Large ensemble assessment of the Arctic stratospheric polar vortex

• Main Authors: Kuchar, Ales, Öhlert, Maurice, Eichinger, Roland, Jacobi, Christoph
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: article; Verlagsveröffentlichung; Arctic
• Online Access: https://doi.org/10.5194/egusphere-2023-1831; https://noa.gwlb.de/receive/cop_mods_00068423; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066851/egusphere-2023-1831.pdf; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1831/egusphere-2023-1831.pdf

The stratospheric polar vortex (SPV) is a phenomenon comprising strong westerly winds during winter in both hemispheres. Especially in the Northern Hemisphere (NH) the SPV is highly variable and is frequently disrupted by sudden stratospheric warmings (SSWs). SPV dynamics are relevant because of both ozone chemistry and its impact on tropospheric dynamics. In this study, we evaluate the capability of climate models to simulate the NH SPV by comparing large ensembles of historical simulations to the ERA5 reanalysis data. For this, we analyze geometric-based diagnostics at 3 pressure levels that describe SPV morphology. Moreover, we assess the ability of the models to simulate SSWs subdivided into SPV split and displacement events. A rank histogram analysis reveals that no model exactly reproduces ERA5 in all diagnostics at all levels. Concerning SPV aspect ratio and centroid latitude, most models are biased to some extent, but the strongest deviations can be found for the kurtosis. Some models underestimate the variability of the SPV area. Assessing the reliability of the ensembles in distinguishing SPV displacement and split events, we find large differences between the model ensembles. In general, SPV displacements are represented better than splits in the simulation ensembles, and high-top models and models with finer vertical resolution perform better. A good performance in representing the geometric-based diagnostics in rank histograms is found to be not necessarily connected to a good performance in simulating displacements and splits. Understanding the biases and improving the representation of SPV dynamics in climate model simulations can help to improve credibility of climate projections, in particular with focus on polar stratospheric dynamics and ozone.