Casanueva Vicente, Ana

Kotlarski, Sven

Liniger, Mark A.

English

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Geophysical Research AbstractsVol. 19, EGU2017-9383, 2017EGU General Assembly 2017© Author(s) 2017. CC Attribution 3.0 License.Bias correction of multi-variate indices: Heat stress in SwitzerlandAna Casanueva, Sven Kotlarski, and Mark A. LinigerFederal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland (ana.casanueva@meteoswiss.ch)High resolution regional climate model (RCM) simulations are important tools to provide the meteorologicalvariables required in climate impact assessments. Despite the advances towards higher spatial resolution and betterresolved physical processes, RCMs often cannot be directly used in impact studies due to their partly substantialbiases. In the climate change context, distributional bias correction (BC) methods are frequently used to deal withsystematic model biases. BC methods can correct either some parameters of the distribution (e.g. the mean viadistributional shift or scaling) or all quantiles (e.g. via the empirical quantile mapping). The empirical quantilemapping (QM) is widely used in the literature to bias correct individual variables (e.g. temperature, precipitation),in a few cases also for variables such as humidity or wind.In the present work we analyze the suitability of QM to derive a multi-variate index (the wet bulb temper-ature, WBT) from the new generation of climate change scenarios for Switzerland (CH2018). WBT is a relativelysimple proxy for heat stress on the human body. It is a simple, but non-linear multi-variate index that depends ontemperature and humidity. Since extreme heat stress conditions occur at sub-daily scale but only daily values areusually available from RCMs, we analyze the sensitivity of the WBT to the use of different daily aggregated valuesin its calculation, compared to the maximum WBT obtained from observed hourly data. Further, we show thatthe separate correction of temperature and humidity allows reproduction of the distribution of the daily maximumWBT. Additionally, we explore climate change projections of WBT comparing the results from bias corrected andraw RCM data using a selection of EURO-CORDEX RCM simulations.

Bias correction of multi-variate indices : heat stress in Switzerland

https://repositorio.unican.es/xmlui/bitstream/10902/18331/3/Biascorrection.pdf

Bias correction of multi-variate indices : heat stress in Switzerland

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