Climate Variability, Predictability and Climate Risks: A by H. Wanner (Editor), M. Grosjean (Editor), R. Rothlisberger

By H. Wanner (Editor), M. Grosjean (Editor), R. Rothlisberger (Editor), E. Xoplaki (Editor)

This booklet presents an built-in evaluation of matters with regards to weather variability and alter, predictability and dangers. It info either the technical features of variability and abrupt weather swap and the rural and low cost affects and outcomes.

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Sample text

Here the focus was on estimating the uncertainty of climate projections. Next, we continue to address probabilistic methods and introduce quantitative statistical measures of forecast uncertainty, with a specific focus on the interface to operational application and the end-user perspective. This will be exemplified on the seasonal time-scale. 1 The rationale A major addition to the forecasting repertoire of weather services in recent years has been the development of operational techniques for seasonal climate predictions.

2003). , Palmer et al. 2005). For example a serial approach utilises a statistical model to generate oceanic boundary conditions for the atmospheric dynamical model (Colman and Davey 2003). Likewise the output of dynamical models can be subject to statistical post-processing to compensate for model drift or to counter known limitations such as resolution. Such a model calibration requires an adequate number of forecasts performed from initial states in the past (“hindcasts”). 4 Hence the desire to derive estimates of forecast uncertainty in the probabilistic framework (Palmer 1993).

Naki´cenovi´c et al. (2000) proposed 40 different emission scenarios (SRES) that were to be viewed as “images of how the future might unfold”. The scenarios took into account “demographic development, socio-economic development, and technological change” (although no political intervention), but “probabilities or likelihood were not assigned to individual scenarios”. 1 Wm−2 (Wigley and Raper 2001). Thus differing emission scenarios result in uncertainty related to radiative forcing. However the absence of an assigned probability to the scenarios makes it difficult to quantify the uncertainty (Schneider 2001; Gr¨ubler and Nakicenovic 2001), and the high dimensionality of the phase space militates simulating all possible settings with GCMs.

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