Generation of regional climate ensembles using Atmospheric Forcing Shifting

Authors

  • Romi Sasse,

    Corresponding author
    1. Institute of Meteorology and Climate Research, KIT Karlsruhe, Eggenstein-Leopoldshafen, Germany
    • Correspondence to: R. Sasse, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. E-mail: romi.sasse@kit.edu

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  • Gerd Schädler

    1. Institute of Meteorology and Climate Research, KIT Karlsruhe, Eggenstein-Leopoldshafen, Germany
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ABSTRACT

Ensembles of high-resolution regional climate model (RCM) simulations are crucial for assessing regional climate change and the associated uncertainties. This article presents an RCM ensemble generation technique which explores uncertainties arising from the positioning of synoptic systems in the large-scale atmospheric forcing by shifting the atmospheric fields from global climate model (GCM) runs horizontally. Here, we discuss how the so-called Atmospheric Forcing Shifting (AFS) affects temperature and precipitation over Europe for the period 1980–1984. We use ERA-40 reanalysis data in which the atmospheric fields are shifted to each direction by 25 and 50 km, respectively, to run RCM simulations with COSMO-CLM at 50-km resolution. The analysis of the AFS ensemble includes comparisons with E-OBS observations and COSMO-CLM runs driven by different GCMs. AFS has an evident effect on the spatiotemporal distributions of temperature and particularly precipitation, which is most pronounced during hydrological summer (May to October) when spatial weather patterns are more variable. Furthermore, AFS produces realistic changes in the likelihood and intensity of extreme precipitation. The changes induced by AFS depend strongly on orography, i.e. precipitation increases are likely to occur where moist air masses are shifted towards the windward mountain side and vice versa. Thus, increasing the RCM ensemble spread by means of AFS is a simple and useful method for sampling observed climate statistics and assessing the variability and changes in mean and extreme climate.

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