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.
Arisido, M., Gaetan, C., Zanchettin, D., Rubino, A. (2017). Bayesian hierarchical model for assessment of climate model biases. In R. Argiento, E. Lanzarone, I.A. Villalobos, A. Mattei (a cura di), Bayesian Statistics in Action - Proceedings of BAYSM 2016 (pp. 105-113). Springer New York LLC [10.1007/978-3-319-54084-9_10].
Bayesian hierarchical model for assessment of climate model biases
Arisido, MW
Primo
;
2017
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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