[1] A statistical bias correction technique is applied to a set of high-resolution climate change simulations for Europe from 11 state-of-the-art regional climate models (RCMs) from the project ENSEMBLES. Modeled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period , which are then applied to the decade 1991-2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both intermodel variability and longer (e.g., decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias-corrected results are ensemble averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on, e.g., hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.Citation: Dosio, A., and P. Paruolo (2011), Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate,
Summary. Composite indicators aggregate a set of variables by using weights which are understood to reflect the variables’ importance in the index. We propose to measure the importance of a given variable within existing composite indicators via Karl Pearson's ‘correlation ratio’; we call this measure the ‘main effect’. Because socio‐economic variables are heteroscedastic and correlated, relative nominal weights are hardly ever found to match relative main effects; we propose to summarize their discrepancy with a divergence measure. We discuss to what extent the mapping from nominal weights to main effects can be inverted. This analysis is applied to six composite indicators, including the human development index and two popular league tables of university performance. It is found that in many cases the declared importance of single indicators and their main effect are very different, and that the data correlation structure often prevents developers from obtaining the stated importance, even when modifying the nominal weights in the set of non‐negative numbers with unit sum.
HighlightsComposite indicators are widely used in sustainable development and elsewhere.The effect of weights used in aggregating indicators is complex.Three tools are presented which help developers and users to investigate effects of weights.Case studies related to sustainable development demonstrate the benefits.
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