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Evaluating model-simulated variability in temperature extremes using modified percentile indices



Percentile indices monitoring the frequency of moderate temperature extremes are widely used to assess changes in present and future temperature extremes because of their straightforward interpretation. While observed trends in such indices can be, and have been, compared with model-simulated trends, their definition relative to each model's own climatology inhibits their use for the evaluation of model-simulated temperature variability. This is unfortunate, as in many parts of the world, indices from observations remain the only source of publicly available information about extreme temperature variability. We approach this problem by introducing a novel adjustment to the standard method for deriving indices from climate models. This involves the removal of the bias in the mean annual cycle of the models and the use of percentile thresholds from a reference data set. We illustrate the technique by comparing daily minimum (TN) and maximum (TX) temperatures from the fifth phase of Coupled Model Intercomparison Project (CMIP5) historical simulations with those from an observation-based data set and from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the European Centre for Medium-Range Weather Forecasts (ERA-40) reanalyses. Biases in the annual cycle also translate into biases in the representation of the percentile indices in the models and reanalyses. Generally, percentile indices based on daily TX are well represented by the models and reanalyses compared to the observations. For percentile indices based on daily minimum temperature, however, large discrepancies occur particularly between the reanalyses.