Simulations of the 2005, 1910 and 1876 Vb cyclones over the Alps – Sensitivity to model physics and cyclonic moisture flux

Abstract

In June 1876, June 1910 and August 2005, northern Switzerland was severely impacted by heavy precipitation and extreme floods. Although occurring in three different centuries, all three events featured very 15 similar precipitation patterns and an extra-tropical storm following a cyclonic, so called Vb trajectory around the Alps. Going back in time from the recent to the historical cases, we explore the potential of dynamical downscaling a global reanalysis product from a grid size of 220 km to 3 km. We use the full, 56-member ensemble provided in the reanalysis and a regional weather model to investigate sensitivities of the simulated precipitation amounts to a set of differing model configurations. These setups are evaluated by combining spatial verification metrics, inter-20 subjective visual inspection and an objective similarity measure. The best-performing model setup, featuring a 1day initialization period and moderate spectral nudging, is then applied to assess the sensitivity of simulated precipitation totals to cyclonic moisture flux along the downscaling steps. The analyses show that cyclone fields and tracks are well defined in the reanalysis ensemble for the 2005 and 1910 cases, while deviations increase for the 1876 case. In the downscaled ensemble, the accuracy of simulated precipitation totals is closely linked to the 25 exact trajectory of the cyclone, with slight shifts producing erroneous precipitation, e.g., due to a break-up of the vortex if simulated too close to the Alpine topography. To reproduce the extreme events, continuous moisture fluxes of >200 kg m -1 s -1 from accurate directions are required. Misplacements of the vortex, in particular for the 1876 case, point to limitations of downscaling from coarse input for such complex weather situations and for the more distant past. On the upside, a well-reasoned selection of reanalysis members for downscaling may be adequate 30 in cases where the driving large-scale features in the atmosphere are well known.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816

“…Among others, this is in line with previous tests for the same region and the distant past (e.g. Stucki et al, 2018Stucki et al, , 2020.…”

“…For Switzerland, gridded daily fields (e.g. of temperature and precipitation) with spatial resolutions as high as 1 km have been created with an analog resampling method (Flückiger et al, 2017;Imfeld et al, 2023;Pfister et al, 2020), and by other statistical approaches including principal component analyses (PCA) of a modern dataset and interpolation of PCA scores from historical station data (Isotta et al, 2019;Stucki et al, 2020). Many of these statistical(-dynamical) reconstructions (or reanalyses) which numerically estimate the state of the atmosphere at a certain point in time involve data assimilation procedures (Carrassi et al, 2018).…”

“…Dynamical downscaling procedures include nesting a limited-area domain from a weather forecast model into the global reanalysis product. This process can then be iterated to refine the global fields of atmospheric variables to local scales (Gómez-Navarro et al, 2018;Michaelis and Lackmann, 2013;Stucki et al, 2015Stucki et al, , 2018Stucki et al, , 2020. In contrast to statistical downscaling procedures, physical processes are inherent to the weather forecast model leading to physically consistent simulations of the weather.…”

Reply on RC2

“…For Switzerland, gridded daily fields (e.g. of temperature and precipitation) with spatial resolutions as high as 1 km have been created with an analog resampling method (Flückiger et al, 2017;Imfeld et al, 2023;Pfister et al, 2020), and by other statistical approaches including principal component analyses (PCA) of a modern dataset and interpolation of PCA scores from historical station data (Isotta et al, 2019;Stucki et al, 2020). Many of these statistical(-dynamical) reconstructions (or reanalyses) which numerically estimate the state of the atmosphere at a certain point in time involve data assimilation procedures (Carrassi et al, 2018).…”

“…Dynamical downscaling procedures include nesting a limited-area domain from a weather forecast model into the global reanalysis product. This process can then be iterated to refine the global fields of atmospheric variables to local scales (Gómez-Navarro et al, 2018;Michaelis and Lackmann, 2013;Stucki et al, 2015Stucki et al, , 2018Stucki et al, , 2020. In contrast to statistical downscaling procedures, physical processes are inherent to the weather forecast model leading to physically consistent simulations of the weather.…”