Skilful seasonal hydrological forecasts are beneficial for water resources planning and disaster risk reduction. The UK Hydrological Outlook (UKHO) provides river flow and groundwater level forecasts at the national scale. Alongside the standard Ensemble Streamflow Prediction (ESP) method, a new Historic Weather Analogues (HWA) method has recently been implemented. The HWA method samples within high resolution historical observations for analogue months that matches the atmospheric circulation patterns forecasted by a dynamical weather forecasting model. In this study, we conduct a hindcast experiment using the GR6J hydrological model to assess where and when the HWA method is skilful across a set of 314 UK catchments for different seasons. We benchmark the skill against the standard ESP and climatology forecasts to understand to what extent the HWA method represents an improvement to existing forecasting methods. Results show the HWA method improves river flow forecasts most notably in winter, with skilful winter river flow forecasts now possible across the UK compared to the standard ESP method where skilful forecasts were only possible in southeast England. Winter river flow forecasts using the HWA method were also more skilful in discriminating high and low flows across all regions. Catchments with the greatest improvement tended to be upland, fast responding catchments with limited catchment storage and where river flow variability is strongly tied with climate variability. Skilful winter river flow predictability was possible due to relatively high forecast skill of winter atmospheric circulation patterns and the ability of the HWA method to derive high resolution meteorological inputs suitable for catchment hydrological modelling. However, skill was not uniform across different seasons. Improvement in river flow forecast skill for other seasons was modest, such as moderate improvements in northern England and northeast Scotland during spring and little change in autumn. Skilful summer flow predictability remains possible only for southeast England and skill scores at some catchments were reduced compared to the ESP method. This study demonstrates that the HWA method can leverage both climate information from dynamical weather forecasting models and the influence of initial hydrological conditions. An incorporation of climate information improved winter river flow predictability nationally, with the advantage of exploring historically unseen weather sequences. The strong influence of initial hydrological conditions contributed to retaining year-round forecast skill of river flows in southeast England. Overall, this study provides justification for when and where the HWA method is more skilful than existing forecasting approaches and confirms the standard ESP method as a “tough to beat” forecasting system that future improvements should be tested against.

UK winter 2023/24 had a particularly stormy start, similar to 2015/16, another strong El Ni & ntilde;o winter. Successfully predicted by the Met Office seasonal forecast system, this increased early-winter storminess was associated with lower-than-average surface pressure to the west of the UK. Although late winter during El Ni & ntilde;o is often associated with the negative phase of the North Atlantic Oscillation (NAO), in 2023/24 the NAO was near-neutral in observations and weakly positive in the ensemble mean forecast, the late-winter Atlantic surface pressure pattern in the forecast resembling the response observed during the very strongest El Ni & ntilde;o events. Both observations and the forecast showed relatively higher pressure west of the UK, but north-west Europe and the southern North Atlantic were more cyclonic than forecast. The forecast ensemble mean stratospheric polar vortex (SPV) was relatively strong, and unusually, the mean of ensemble members which had a sudden stratospheric warming (SSW) did not project strongly onto the negative NAO, analysis suggesting tropospheric teleconnections masked the usual SSW impact. Conversely, the observed SPV was weak; however, a mid-January SSW had little surface impact. Overall, our analysis suggests that, despite some differences in late winter, El Ni & ntilde;o was the principal driver of observed conditions in the North Atlantic-European sector.

AbstractThe boreal winter of 2022/23 was notable as a third consecutive winter in which La Niña had an influence on the European weather. The GloSea6 seasonal forecast system predicted a blocked circulation pattern in the North Atlantic in early winter (December), and then a transition through mid‐winter (January) into a more zonal pattern in late winter (February), consistent with the canonical La Niña teleconnection pattern seen previously. The seasonal forecast for the UK was an increased likelihood of near average temperatures, and drier‐ and calmer‐than‐average conditions. Both the predicted broad‐scale circulation patterns and UK winter mean weather conditions verified well against observations, and we show that seasonal forecasts of the North Atlantic Oscillation (NAO) over the last 10 winters show similar skill to previously reported hindcasts. Throughout the winter, the Madden–Julian Oscillation (MJO) was particularly active. On three occasions, it exhibited strong phases 6 and 7. There was also a sudden stratospheric warming (SSW) that occurred on 16th February. This was followed by colder conditions and associated impacts similar to the canonical negative NAO response over the UK, although the main impact fell in March and so did not affect the winter (December–January–February) mean conditions.

The Northern Hemisphere winter of 2021/2022 exhibited a positive North Atlantic Oscillation (NAO) which led to largely mild and wet conditions for Northern Europe. A moderate La Niña in the tropical Pacific and a stronger than average stratospheric polar vortex together explained the observed anomalies over the winter. Winter 2021/2022 was well predicted in general by seasonal forecast systems. The ensemble mean indicated a positive winter NAO and the forecast spread of forecasts from the Met Office GloSea6 seasonal prediction system spanned the observed mean sea level pressure anomaly for the whole winter and the individual months. However, December showed the largest departure from the mean of the forecast which is consistent with evidence from previous work that early winter ENSO teleconnections are too weak in model predictions. Nevertheless, around one in four members captured the negative NAO pattern in December. The strong pressure gradient and positive NAO predicted for the latter part of the winter allowed successful warning of the possibility of above average storminess and strong winds which occurred in February 2022. This is potentially useful information for the energy sector who increasingly rely on wind power and the insurance industry for warning of storm damage.

Recent studies have shown that seasonal forecasting systems have significant skill in predicting northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the United Kingdom (UK), we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic Oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.

<p>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.</p>

Abstract Boreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over‐predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.

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.

Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

The European winters of 2017-18 and 2018-19 were not climatically extreme, but both winters had a major sudden stratospheric warming (SSW). In February 2018, an SSW led to an intense cold outbreak across Europe and further spells of cold weather in March. The SSW of January 2019, although well predicted and expected to increase the chance of a cold end to winter, apparently produced little impact. In this study, we examine the performance of the Met Office seasonal prediction system in these winters, and the influences that led to these outcomes. To achieve this latter objective, sets of numerical experiments are performed in which the tropical troposphere and the extratropical stratosphere are relaxed towards their observed state, allowing the influence of each on the North Atlantic-European atmospheric circulation to be identified. Using these experiments, we show that the SSWs had similar impacts in each case, creating a signal of easterly surface wind anomalies in the weeks following the event. In contrast, tropical influences were opposite in the two winters, acting to strengthen the easterly signal at the end of February 2018 and opposing it in January 2019. The different apparent responses to the two events therefore came about largely through tropical tropospheric variability. Furthermore, we highlight the importance of a very strong cycle of the Madden-Julian Oscillation (MJO) in late January and early February 2018 as an important driver for the February 2018 SSW. MJO teleconnections appear to have been critical in creating the large mid-latitude wave 2 amplitude that has been identified as the immediate cause of this event.

Recent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of 1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores of 0.8 (1-day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

Different methods to increase the skill of hydro-meteorological variables over Europe in operational forecasts are investigated. In particular, a method to capitalize on skillful NAO forecasts based on analogues and various techniques to create an ensemble are investigated at the seasonal timescale. At the same timescale, the skill at the scale of a basin catchment over Crete is also investigated. Furthermore, the skill of sub-seasonal forecasts over Europe and the use of integrated vapour transport to improve long-range weather forecast are also investigated. Whilst the results vary between studies, the skill associated with precipitation generally remains low.

In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events.

Pete Falloon1*, David Fereday1, Nicky Stringer1, Karina Williams1, Jemma Gornall1, Emily Wallace1, Rosie Eade1, Anca Brookshaw1, Joanne Camp1, Richard Betts1, Rutger Dankers1, Kathryn Nicklin2, Michael Vellinga1, Richard Graham1, Alberto Arribas1 and Craig MacLachlan1 1Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK 2School of Earth and Environment, The University of Leeds, Leeds, LS2 9JT, UK