An improved statistical bias correction method that also corrects dry climate models

Abstract

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.

Assessing the impact of bias correction approaches on climate extremes and the climate change signal

“…This SSR approach deals with too many wet days and too many dry days in the same way. Lehner et al (2021Lehner et al ( , 2020 provided an algorithm that follows the bias adjustment and adds additional wet days in order to reproduce the observation's precipitation sums and wet day frequency. Tschöke et al (2017) proposed a methodology for handling the null precipitation values in order to improve the dry day quantities from the simulations.…”

“…The effect however was generally small. In most applications (Cannon et al, 2015; Lehner et al, 2020; Lehner et al, 2021), trend preservation may be regarded as an advantage. However, other studies pointed out that provided that application of intensity‐dependent bias correction is scientifically appropriate, the climate change signal (CCS) modification should be a desirable effect (Ivanov et al, 2018).…”

Multivariate adjustment of drizzle bias using machine learning in European climate projections

“…2 illustrate for each RCM the total annual precipitation, the number of rainy days, and the mean annual temperature for the grid cells closest to the 600 GSOD stations over the 1981-2005 period. In line with common practice for distinguishing between rainy and non-rainy days using a threshold of 0.1 mm, we have adopted this criterion for the present research (Anagnostopoulou and Tolika, 2012;Liu et al, 2013;Lehner et al, 2020). Another commonly used threshold is 1 mm (e.g.…”