the issues of finding optimal model weight for constructing ensemble means.

Max Kulinich et al.

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Max Kulinich et al.

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Latest update: 23 Oct 2020
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Short summary
We present a novel approach based on Markov chains to estimate model weights to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data. The Markov chain method showed improved performance over those methods when measured by the root mean squared error and the R-squared metrics. The results of this comparative analysis should serve to motivate further studies in Markov chain and other nonlinear methods applications.
We present a novel approach based on Markov chains to estimate model weights to obtain ensemble...
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