Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography

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 <span classCombining double low line"inline-formula"4×4 km²/span grid for the South Island of New Zealand for an 11-year period, ranging from span classCombining double low line"inline-formula"2007/span to span classCombining double low line"inline-formula"2017/span. It focuses on complex topography and evaluates ICAR at span classCombining double low line"inline-formula"16/span weather stations, 11 of which are situated in the Southern Alps between span classCombining double low line"inline-formula"700/span and span classCombining double low line"inline-formula"math xmlnsCombining double low line"http://www.w3.org/1998/Math/MathML" idCombining double low line"M6" displayCombining double low line"inline" overflowCombining double low line"scroll" dspmathCombining double low line"mathml"mrowmn mathvariantCombining double low line"normal"2150/mnmspace linebreakCombining double low line"nobreak" widthCombining double low line"0.125em"/mrow classCombining double low line"unit"mi mathvariantCombining double low line"normal"m/mimspace widthCombining double low line"0.125em" linebreakCombining double low line"nobreak"/mi mathvariantCombining double low line"normal"m/mimo./momi mathvariantCombining double low line"normal"s/mimo./momi mathvariantCombining double low line"normal"l/mi/mrow/mrow/mathspansvg:svg xmlns:svgCombining double low line"http://www.w3.org/2000/svg" widthCombining double low line"61pt" heightCombining double low line"10pt" classCombining double low line"svg-formula" dspmathCombining double low line"mathimg" md5hashCombining double low line"bccda4f3ebd8779f3fffbd3c5ab68286"svg:image xmlns:xlinkCombining double low line"http://www.w3.org/1999/xlink" xlink:hrefCombining double low line"hess-23-2715-2019-ie00001.svg" widthCombining double low line"61pt" heightCombining double low line"10pt" srcCombining double low line"hess-23-2715-2019-ie00001.png"//svg:svg/span/span (m&thinsp;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 span classCombining double low line"inline-formula"4 km/span 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 span classCombining double low line"inline-formula"30 %/span and up to span classCombining double low line"inline-formula"53 %/span. 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.