Using Bayesian Networks to Investigate the Role of Arctic Variability in Midlatitude Circulation

Abstract

Recent enhanced warming and sea ice depletion in the Arctic have been put forward as potential drivers of severe weather in the midlatitudes. Evidence of a link between Arctic warming and midlatitude atmospheric circulation is growing, but the role of Arctic processes relative to other drivers remains unknown. Arctic-midlatitude connections in the North Atlantic region are particularly complex but important due to the frequent occurrence of severe winters in recent decades. Here, Dynamic Bayesian Networks with hidden variables are introduced to the field to assess their suitability for teleconnection analyses. Climate networks are constructed to analyse North Atlantic circulation variability at 5-day to monthly timescales during the winter months of the years 1981-2018. The inclusion of a number of Arctic, midlatitude and tropical variables allows for an investigation into the relative role of Arctic Amplification as a driver compared to internal atmospheric variability and other remote drivers. A robust covariability between regions of amplified Arctic warming and two definitions of midlatitude circulation is found to occur entirely within winter at submonthly timescales. Hidden variables incorporated in networks capture periodic shifts between average and anomalously slow stratospheric polar vortex flow. An increase in predictive skill is achieved with the inclusion of hidden variables, but a number of caveats to their usage are demonstrated. The influence of the Barents-Kara Seas region on the North Atlantic Oscillation is found to be the strongest link at 5- and 10-day averages, whilst the stratospheric polar vortex strongly influences jet variability on monthly timescales.