Preprints
https://doi.org/10.5194/hess-2021-498
https://doi.org/10.5194/hess-2021-498
 
02 Nov 2021
02 Nov 2021
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Evaluating quantile-based bias adjustment methods for climate change scenarios

Fabian Lehner, Imran Nadeem, and Herbert Formayer Fabian Lehner et al.
  • 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

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

Fabian Lehner et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-498', Anonymous Referee #1, 13 Jan 2022
    • AC1: 'Reply on RC1', Fabian Lehner, 07 Mar 2022
  • RC2: 'Comment on hess-2021-498', Anonymous Referee #2, 21 Feb 2022
    • AC2: 'Reply on RC2', Fabian Lehner, 07 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-498', Anonymous Referee #1, 13 Jan 2022
    • AC1: 'Reply on RC1', Fabian Lehner, 07 Mar 2022
  • RC2: 'Comment on hess-2021-498', Anonymous Referee #2, 21 Feb 2022
    • AC2: 'Reply on RC2', Fabian Lehner, 07 Mar 2022

Fabian Lehner et al.

Fabian Lehner et al.

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Short summary
Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.