Abstract
Abstract. The coarse grid spacing of global circulation models necessitates the application of downscaling techniques to investigate the local impact of a changing global climate. Difficulties arise for data-sparse regions in complex topography, as they are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high-quality observational data. The Intermediate Complexity Atmospheric Research (ICAR) model is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multiyear precipitation time series generated with ICAR on a 4×4 km2 grid for the South Island of New Zealand for an 11-year period, ranging from 2007 to 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, 11 of which are situated in the Southern Alps between 700 and 2150mm.s.l (m m.s.l refers to meters above mean sea level). ICAR is assessed with standard skill scores, and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving the median of mean squared errors (MSEs) by 30 % and up to 53 %. It performs similarly during all seasons with a MSE minimum during winter, while flow linearity and atmospheric stability are found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational calibration or bias correction techniques. Based on these findings ICAR shows the potential to generate downscaled fields for long-term impact studies in data-sparse regions with complex topography.
Highlights
Global circulation models (GCM) generate atmospheric data sets on spatiotemporal grids that, especially in complex topography, are too coarse to investigate the local impact of a changing global climate
While above the ocean no data are available for the virtual climate station gridded daily rainfall product (VCSR), the results clearly show that Intermediate Complexity Atmospheric Research (ICAR) is able to generate precipitation with seasonal variation above the ocean where no topography is present (Fig. 5f–j)
The model top leading to the smallest average mean squared errors (MSEs) of ICARCP over all alpine weather stations was determined with a sensitivity study at 4 km above topography
Summary
Global circulation models (GCM) generate atmospheric data sets on spatiotemporal grids that, especially in complex topography, are too coarse to investigate the local impact of a changing global climate. J. Horak et al.: Assessing the added value of ICAR lematic, as soon as observation-based training or calibration is applied, the assumption of stationarity is introduced for statistical downscaling, which may not hold under a changing climate (Maraun, 2013; Gutmann et al, 2012). Horak et al.: Assessing the added value of ICAR lematic, as soon as observation-based training or calibration is applied, the assumption of stationarity is introduced for statistical downscaling, which may not hold under a changing climate (Maraun, 2013; Gutmann et al, 2012) Overall, both classes are not ideally suited for the longterm study of the regional effects of a changing global climate. Processbased glacier models require long-term information about the state of the atmosphere above the glacier to investigate the impact of a changing global climate
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