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Global space-time models for climate ensembles. (English) Zbl 1454.62436

Summary: Global climate models aim to reproduce physical processes on a global scale and predict quantities such as temperature given some forcing inputs. We consider climate ensembles made of collections of such runs with different initial conditions and forcing scenarios. The purpose of this work is to show how the simulated temperatures in the ensemble can be reproduced (emulated) with a global space/time statistical model that addresses the issue of capturing nonstationarities in latitude more effectively than current alternatives in the literature. The model we propose leads to a computationally efficient estimation procedure and, by exploiting the gridded geometry of the data, we can fit massive data sets with millions of simulated data within a few hours. Given a training set of runs, the model efficiently emulates temperature for very different scenarios and therefore is an appealing tool for impact assessment.

MSC:

62P12 Applications of statistics to environmental and related topics
62M30 Inference from spatial processes
86A10 Meteorology and atmospheric physics
86A32 Geostatistics

Software:

FRK
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References:

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