Probabilistic climate change predictions applying Bayesian model averaging
SCIE
SCOPUS
- Title
- Probabilistic climate change predictions applying Bayesian model averaging
- Authors
- Min, SK; Simonis, D; Hense, A
- Date Issued
- 2007-08-15
- Publisher
- Royal Society
- Abstract
- This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature ( SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging ( BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.
- Keywords
- global climate change; Bayesian model averaging; probabilistic prediction; surface air temperature; MULTIMODEL ENSEMBLES; REGIONAL-SCALE; TEMPERATURE; UNCERTAINTY; SIMULATIONS; PROJECTIONS
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/15557
- DOI
- 10.1098/RSTA.2007.2070
- ISSN
- 1364-503X
- Article Type
- Article
- Citation
- PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, vol. 365, no. 1857, page. 2103 - 2116, 2007-08-15
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