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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.

The process of engaging in arts-based research is unique; it draws upon the creative essence of the researcher to work with artistic forms which carry intangible information that is perhaps unknowable by other means. In this process, the researcher is engaged wholly; all faculties of the person are drawn into the artistic world. This article explores the experiences of two music therapists conducting arts-based research studies, weaving together distinct narratives with common themes. The reader is taken along the journey of two separate music therapy research projects: one whose participants are a group of music therapists, one whose participant is a child living with mental health challenges. Thinking retrospectively, the researchers discuss links between their personal artistry and the arts-based research process, exploring issues such as trust, creativity, and the credibility of information carried in artistic media. Visual art, musical excerpts and creative writing are included. By exploring the professional and personal journeys as music therapists in the arts-based research process we highlight the strengths and challenges of this approach that shaped our studies and gave light to emergent understandings through the arts.

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future. LH and AS were supported by the EUCP project funded by the European Commission’s Horizon 2020 programme, Grant Agreement 776613 and supported by the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. AS, DS, and MS were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. The EC-Earth simulations at SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC and NSC and have been performed as part of the NordForsk-funded Nordic Centre of Excellence project (Award 76654) Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH). SY and TT were supported by the ARCPATH (NordForsk Award 76654) and the Danish state-funded National Centre for Climate Research [Nationalt Center for Klimaforskning (NCFK)]. SY was also partly supported by the EUCP project (Horizon 2020 Grant Agreement 776613). The EC-Earth simulations at BSC were performed using resources from the Red Española de Supercomputación. HP and WM received funding from the German Federal Ministry of Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). Takahito Kataoka and Hiroaki Tatebe were supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. MIROC simulations were performed on the Earth Simulator at JAMSTEC. NK, YW, FC, and IB were supported by the Trond Mohn Foundation (Grant BFS2018TMT01; Bjerknes Climate Prediction Unit). Peer Reviewed "Article signat per 45 autors/es: Leon Hermanson, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav V. Kharin, William J. Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicoli, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam Scaife, Mark Collier, Terence O’Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Tom Delworth, Xiaosong Yang, Fanrong Zeng, Yiguo Wang, François Counillon, Noel Keenlyside, Ingo Bethke, Judith Lean, Jürg Luterbacher, Rupa Kumar Kolli, and Arun Kumar"