The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes Theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes Theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time $t$ can be used to inform the prior probabilities at time $t+1$, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Ni\~no years.

翻译：研究大气环流型及其动力学时的标准方法是使用硬性归类，即将每个大气状态归入距离最近的环流型。然而，由于噪声导致与环流型距离的微小偏差可能影响归类结果，这或许并非最适方法。为缓解此问题，我们基于贝叶斯定理开发了一种序贯概率性环流型归类方法，该方法可应用于预先定义的环流型，并能在获取新数据时实时实现。贝叶斯定理指出，给定数据时处于某环流型的概率可通过结合气候学似然与先验信息确定。时间$t$的环流型概率可用于构建时间$t+1$的先验概率，进而序贯更新环流型概率。我们将此方法应用于再分析数据及季节后报集合，并融入环流型间转移概率的先验知识。此外，利用集合内部信号优化先验概率，可识别更显著的年际变率。采用分类法和回归法评估冬季北大西洋环流型年际变率中的信号，发现最强信号出现在强厄尔尼诺年份。