Laboratoire des Sciences du Climat et de l'Environnement LSCE-IPSL, CEA Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, 91191, Gif-sur-Yvette, France.
London Mathematical Laboratory, 8 Margravine Gardens, London, W68RH, UK.
Nat Commun. 2019 Mar 21;10(1):1316. doi: 10.1038/s41467-019-09305-8.
The atmosphere's chaotic nature limits its short-term predictability. Furthermore, there is little knowledge on how the difficulty of forecasting weather may be affected by anthropogenic climate change. Here, we address this question by employing metrics issued from dynamical systems theory to describe the atmospheric circulation and infer the dynamical properties of the climate system. Specifically, we evaluate the changes in the sub-seasonal predictability of the large-scale atmospheric circulation over the North Atlantic for the historical period and under anthropogenic forcing, using centennial reanalyses and CMIP5 simulations. For the future period, most datasets point to an increase in the atmosphere's predictability. AMIP simulations with 4K warmer oceans and 4 × atmospheric CO concentrations highlight the prominent role of a warmer ocean in driving this increase. We term this the hammam effect. Such effect is linked to enhanced zonal atmospheric patterns, which are more predictable than meridional configurations.
大气的混沌性质限制了其短期可预测性。此外，人们对人为气候变化如何影响天气预报的难度知之甚少。在这里，我们通过使用动力系统理论的指标来描述大气环流，并推断气候系统的动力特性，来解决这个问题。具体来说，我们使用百年再分析和 CMIP5 模拟，评估北大西洋大尺度大气环流的次季节可预测性在历史时期和人为强迫下的变化。对于未来时期，大多数数据集都表明大气的可预测性增加。与 4K 更暖的海洋和 4 倍大气 CO2浓度的 AMIP 模拟突出了更暖海洋在推动这种增加方面的重要作用。我们将此称为哈马姆效应。这种效应与增强的纬向大气模式有关，这些模式比经向配置更具可预测性。
