Original paper
A new combined statistical method for bias adjustment and downscaling making use of multi-variate bias adjustment and PCA-driven rescaling
Krähenmann, Stefan; Haller, Michael; Walter, Andreas
Meteorologische Zeitschrift Vol. 30 No. 5 (2021), p. 391 - 411
66 references
published: Oct 22, 2021
published online: Jul 15, 2021
manuscript accepted: Mar 29, 2021
manuscript revision received: Mar 26, 2021
manuscript revision requested: Nov 30, 2020
manuscript received: Sep 22, 2020
Open Access (paper may be downloaded free of charge)
Abstract
One major concern of climate modeling is the projection of future extreme events as they have the most severe impact on society and environment. This is a challenging task for modeling and due to the low occurrence rate of extreme values. Furthermore, the local-scale characteristics of extreme events demand for high resolution model data. In the framework of the EURO-CORDEX initiative, climate model ensemble data on 0.11° grid resolution are produced. In order to provide climate data on a higher resolution in an efficient and reliable way, a statistical downscaling method has been developed, which combines bias adjustment and downscaling. With this method, an ensemble of climate model data on a target resolution of 5 km has been built and it was established as a reference ensemble for Germany. The ensemble consists of the three scenarios RCP 2.6, 4.5 and 8.5, 44 members in total. The method is comprehensible and of minimum complexity. It involves objective predictor selection and it can be applied for different areas, horizontal resolutions, target variables and predictor data sets, and, thus, providing high flexibility. While the methodology imposes refined structures onto modeled data, it does not affect the models data range and therefore allows for extrapolation beyond observed values. The raw model data show for threshold-based indices a rather large spread and bias, which was tremendously improved in the bias adjustment step. Downscaling is challenging as local terrain features can introduce unpredictable residual variation without localized information from e.g. observations. In particular, temperature-based extreme values were well captured by the downscaling algorithm, as temperature is strongly elevation dependent.
Keywords
bias adjustment • statistical downscaling • climate modeling • evaluation • extreme events