Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3539-2021
https://doi.org/10.5194/gmd-14-3539-2021
Development and technical paper
 | 
11 Jun 2021
Development and technical paper |  | 11 Jun 2021

A Markov chain method for weighting climate model ensembles

Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson

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Cited articles

Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. a, b
Bai, J. and Wang, P.: Conditional Markov chain and its application in economic time series analysis, J. Appl. Econ., 26, 715–734, https://doi.org/10.1002/jae.1140, 2011. a
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Del Moral, P. and Penev, S.: Stochastic Processes. From Applications to Theory, p. 121, Taylor and Francis Group, Boca Raton, 2016. a
Evans, J. P., Ji, F., Lee, C., Smith, P., Argüeso, D., and Fita, L.: Design of a regional climate modelling projection ensemble experiment – NARCliM, Geosci. Model Dev., 7, 621–629, https://doi.org/10.5194/gmd-7-621-2014, 2014. a
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Short summary
We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
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