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

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.

A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1

“…13). The resulting value of Z min is found at 15.2 km, which is in stark contrast to the value of 4.4 km in Horak et al (2019). This indicates that determining the optimal model top elevation solely by comparing simulation output to measurements may lead to an incorrect result.…”

“…If w Nz+1/2 < w Nz−1/2 persists for more than one time step, the concentration of the quantity in the topmost vertical level will continue to increase until it is redistributed within the domain via advection or conversion into other microphysics species. As observed by Horak et al (2019), this influx of additional water therefore may cause numerical artifacts such as the formation of spurious clouds.…”

“…Furthermore, the procedure to identify the lowest possible model top elevation Z min , as described in Sect. 3.5, is applied to this real case scenario and the result compared to the optimal model top elevation of 4 km found by Horak et al (2019) for this region. In their study the model top elevation was chosen as the elevation that led to the lowest mean squared errors between simulated and measured 24-h accumulated precipitation for eleven sites in the Southern Alps.…”

“…However, for instance, precipitation induced by convection or enhanced by non-linearities in the wind field is not considered by ICAR but may be accounted for with other methods (e.g. Jarosch et al, 2012;Horak et al, 2019). For such cases Schlünzen (1997) advises that a model has to be assessed with respect to its limit of application.…”

“…Bernhardt et al (2018) applied ICAR to study changes in precipitation patterns in the European Alps in dependence of the chosen microphysics scheme. Horak et al (2019) evaluated ICAR for the South Island of New Zealand based on multi-year precipitation time series from weather station data and diagnosed the model performance with respect to season, atmospheric background state, synoptic weather patterns and the location of the model top. By comparing to measurements, Horak et al (2019) observed a strong dependence of the performance of ICAR on the location of the model top, finding an optimal setting of 4.0 km above topography that minimized the mean squared errors calculated at all weather stations.…”