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Climate Variability, Predictability and Climate Risks: A by Heinz Wanner, Martin Grosjean, Regine Röthlisberger, Elena

By Heinz Wanner, Martin Grosjean, Regine Röthlisberger, Elena Xoplaki

Heinz Wanner is the pinnacle of the weather learn crew and Director of the NCRR administration Center.

Martin Grosjean is

Regine Rothlisberger is a technological know-how Officer with the NCCR weather administration Center.

Elena Xoplaki is a technological know-how Officer with the NCCR weather administration Center.

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Extra resources for Climate Variability, Predictability and Climate Risks: A European Perspective

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2002) used a zonally averaged 2-D SDM coupled to a mixed-layer ocean model in which temperature anomalies diffused into the deep ocean, and conducted ∼101 simulations. 0 × 10−4 m2 s−1 , respectively. Knutti et al. (2002) used a similar model as Stocker and Schmittner (1997), and conducted ∼104 simulations. 2 to 0 Wm−2 . The a priori uncertainty range for climate sensitivity from 1 to 10 ◦ C was not narrowed. The number of ensemble members can be increased to the order of ∼106 by introducing a neural network to serve as a model substitute (Knutti et al.

This approach provides information on the single-model prediction uncertainty from uncertain starting conditions (Tracton and Kalnay 1993; Palmer and Anderson 1994; Stern and Miyakoda 1995). Additionally, the uncertainty due to unresolved processes can be described by introducing a stochastic component during the integration (Buizza et al. 1999) or by implementing socalled stochastic-dynamic subgrid models (Palmer et al. 2005). The degree of inter-model uncertainty can be assessed by comparing results obtained with different models (multi-model ensemble).

The daily values are smoothed using a 14 day running average Springer 46 Climatic Change (2006) 79:31–63 2004).

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