The bootstrap in climate risk analysis

Abstract

Climate risk is the probability of adverse effects from extreme values of variables in the climate system. Because climate changes, so can the various types of climate risk (floods, storms, etc.) change. This field is of strong socioeconomic relevance. Estimates of climate risk variations come from instrumental, proxy and documentary records of past climate extremes and projections of future extremes. Kernel estimation is a powerful statistical technique for quantifying trends in climate risk. It is not parametrically restricted and allows realistic, non-monotonic trends. The bootstrap is a computing-intensive statistical resampling method used here to provide a confidence band around the estimated risk curve. Confidence bands, like error bars, are essential for a reliable assessment whether changes and trends are significant or came by chance into the data. This methodology is presented using reconstructed flood records of the central European rivers Elbe, Oder and Werra over the past five centuries. Trends in flood risk differ among rivers and also between hydrological seasons. The scientific conclusion is that flood risk analysis has to take into account the high spatial variability from orographic rainfall, as well as different hydrological regimes in winter and summer. In an ideal co-operation between experts, quantitative knowledge with uncertainty ranges (like the estimated trends in climate risk) should form the deliverable from scientists to policy makers and decision takers. © 2011 Springer-Verlag Berlin Heidelberg.