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It is well established that the positive phase of El Niño Southern Oscillation (ENSO) tends to weaken the Northern Hemisphere stratospheric polar vortex (SPV), promoting a negative North Atlantic Oscillation (NAO). Pacific Decadal Variability (PDV) is characterised by a pattern of sea surface temperatures similar to ENSO, but its impacts are more uncertain: some studies suggest similar impacts of ENSO and PDV on the SPV and NAO, while others find the opposite. We use climate model experiments and reanalysis to find further evidence supporting opposite interannual and decadal impacts of Pacific variability on the extratropics. We propose that the decadal strengthening of the SPV in response to positive PDV is caused by a build-up of stratospheric water vapour leading to enhanced cooling at the poles, an increased meridional temperature gradient and a strengthened extratropical jet. Our results are important for understanding decadal variability, seasonal to decadal forecasts and climate projections.

There is growing use of machine learning algorithms to replicate sub-grid parametrisation schemes in global climate models. Parametrisations rely on approximations, thus there is potential for machine learning to aid improvements. In this study, a neural network is used to mimic the behaviour of the non-orographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability. The neural network is found to require only two of the six inputs used by the parametrisation scheme, suggesting the potential for greater efficiency in this scheme. Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be trained based on emergent features of the coupled system with minimal computational cost, and providing a test bed prior to coupling to a climate model. A climate model simulation, using the neural network in place of the existing parametrisation scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the non-orographic gravity wave variability associated with the El Nino Southern Oscillation and stratospheric polar vortex variability. These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

Pacific Decadal Variability (PDV) is characterised by a pattern similar to the interannual El Niño Sothern Oscillation (ENSO), with anomalously warm sea surface temperatures (SSTs) in the tropical Pacific surrounded by a horseshoe of cool SSTs. PDV is one of the most influential decadal climate modes, contributing to the ‘slowdown’ of global temperature rise at the beginning of the 21st century and affecting precipitation extremes worldwide. However, differences between climate impacts of PDV and ENSO are more uncertain.Here, the interannual and decadal impacts of PDV are investigated using ERA5 re-analyses and forced Pacific experiments from the CMIP6 Decadal Climate Prediction Project (DCPP) which impose the PDV SST pattern in the coupled Met Office climate model (HadGEM3.1). Both model and observations show a decreased Pacific Walker circulation and an increased Hadley response which combine to generate large scale Rossby waves, impacting global atmospheric circulation and precipitation patterns.Impacts on the winter North Atlantic Oscillation (NAO) are opposite on the two timescales in both the model and observations. On interannual timescales a positive PDV produces a weakening of the stratospheric polar vortex and a negative NAO, whereas on decadal timescales a strengthening of the polar vortex and positive NAO is seen. This appears to be related to the balance between an increased meridional temperature gradient which tends to shift the jet poleward on decadal timescales, and increased upward propagation of planetary waves which reduces the polar vortex and shifts the jet equatorward on interannual timescales. Reasons for these differences will be explored further.