Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-29-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-29-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Apr 2023
| 24 Apr 2023
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Imran Nadeem
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Herbert Formayer
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
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Short summary
Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Climate model output has systematic errors which can be reduced with statistical methods. We...
