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.
An improved statistical bias correction method that also corrects dry climate models
Fabian Lehner,Imran Nadeem,and Herbert Formayer
Abstract. Daily meteorological data 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 correcting is applied to correct the raw model data. However, up to now no method has been introduced that fulfills the following demands simultaneously: (1) The long term climatological trends (climate change signal) should not be altered during bias correction, (2) the model data should match the observational data in the historical period as accurate as possible in a climatological sense and (3) models with too little wet days (precipitation above 0.1 mm) should be corrected accurately, which means that the wet day frequency is conserved. We improve the already existing quantile mapping approach so that it satisfies all three conditions. Our new method is called empirical percentile–percentile mapping (EPPM) which uses empirical distributions for meteorological variables and is therefore computationally inexpensive. The correction of precipitation is particularly challenging so our main focus is on precipitation. EPPM corrects the historical model data so that precipitation sums and wet days are equal to the observational data.
How to cite. Lehner, F., Nadeem, I., and Formayer, H.: An improved statistical bias correction method that also corrects dry climate models, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-515, 2020.
Received: 02 Oct 2020 – Discussion started: 16 Oct 2020
We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
We present an improved statistical bias correction method for climate models and put a focus on...