A new region-aware bias correction method for simulated precipitation in the Alpine region

Juan José Gómez-Navarro, Christoph C. Raible, Denica Bozhinova, Olivia Martius, Juan Andrés García-Valero, Juan Pedro Montávez
2018 Geoscientific Model Development Discussions  
Regional climate modelling is used to better capture the hydrological cycle, which is fundamental for climate impact investigations. However, the output of these models is affected by biases that hamper its direct use in impact modelling. Here, we present and evaluate the performance of two high-resolution (2 km) climate simulations of precipitation in the Alpine region and develop a new bias correction technique for precipitation suitable for complex topography. The latter is based
more » ... n quantile mapping, which is applied separately across a number of non-overlapping regions defined through cluster analysis. This technique allows removing prominent biases while it aims at minimising disturbances to the physical consistency of the simulation. <br><br> The simulations span the period 1979&amp;ndash;2005 and are carried out with the Weather Research and Forecasting model (WRF), driven by the reanalysis ERA-Interim (hereafter WRF-ERA), and the Community Earth System Model (hereafter WRF-CESM). The simulated precipitation is in both cases validated against observations. In a first step, Switzerland is classified into regions of similar temporal variability of precipitation. Similar spatial patterns emerge in all datasets, with a clear Northwest-Southeast separation following the main orographic features of this region. The daily evolution and the annual cycle of precipitation in WRF-ERA closely reproduces the observations. This is in contrast to WRF-CESM, which shows a different seasonality with peak precipitation in Winter and not in Summer as in the observations or in WRF-ERA. The application of the new bias correction technique minimises systematic biases in the WRF-CESM simulation, and substantially improves the seasonality, while the temporal and physical consistency among simulated variables is preserved.
doi:10.5194/gmd-2017-329 fatcat:5aekoicg6bhbzerkulviwrk2lq