Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3539–3551, 2021
Geosci. Model Dev., 14, 3539–3551, 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 et al.

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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,, 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,, 2011. a
Bishop, C. H. and Abramowitz, G.: Climate model dependence and the replicate Earth paradigm, Clim. Dynam., 41, 885–900,, 2013. a, b, c
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,, 2014. a
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.