The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard, categorical, regime assignment. That is, 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 probabilistic regime assignment using Bayes theorem. Bayes theorem tells us that the probability of 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 then is 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:在研究大气环流制度及其动态时,标准的方法是使用硬性的、绝对的系统分配,即将每个大气状态指定给最接近距离的管理制度,然而,这不一定总是最适当的办法,因为由于噪音,制度分配可能因与制度距离的微小偏差而受到影响。为了减轻这种偏差,我们利用拜斯理论来发展一种概率性制度分配办法。拜斯理论告诉我们,一个制度提供数据的概率可以通过将气候可能性与先前的信息结合起来来确定。当时的制度概率t可用于在时间 t+1上通报先前的概率,然后用于连续更新制度概率。我们采用这一办法对数据进行再分析,并采用季节性交错组合,同时纳入对制度之间过渡概率的了解。此外,利用该套内存在的信号,更好地为先前的概率提供信息,从而能够确定更明显的年度间变异性。在冬季北大西洋环流制度内部发现的强烈的信号,即采用直线式回归法,在冬季北大西洋环流制度期间,以最强烈的信号评估。