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11 IMSC Session Program “A New Method for Making Objective Probabilistic Climate Forecasts from Numerical Climate Models Based on Jeffreys' Prior” (2009), Jewson S., Rowlands D. and Allen, M., http://arxiv.org/abs/0908.4207 Abstracts 49
11 IMSC Session Program Multi-model combination by a Bayesian hierarchical model: Assessment of ice accumulation over the oceanic Arctic region Monday - Poster Session 9 Malaak Kallache 1,2 , Elena Maksimovich 3 , Paul-Antoine Michelangeli 2 and Philippe Naveau 1 1 Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), Paris, France 2 CLIMPACT, Paris, France 3 Laboratoire d’Océanographie et du Climat (LOCEAN-IPSL) Paris, France The performance of general circulation models (GCM) varies across regions and periods. When projecting into the future it is therefore not evident to reject or prefer a certain GCM. Combining the outputs of several GCM models may enhance results. We present a method to merge multi-model GCM projections via a Bayesian hierarchical model similar to Tebaldi et al. (2005). Here, the influence of each GCM is weighted according to its performance in a training period, with regard to observations. As outcome predictive distributions for yet unobserved observations are obtained. Technically, GCM outputs and observations are assumed to vary randomly around common means, which are interpreted as the actual target values under consideration. Posterior parameter distributions of our Bayesian hierarchical model are obtained by a MCMC method. Advantageously all parameters, such as bias and precision of the GCM models, are estimated together. Potential time dependence is accounted for by integrating a Kalman filter (cf. Harvey et al., 2007). The significance of trend slopes of the common means is evaluated by analysing the posterior distribution of the parameters. Our method is applied to assess the evolution of ice accumulation over the oceanic Arctic region in cold seasons. The observed ice index is created out of NCEP reanalysis data. Outputs of seven GCM models are merged by using the training period 1962–1999 and prediction periods 2046–2065 and 2081–2100 with SRES scenarios A2 and B1. A continuing decrease of ice accumulation is visible for the A2 scenario, whereas the index stabilises for the B1 scenario in the second prediction period. Tebaldi, C., R. L. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multi-model ensembles. Journal of Climate, 18 (10), 1524–1540. Harvey, A. C., T. M. Trimbur, and H. K. Van Dijk, 2007: Trends and cycles in economic time series: A Bayesian approach. Journal of Econometrics, 140 (2), 618– 649, doi:10.1016/j.jeconom.2006.07.006. Abstracts 50
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