• Corpus ID: 209531805

Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach

@article{Huang2019CombiningIC,
  title={Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach},
  author={Huang Huang and Dorit M. Hammerling and B. Li and Richard L. Smith},
  journal={arXiv: Applications},
  year={2019}
}
Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model and valuable for uncertainty quantification. However, Bayesian approaches to multi-model ensembles have been criticized for making oversimplified assumptions about bias and variability, as well as treating different models as statistically independent. This paper extends the Bayesian hierarchical approach of Sansom et… 
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