Kolbe Marlen, Torres Alavez Jose Abraham, Mottram Ruth, Bintanja Richard, van der Linden Eveline C, Stendel Martin
Faculty of Science and Engineering, Energy and Sustainability Research Institute Groningen, University of Groningen, Groningen, Netherlands.
National Centre for Climate Research, Danish Meteorological Institute, Copenhagen, Denmark.
Discov Atmos. 2025;3(1):4. doi: 10.1007/s44292-025-00026-w. Epub 2025 Mar 17.
There is increasing evidence that atmospheric rivers (ARs) drive extreme precipitation and melt events across Antarctica and that these impacts are more accurately captured in high-resolution models. However, a comprehensive evaluation of AR impacts, comparing the performance of models with varying resolutions and physics across multiple AR events, has not yet been conducted. In this study, we simulate four recent AR events using the regional climate model HCLIM43 in its ALADIN (11 km) and AROME (11 km and 2.5 km) configurations, as well as ERA5 (31 km) and MERRA-2 (50 km), to analyze the dominant factors driving melt and precipitation and how spatial resolution and model physics affect surface impacts compared to observations. The events include intense snowfall and longwave radiation (Jun 2019), surface melt from foehn winds (Feb 2020), a large-scale heat anomaly driven by radiative and turbulent processes (Mar 2022), and inland surface warming after moisture is released by sea ice and ice shelves (Dec 2023). While all reanalyses and models underestimate surface warming and melt during these events, the high-resolution 2.5 km AROME configuration tends to simulate the most realistic precipitation and melt extents, largely due to its improved representation of foehn effects and reduced cloud biases. Longwave radiation generally dominates AR-induced warming, particularly over wider inland regions, while sensible heat fluxes are dominant in coastal and foehn-prone regions. Lastly, substantial differences among models/reanalyses in cloud phase and total cloud water paths underscore the need for improved cloud parameterizations and surface energy budget calculations.
The online version contains supplementary material available at 10.1007/s44292-025-00026-w.
越来越多的证据表明,大气河流(ARs)驱动了南极洲各地的极端降水和融化事件,并且这些影响在高分辨率模型中能得到更准确的体现。然而,尚未对AR的影响进行全面评估,即比较不同分辨率和物理过程的模型在多个AR事件中的表现。在本研究中,我们使用区域气候模型HCLIM43的ALADIN(11公里)和AROME(11公里和2.5公里)配置,以及ERA5(31公里)和MERRA - 2(50公里),模拟了最近的四次AR事件,以分析驱动融化和降水的主要因素,以及与观测相比,空间分辨率和模型物理过程如何影响地表影响。这些事件包括强降雪和长波辐射(2019年6月)、焚风引起的地表融化(2020年2月)、由辐射和湍流过程驱动的大规模热异常(2022年3月),以及海冰和冰架释放水分后的内陆地表变暖(2023年12月)。虽然所有再分析和模型在这些事件中都低估了地表变暖和融化,但高分辨率的2.5公里AROME配置往往能模拟出最逼真的降水和融化范围,这主要归功于其对焚风效应的改进表示和减少的云偏差。长波辐射通常在AR引起的变暖中占主导地位,特别是在更广阔的内陆地区,而感热通量在沿海和易出现焚风的地区占主导地位。最后,模型/再分析在云相和总云水路径方面的显著差异突出了改进云参数化和地表能量收支计算的必要性。
在线版本包含可在10.1007/s44292 - 025 - 00026 - w获取的补充材料。