A Bayesian Approach to Atmospheric Circulation Regime Assignment

Abstract

Abstract 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’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s 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 Niño years. Significance Statement Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.