Abstract
Studies of climate change rely on numerical outputs simulated from Global Climate Models coupling the dynamics of ocean and atmosphere (GCMs). GCMs are, however, notoriously affected by substantial systematic errors (biases), whose assessment is essential to assert the accuracy and robustness of simulated climate features. This contribution focuses on constructing a Bayesian hierarchical model for the quantification of climate model biases in a multi-model framework. The method combines information from a multi-model ensemble of GCM simulations to provide a unified assessment of the bias. It further individuates different bias components that are characterized as non-stationary spatial fields accounting for spatial dependence. The approach is illustrated based on the case of near-surface air temperature bias over the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled Model Intercomparison Project.
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Acknowledgements
The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP7/2007–2013) under Grant agreement n 603521 - PREFACE. The authors would like to thank the two anonymous reviewers.
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Arisido, M.W., Gaetan, C., Zanchettin, D., Rubino, A. (2017). Bayesian Hierarchical Model for Assessment of Climate Model Biases. In: Argiento, R., Lanzarone, E., Antoniano Villalobos, I., Mattei, A. (eds) Bayesian Statistics in Action. BAYSM 2016. Springer Proceedings in Mathematics & Statistics, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-54084-9_10
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DOI: https://doi.org/10.1007/978-3-319-54084-9_10
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