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Abstract The path toward a warmer global climate is not smooth, but, rather, is made up of a succession of positive and negative temperature trends, with cooling having more chance to occur the shorter the time scale considered. In this paper, estimates of the probabilities of short-term cooling ( P cool ) during the period 2006–35 are performed for 5146 locations across Canada. Probabilities of cooling over durations from 5 to 25 yr come from an ensemble of 60 climate scenarios, based on three different methods using a gridded observational product and CMIP5 climate simulations. These methods treat interannual variability differently, and an analysis in hindcast mode suggests they are relatively reliable. Unsurprisingly, longer durations imply smaller P cool values; in the case of annual temperatures, the interdecile range of P cool values across Canada is, for example, ~2%–18% for 25 yr and ~40%–46% for 5 yr. Results vary slightly with the scenario design method, with similar geographical patterns emerging. With regards to seasonal influence, spring and winter are generally associated with higher P cool values. Geographical P cool patterns and their seasonality are explained in terms of the interannual variability over background trend ratio. This study emphasizes the importance of natural variability superimposed on anthropogenically forced long-term trends and the fact that regional and local short-term cooling trends are to be expected with nonnegligible probabilities.
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Abstract Spatial analog techniques consist in identifying locations whose historical climate is similar to the anticipated future climate at a reference location. In the process of identifying analogs, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this study, six a priori suitable metrics are described (the standardized Euclidean distance, the Kolmogorov–Smirnov statistic, the nearest-neighbor distance, the Zech–Aslan energy statistic, the Friedman–Rafsky runs statistic, and the Kullback–Leibler divergence) and criteria are proposed and investigated in an attempt to identify the best metric for selecting spatial analogs. The case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM, version 4.2.3), from which three annual indicators (total precipitation, heating degree-days, and cooling degree-days) are calculated over 30-yr periods (1971–2000 and 2041–70). It is found that the six metrics identify comparable analog regions at a relatively large scale but that best analogs may differ substantially. For best analogs, it is shown that the uncertainty stemming from the metric choice does not generally exceed that stemming from the simulation or model choice. On the basis of the set of criteria considered in this study, the Zech–Aslan energy statistic stands out as the most recommended metric for analog studies, whereas the Friedman–Rafsky runs statistic is the least recommended.