A Markov chain method for weighting climate model ensembles.

Kulinich, M., Y. Fan, S. Penev, J.P. Evans and R. Olson
Geoscientific Model Development, 14(6), 3539-3551, doi: 10.5194/gmd-14-3539-2021, 2021.


Climate change is typically modeled using sophis- ticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi- model ensemble means of climate models show better cor- relation with the observations than any of the models sepa- rately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to ob- tain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data, as well as model-as-truth experiments. The Markov chain method showed improved performance over those methods when measured by the root mean squared er- ror in validation and comparable performance in model-as- truth experiments. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing en- semble means.

Key Figure

Figure 3. CMIP5 data properties. (a) Model outputs and observations. (b) Model output distribution. (c) AVE, COE and MCE weights.

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