Sources of uncertainty in future changes in local precipitation
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- Rowell, D.P. Clim Dyn (2012) 39: 1929. doi:10.1007/s00382-011-1210-2
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This study considers the large uncertainty in projected changes in local precipitation. It aims to map, and begin to understand, the relative roles of uncertain modelling and natural variability, using 20-year mean data from four perturbed physics or multi-model ensembles. The largest—280-member—ensemble illustrates a rich pattern in the varying contribution of modelling uncertainty, with similar features found using a CMIP3 ensemble (despite its limited sample size, which restricts it value in this context). The contribution of modelling uncertainty to the total uncertainty in local precipitation change is found to be highest in the deep tropics, particularly over South America, Africa, the east and central Pacific, and the Atlantic. In the moist maritime tropics, the highly uncertain modelling of sea-surface temperature changes is transmitted to a large uncertain modelling of local rainfall changes. Over tropical land and summer mid-latitude continents (and to a lesser extent, the tropical oceans), uncertain modelling of atmospheric processes, land surface processes and the terrestrial carbon cycle all appear to play an additional substantial role in driving the uncertainty of local rainfall changes. In polar regions, inter-model variability of anomalous sea ice drives an uncertain precipitation response, particularly in winter. In all these regions, there is therefore the potential to reduce the uncertainty of local precipitation changes through targeted model improvements and observational constraints. In contrast, over much of the arid subtropical and mid-latitude oceans, over Australia, and over the Sahara in winter, internal atmospheric variability dominates the uncertainty in projected precipitation changes. Here, model improvements and observational constraints will have little impact on the uncertainty of time means shorter than at least 20 years. Last, a supplementary application of the metric developed here is that it can be interpreted as a measure of the agreement amongst models of their projected local precipitation change. Results differ from, but are complementary to, those of the more usual approach.