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This paper introduces an original method for climate change detection, called temporal optimal detection method. The method consists in searching for a smooth temporal pattern in the observations. This pattern can be either the response of the climate system to a specific forcing or to a combination of forcings. Many characteristics of this new method are different from those of the classical “optimal fingerprint” method. It allows to infer the spatial distribution of the detected signal, without providing any spatial guess pattern. The spatial properties of the internal climate variability doesn’t need to be estimated either. The estimation of such quantities being very challenging at regional scale, the proposed method is particularly well-suited for such scale. The efficiency of the method is illustrated by applying it on real homogenized datasets of temperatures and precipitation over France. A multimodel detection is performed in both cases, using an ensemble of atmosphere-ocean general circulation models for estimating the temporal patterns. Regarding temperatures, new results are highlighted, especially by showing that a change is detected even after removing the uniform part of the warming. The sensitivity of the method is discussed in this case, relatively to the computation of the temporal patterns and to the choice of the model. The method also allows to detect a climate change signal in precipitation. This change impacts the spatial distribution of the precipitation more than the mean over the domain. The ability of the method to provide an estimate of the spatial distribution of the change following the prescribed temporal patterns is also illustrated. [ABSTRACT FROM AUTHOR]

In August 2003, Western Europe experienced a deadly heat wave. It is estimated that 15,000 people died in France from its direct effects. Applying an indirect estimation technique to the monthly number of deaths by sex, age, and place of residence for the period 1996-2003, we estimate the number of years that would have remained to those who died from the heat wave had they been spared. The paper also evaluates the role of the 'harvesting' effect of the 2003 heat wave in explaining the deficit of deaths recorded in 2004 - estimated at 23,000 out of an expected total of 535,000 deaths. The harvesting effect was modest and accounted for fewer than 5,000 of the 'missing' deaths in 2004. The mortality decline in 2004 is thus due mainly to other factors. One hypothesis is that special efforts have been directed towards isolated older people as a consequence of the heat wave. [ABSTRACT FROM AUTHOR]

High-resolution weather scenarios generated for climate change impact studies from the output of climate models must be spatially consistent. Analog models (AMs) offer a high potential for the generation of such scenarios. For each prediction day, the scenario they provide is the weather observed for days in a historical archive that are analogous according to different predictors. When the same 'analog date' is chosen for a prediction at several sites, spatial consistency is automatically satisfied. The optimal predictors and consequently the optimal analog dates, however, are expected to depend on the location for which the prediction is to be made. In the present work, the predictor (1000- and 500-hPa geopotential heights) domain of a benchmark AM is optimized for the probabilistic daily prediction of 8981 local precipitation 'stations' over France. The corresponding 8981 locally domain-optimized AMs are used to explore the spatial transferability and similarity of the optimal analog dates obtained for different locations. Whereas the similarity is very low even when the locations are close, the spatial transferability of the optimal analog dates for a given location is high. When they are used for the prediction at all other locations, the loss of prediction performance is therefore very low over large spatial domains (up to 500 km). Spatial transferability is lower in the presence of high mountains. It also depends on the parameters of the AM (e.g., its archive length, predictors, and number of analog dates used for the prediction). In the present case, AMs with higher prediction skill exhibit lower transferability. [ABSTRACT FROM AUTHOR]