The Madden-Julian Oscillation (MJO) is the leading mode of intraseasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intraseasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent.

中文翻译：

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CMIP6 模型中低估的 MJO 变异性


马登-朱利安振荡 (MJO) 是季节内气候变率的主要模式，对多种天气和气候现象产生深远影响。在这里，我们使用基于小波的谱主成分分析 (wsPCA) 来评估 20 个最先进的 CMIP6 模型在捕获 MJO 的幅度和动态方面的技能。通过构建，wsPCA 能够专注于所需频率并利用一个主成分 (PC) 捕获每种传播物理模式。我们表明，在大多数 CMIP6 模型中，MJO 对总季节内气候变率的贡献被大大低估。与 MJO 相关的小波 PC 系列的模量和角频率的联合分布用于相对于通过 Wasserstein 距离的观测对模型进行排序。使用 Hovmöller 相位经度图，我们还表明，在亚马逊流域、西南非洲和海洋大陆的大多数 CMIP6 模型中，与 MJO 相关的降水变化被低估。