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Adv. Sci. Res., 15, 107-116, 2018
https://doi.org/10.5194/asr-15-107-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
08 Jun 2018
Bias adjustment for threshold-based climate indicators
Peter Hoffmann, Christoph Menz, and Arne Spekat Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Abstract. A method is presented which applies bias adjustments to climate indicators that are based on fixed thresholds, e.g., the number of hot days with the maximum temperature exceeding 30 °C or the number of days with heavy precipitation in exceedance of 20 mm rainfall. The bias adjustment first identifies the percentile of the required threshold value in reference climate data. Then it computes the value of this percentile for the individual historical climate model simulations – here an ensembles of EURO-CORDEX model runs, including dynamical and statistical models. Finally, the climate indicator is re-calculated for each model. The method is applied to climate projections as well, giving further insight into the projected development of the ensemble for extreme conditions. It is assessed that communication to the public and decision makers is improved by expressing these changes in extremes based on absolute values.
Citation: Hoffmann, P., Menz, C., and Spekat, A.: Bias adjustment for threshold-based climate indicators, Adv. Sci. Res., 15, 107-116, https://doi.org/10.5194/asr-15-107-2018, 2018.
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Short summary
The adjustment of bias, i.e., systematic errors, of climate models are a necessity when comparing results of an ensemble of these models. Usually, the meteorological parameters such as temperature or rainfall amounts themselves are subject to bias adjustments. We present a new method to apply bias adjustment to so-called climate indicators which are derived from those parameters, e.g., the number of days warmer than 30 °C or the number of days with more than 20 mm of rain.
The adjustment of bias, i.e., systematic errors, of climate models are a necessity when...
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