final_program_abstracts[1]

11 IMSC Session Program
Climate change projections for Switzerland: A Bayesian multimodel
combination using ENSEMBLES regional climate
models
Monday - Parallel Session 9
A. M. Fischer 1 , C. Buser 2 , A. P. Weigel 1 , M. A. Liniger 1 and C. Appenzeller 1
1
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
2 Seminar for Statistics ETH Zurich, Zurich, Switzerland
The assessment of different future climate pathways is a highly challenging task due
to the cascade of uncertainties ranging from emission uncertainties, model
uncertainties over natural fluctuations down to uncertainties arising from downscaling
approaches. The latter is particularly challenging over complex terrains such as the
Alpine region. However, it is climate change information on these spatial scales,
which is most relevant for end-user needs. Here, we focus on model uncertainty. A
pragmatic and well-accepted approach to address model uncertainty is given by the
concept of multi-model combination. The key challenge in this context is the
derivation of a probability density function (pdf) from a finite set of discrete ensemble
members. Given that any climate projection needs to be conditioned on an array of
unprovable assumptions (e.g. assumptions concerning the future behavior of
systematic model biases), it is conceptually reasonable to determine such a multimodel
pdf within a Bayesian framework.
Here, we discuss the recently developed Bayesian multi-model combination algorithm
of Buser et al. (2009) with regard to its applicability for regional climate scenarios.
This Bayesian methodology combines observations of the control period with the
output of control and scenario runs from multiple climate models. The algorithm
considers both the change of the mean signal as well as changes in inter-annual
variability and quantifies the systematic model biases. Due to an identifyability
problem between the climate change signal and model-induced projection errors, an
informative prior needs to be applied to constrain the projection error tolerance. Using
synthetic data, mimicking real model data, it can be shown that the posterior
distribution in the mean climate shift is largely dominated by the underlying
assumptions of this prior: the more informative the prior is chosen, the smaller is the
uncertainty in the mean shift and vice versa. This also has implications on how an
outlier in the different model projections is contributing to the posterior distribution.
Different approaches to define this informative prior are discussed.
As the main application, the Bayesian methodology is then applied to seasonally
averaged regional climate scenarios for Switzerland under the A1B emission scenario,
taking 2021-2050 as scenario and 1961-1990 as control period. The data basis for
these projections stems from a new generation of high-resolution (25 km) regional
climate model (RCM) simulations provided by the European project FP6-
ENSEMBLES. The magnitudes of internal variability are quantified, and a proposal is
made on how these may be included into the prior specifications. Applying these
specifications, the projections indicate a temperature increase over Switzerland within
the range of plus 0.5 – 3.0 °C, similar to previous estimates of climate scenarios
derived from a pattern scaling approach of PRUDENCE models. However, unlike this
earlier study, the transient ENSEMBLES model simulations do not reveal a
significant decrease in summer and autumn precipitation. This points to the
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