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The standard approach when studying atmospheric circulation regimes and theirdynamics is to use a hard regime assignment, where each atmospheric state isassigned to the regime it is closest to in distance. However, this may notalways be the most appropriate approach as the regime assignment may beaffected by small deviations in the distance to the regimes due to noise. Tomitigate this we develop a sequential probabilistic regime assignment usingBayes Theorem, which can be applied to previously defined regimes andimplemented in real time as new data become available. Bayes Theorem tells usthat the probability of being in a regime given the data can be determined bycombining climatological likelihood with prior information. The regimeprobabilities 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. Weapply this approach to both reanalysis data and a seasonal hindcast ensembleincorporating knowledge of the transition probabilities between regimes.Furthermore, making use of the signal present within the ensemble to betterinform the prior probabilities allows for identifying more pronouncedinterannual variability. The signal within the interannual variability ofwintertime North Atlantic circulation regimes is assessed using both acategorical and regression approach, with the strongest signals found duringvery strong El Ni\~no years.

A Bayesian Approach to Atmospheric Circulation Regime Assignment