Reducing negative impacts of bias adjustment on the distribution tail and extreme climate indicators in MIdAS

Bias adjustment of climate models is today normally performed with quantile mapping methods that account for the whole distribution of the parameter. The bulk of the distribution is well described as long as sufficient data records are used (Berg et al., 2012), however, the extreme tails will always suffer from large uncertainties. These uncertainties stem from both the climate model and the reference data set, which prevents a robust and detailed identification of bias in the extreme tail. Empirical quantile mapping methods are therefore prone to overfitting, and may introduce substantial bias when applied outside the calibration period. Commonly, a constant adjustment is applied for values outside the range of the calibration period, but there is room for improvements of the extrapolation method.

While working with a climate service for Sweden, a clear offset was identified between data adjusted within and outside the calibration period for an extreme indicator of daily maximum precipitation. This study explores different extrapolation methods for the extreme tail of the distribution in the spline-based empirical quantile mapping method of the MIdAS bias adjustment method (Berg et al., 2022). By limiting the bias adjustment to the first 95% of the distribution, and thereafter applying a constant or a linear fit to the remaining 5% of data in the tail, the offset is strongly reduced and the adjusted extremes become more robust and plausible.

Berg, P., Feldmann, H., & Panitz, H. J. (2012). Bias correction of high resolution regional climate model data. Journal of Hydrology, 448, 80-92.

Berg, P., Bosshard, T., Yang, W., & Zimmermann, K. (2022). MIdASv0. 2.1–MultI-scale bias AdjuStment. Geoscientific Model Development, 15(15), 6165-6180.