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Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was widely predicted by seasonal forecasts. We argue that in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via physical understanding of dynamical mechanisms, perturbation experiments to isolate drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence exploit windows of opportunity to issue trustworthy and actionable early warnings.

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.

Initialised climate predictions demonstrate ultra long-range predictability of atmospheric angular momentum, Earth's rotation and length of day. We show how slow, poleward propagating anomalies in the atmospheric angular momentum field allow interannual 'memory', well beyond currently assumed limits of atmospheric predictability. The mechanism involves wave-mean flow interaction between transient eddies and zonal winds in the troposphere and supports the persistence and poleward migration of both positive and negative anomalies. We discuss some of the implications and opportunities this presents for multiyear prediction and show how it leads to new teleconnections that are important for interpreting the observed record of climate variability.

For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners. The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months. In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers. In this presentation we show the skill of the Met Office’s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability. Skill in predicting the UK mean wind speed and storminess peaks in winter (December–February), owing to predictability of the North Atlantic oscillation. In summer (June–August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.

The European North West shelf seas (NWS) support economic and environmental interests of several adjacent populous countries. Forecasts of physical marine variables on the NWS for upcoming months – an important decision-making timescale – would be useful for many industries. However, currently there is no operational seasonal forecasting product deemed sufficient for capturing the high variability associated with shallow, dynamic shelf waters. Here, we identify the dominant sources of seasonal predictability on the shelf and quantify the extent to which empirical persistence relationships can produce skilful seasonal forecasts of the NWS at the lowest level complexity. We find that relatively skilful forecasts of the typically well-mixed Winter and Spring seasons are achievable via persistence methods at a one-month lead time. In addition, incorporating observed climate modes of variability, such as the North Atlantic Oscillation (NAO), can significantly boost persistence for some locations and seasons, but this is dependent on the strength of the climate mode index. However, even where high persistence skill is demonstrated, there are sizeable regions exhibiting poor predictability and skilful persistence forecasts are typically limited to ≈ one-month lead times. Summer and Autumn forecasts are generally less skilful owing largely to the effects of seasonal stratification which emphasises the influence of atmospheric variability on sea surface conditions. As such, we also begin incorporating knowledge of future atmospheric conditions to forecasting strategies. We assess the ability of an existing global coupled ocean-atmosphere seasonal forecasting system to exceed persistence skill and highlight areas where additional downscaling efforts may be needed.

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.

The UK is committed to substantially increasing offshore wind capacity in its drive to decarbonise electricity production and achieve net zero. If low wind episodes – or “wind drought” events – occur during high energy demand periods, energy security may be threatened without alternative supply. To ensure resilience of the power system now and in the coming years as offshore wind generation grows, better understanding of the severity, frequency and duration of low wind episodes would be useful. Variability in winds is likely to dominate over trends in the next few decades, and hence having improved information on present day characteristics of wind drought is valuable.Here we focus our attention on the North Sea as a centre of current and planned offshore wind resource for the UK and a number of other European countries, and on the winter season, given the occurrence of weather patterns that risk security of supply. We use a large ensemble of initialised climate model simulations to provide a synthetic but realistic event set that greatly increases the sample size of extreme events compared with that available from reanalysis data, and gives more robust information about their likelihood and properties. Using the basic unit of a week of low winds as the timescale of analysis, we report on the frequency and duration of wind drought events. In addition, we examine the wider conditions associated with wind drought events to investigate what remote factors may contribute to prolonged wind drought.

The El Niño-Southern Oscillation (ENSO) influences climate on a global scale and is a source of long-range predictability. Accurate modelling of the impact of ENSO requires accurate representation of teleconnections as well as of ENSO itself. We consider a set of CMIP6 models and assess the effect of increasing model resolution on ENSO and its boreal winter teleconnections. The spatial structure, strength and asymmetry of both ENSO and its teleconnection to the extratropical North Pacific are considered. We find evidence of an improved El Niño teleconnection in high resolution models, but this effect is weaker for La Niña. We aim to establish whether ocean or atmospheric resolution is the primary driver of resolution-based trends, and we evaluate the relevance of mean state biases on these trends.

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.

Winter (DJF) 2020/21 in the North Atlantic/European sector was characterised by the negative phase of the North Atlantic Oscillation (NAO). However, this was not well forecast by the leading seasonal prediction systems. We focus on forecasts from GloSea5, which was the Met Office operational seasonal prediction system at the time. Forecasts initialised in November 2020, at the 1-month lead time, indicated that a positive NAO was likely, although a few ensemble members did agree with the eventual outcome. Analysis suggests that the sudden stratospheric warming (SSW) that occurred in early January 2021 and an active MJO in late January/early February 2021 probably contributed to the observed negative NAO. In particular, GloSea5 indicated a rather low probability for SSW activity, which may well have been exacerbated by the forecast of a stronger than observed La Niña by this system.

Within the earth system, glaciers serve as important indicators of climate change, being principally governed by temperature and precipitation. Additionally, they provide essential freshwater storage on various scales, ranging from long-term in firn and ice, to short-term storage in snow cover. By preventing precipitation from immediately turning into runoff, glaciers fulfill a buffering role within their basins, providing downstream runoff during melt season. With changes in glacier mass balance in response to changes in climate, a glacier's buffering capacity is altered simultaneously. In order to predict the evolution of runoff on temporal scales relevant to water resource management (5-15 years), it is essential to observe and simulate glacier mass balance on the same scale. The current research presents a global modelling approach using the Open Global Glacier Model (OGGM), forced with a multi-model, multi-member retrospective ensemble of monthly temperature and precipitation re-forecasts (hindcasts) from the Decadal Climate Prediction Project (DCPP), part of the Coupled Model Intercomparison Project, phase 6 (CMIP6). The decadal hindcasts are initialized each year in the period 1960-2010 and are bias corrected for model drift, while retaining the period's warming trend, using a lead-time based correction. The hindcasts are then downscaled to the glacier scale and used to compute the climatic mass balance with OGGM, with fixed glacier geometries. The method is validated using 274 reference glaciers, which have a >5 year observational record. It is then applied globally, to all land-terminating glaciers in the Randolph Glacier Inventory (RGI), outside the Greenland Ice Sheet and Antarctica. The results indicate merit in using decadal re-forecasts to model glacier mass balance, paving the way for reliable decadal scale runoff predictions on regional and global scales.

Seasonal forecasts of extratropical storms are of interest to the scientific community as well as insurers, government contingency planners and the general public.In previous studies, seasonal forecasts of winter windstorm events over Europe from the Met Office GloSea5 model have shown significant skill especially over north-west Europe for windstorm frequency and were connected to large-scale patterns, i.e., the NAO. Recent investigations show links between windstorm intensities and the three dominant large-scale patterns over Europe (NAO, SCA and EA) which explain up to 80% of interannual windstorm variability.This new investigation quantifies the role of additional, dynamical forcing factors that could influence windstorm predictions. The factor selection is based on known dynamical influences on cyclone development and is thus related to the existence to severe windstorms. We analyse the Eady-Growth-Rate (EGR), 200hPa jet speed and location, a proxy for Rossby wave source (RWS), and one factor related to tropical precipitation. The seasonal forecast skill of the factors themselves shows positive and significant skill in regions they are expected to be most influential or dominant, like for the RWS around its dipole over the south-west of the North Atlantic or for the EGR east of North America.The links between these dynamical forcing factors to windstorm impact-relevant regions in the model and reanalysis data will be presented and the explanatory power of these factors for the overall model skill is discussed.

Modes of climate variability that remotely alter the northern hemisphere stratospheric polar vortex state are explored using the Hadley Centre Climate Model (HadGEM3). Experiments are performed that sample combinations of El Niño—Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) states. These modes were chosen as El Niño and QBO easterly phases are known to weaken the polar vortex.The El Niño induced weakening of the polar vortex is found to be more pronounced during QBO easterly than QBO westerly. Likewise, the polar vortex weakening caused by QBO easterly is stronger during El Niño than during neutral ENSO conditions.It is also found that El Niño induces a change to the QBO itself, namely an increase in the descent rate of the QBO, but this is not large enough to explain the nonlinear response of the polar vortex. Other possible mechanisms are investigated, such as whether the QBO and ENSO teleconnections to the polar vortex are sensitive to the prior state of the polar vortex. Impacts of this nonlinearity on the surface response are also explored.

Operational seasonal forecasts demonstrate an increasingly useful level of skill in predicting extratropical winter climate. However, particularly in and around the Atlantic basin, atmospheric circulation features such as the North Atlantic Oscillation (NAO) exhibit a phenomenon known as the ‘signal-to-noise paradox’; where the ensemble mean correlates more strongly (on average) with observations than individual ensemble members. The paradox may be caused by overestimation of unpredictable internal noise, or by underestimation of the strength of predictable signals. The predictable component of extratropical winter climate is strongly influenced by tropical drivers such as the El Niño-Southern Oscillation. Modelled teleconnections have errors in their phase and amplitude – either or both of which could contribute to the signal-to-noise paradox in the NAO index. We find that the amplitude of the tropospheric ENSO-North Atlantic teleconnection is weaker in the Met Office GloSea5 forecasting system than in observations. This leads to a smaller predictable signal and may therefore contribute to the signal-to-noise paradox. A method of amplitude correction is applied to GloSea5 hindcast data and reduces the signal-to-noise problem for geopotential height predictions in the North Atlantic and North Pacific. A similar method to correct phase errors has little effect.

The decadal time scale (∼1–10 years) bridges the gap between seasonal predictions and longer-term climate projections. It is a key planning time scale for users in many sectors as they seek to adapt to our rapidly changing climate. While significant advances in using initialized climate models to make skillful decadal predictions have been made in the last decades, including coordinated international experiments and multimodel forecast exchanges, few user-focused decadal climate services have been developed. Here we highlight the potential of decadal climate services using four case studies from a project led by four institutions that produce real-time decadal climate predictions. Working in co-development with users in agriculture, energy, infrastructure, and insurance sectors, four prototype climate service products were developed. This study describes the challenge of trying to match user needs with the current scientific capability. For example, the use of large ensembles (achieved via a multisystem approach) and skillfully predicted large-scale environmental conditions, are found to improve regional predictions, particularly in midlatitudes. For each climate service, a two-page “product sheet” template was developed that provides users with both a concise probabilistic forecast and information on retrospective performance. We describe the development cycle, where valuable feedback was obtained from a “showcase event” where a wider group of sector users were engaged. We conclude that for society to take full and rapid advantage of useful decadal climate services, easier and more timely access to decadal climate prediction data are required, along with building wider community expertise in their use. This study received support from the C3S_34c contract (ECMWF/COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by ECMWF. DS, AS, and HT were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. AP, KP, and BF were funded by the Deutscher Wetterdienst. Peer Reviewed "Article signat per 22 autors/es: Nick Dunstone, Julia Lockwood, Balakrishnan Solaraju-Murali, Katja Reinhardt, Eirini E. Tsartsali, Panos J. Athanasiadis, Alessio Bellucci, Anca Brookshaw, Louis-Philippe Caron, Francisco J. Doblas-Reyes, Barbara Früh, Nube González-Reviriego, Silvio Gualdi, Leon Hermanson, Stefano Materia, Andria Nicodemou, Dario Nicolì, Klaus Pankatz, Andreas Paxian, Adam Scaife, Doug Smith, and Hazel E. Thornton"

Seasonal forecasts of global CO₂ concentrations rely on the well-documented relationship with the El Niño–Southern Oscillation (ENSO), combined with estimated anthropogenic emissions. Here, we investigate the skill of the GloSea5 seasonal forecasting system for two carbon cycle processes that underlie the global CO₂–ENSO relationship: the impact of meteorological conditions on CO₂ uptake by vegetation (characterised by net primary productivity, NPP), and on fire occurrences (characterised by fire risk indices). In the tropics, during El Niño events, CO₂ uptake by vegetation is reduced and fires occur more frequently, leading to higher global CO₂ levels. We use the McArthur forest fire index, calculated from daily data from several meteorological variables. We also assess a simpler fire index, based solely on seasonal mean temperature and relative humidity, since seasonal forecasts based on simpler combinations of model output (fewer input variables, averaged over longer periods/larger regions) can be more skillful than more complicated metrics, which retain more noise. For NPP, the skill is high in regions that respond strongly to ENSO, such as equatorial South America in boreal winter, and northeast Brazil in boreal summer. There is also skill in some regions without a strong ENSO response. For the fire risk indices, there is significant skill across large parts of the tropics, including in Indonesia, southern and eastern Africa, and parts of the Amazon Basin. We relate this skill to the underlying meteorological variables, finding that fire risk in particular follows similar patterns to relative humidity. On the seasonal-mean timescale, the McArthur index offers no benefits over the simpler fire index: they show the same relationship to burnt area and response to ENSO, and the same levels of skill, in almost all cases. Our results highlight potentially useful prediction skill, as well as important limitations, for seasonal forecasts of land-surface impacts of climate variability.

Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is sometimes lower in models than observations, especially for seasonal forecasts of the North Atlantic Oscillation and multiyear forecasts of North Atlantic temperature and pressure. In these cases the forecasts are underconfident, with each ensemble member containing too much noise. Consequently, most deterministic and probabilistic measures underestimate potential skill and idealized model experiments underestimate predictability. However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here.

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"