Bayesian multi-model projection of climate: bias assumptions and interannual variability |
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| Authors: | Christoph M Buser H R Künsch D Lüthi M Wild C Schär |
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| Institution: | 1.Seminar for Statistics,ETH Zurich,Zurich,Switzerland;2.Institute for Atmospheric and Climate Science,ETH Zurich,Zurich,Switzerland |
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| Abstract: | Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations.
Application of this information to impact studies requires a combined probabilistic estimate taking into account the different
models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution
of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control
period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian
methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability.
In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent
(i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model
biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating
the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative
priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present
results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES
A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the
two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C
and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason
for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results
show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is
needed to determine how bias information can be exploited for this task. |
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