Joint Seminar: Challenges in Quantifying Climate Model Bias, Dependence and Performance

Recent coordinated efforts like CMIP3, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate change in a probabilistic way.

This presentation outlines the motivation for using multi-model ensembles, discusses the methods published so far and compares their results for regional temperature projections. The challenges in interpreting results based on the multi-model ensemble are caused by the lack of verification of climate projections, the problem of model dependence, compensating errors in climate sensitivity and forcing, model bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', of which the sampling is unclear in the first place. It is shown that the common assumption that model biases compensate as models are averaged is only partly valid, and that the decision of what constitutes a 'good model' for future projections is difficult to answer based on present observations.

The presentation is an extension of a recent review article by C. Tebaldi and R. Knutti, 2007, The use of the multi-model ensemble in probabilistic climate projections, Philosophical Transactions of the Royal Society  A, 365, 2053-2075, doi:10.1098/rsta.2007.2076, available at




13:30 Uhr


Bundesstr. 53, room 022/023
Seminar Room 022/023, Ground Floor, Bundesstrasse 53, 20146 Hamburg, Hamburg


Reto Knutti, ETH Zürich


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