A Bayesian posterior predictive framework for weighting ensemble regional climate models
We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-06-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/10/2321/2017/gmd-10-2321-2017.pdf |
| Summary: | We present a novel Bayesian statistical approach to computing model
weights in climate change projection ensembles in order to create
probabilistic projections. The weight of each climate model is obtained by
weighting the current day observed data under the posterior distribution
admitted under competing climate models. We use a linear model to describe
the model output and observations. The approach accounts for uncertainty in
model bias, trend and internal variability, including error in the
observations used. Our framework is general, requires very little
problem-specific input, and works well with default priors. We carry out
cross-validation checks that confirm that the method produces the correct
coverage. |
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| ISSN: | 1991-959X 1991-9603 |