How to cite: Yan, X. and Tang, Y.: Seasonal prediction of North Atlantic sea surface temperature anomalies using the LSTM machine learning method , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-153, https://doi.org/10.5194/egusphere-egu25-153, 2025.

The analogue method is a powerful and efficient tool for climate predictions, particularly in regions like the North Atlantic, where impacts of climate change have been relatively modest. While climate projections effectively estimate global mean surface temperature trends over a century, decadal trends in the North Atlantic diverge from the global trend. Here, we leverage on the similar evolution of analogous patterns on a decadal time scale by comparing SST patterns in observed data with patterns from an existing simulation ensemble. We apply this method to ten-year SST trend reconstructions in the North Atlantic using the MPI CMIP6 grand ensemble. In addition, we assess the impact of volcanic eruptions on the quality of the SST trend reconstruction for the time period 1960-2019. We also provide a prediction for 2020–2029. We find that the analogue method delivers high correlation of SST trend reconstructions with observed trends for the MPI CMIP6 grand ensemble. Volcanic influence can be accounted for by trimming the time series to those times unaffected by volcanic eruptions, which results in a higher correlation. Our results suggest that the decadal predictions of SST trends might also be achieved without the need for new, computationally expensive simulations.

How to cite: Heyl, L., Brune, S., and Baehr, J.: Predicting North Atlantic Temperature Trends with the Analogue Method using the MPI CMIP6 Grand Ensemble, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8980, https://doi.org/10.5194/egusphere-egu25-8980, 2025.

Decadal predictions have advanced greatly in recent years: not only have they become operational worldwide and have been demonstrated to be skillful in various aspects of climate variability, including predicting changes in the atmospheric circulation and in the occurrence of extremes several years ahead, but —as such— they are also being used increasingly in climate services. Climate adaptation and policy making, however, also require climate predictions that go beyond the 10-year horizon. For climate information beyond 10 years into the future, uninitialized climate projections, which completely miss any predictability stemming from internal variability, have been the only available product. Trying to account for this lack of information in climate projections regarding any predictable components of internal variability, methods to constrain climate projections using information from large ensembles of initialized decadal predictions have been developed and have been shown to reduce the uncertainty and increase the skill of climate projections, even beyond the 10-year horizon. The demonstrated benefits of such indirect methods to account for predictable internal variability indicate that the latter remains significant beyond the 10-year limit of decadal predictions. Hence, directly harnessing this predictability through running initialized 20-year predictions emerges as a strategic endeavour.
In this study a novel, multi-system ensemble of initialized extended-decadal predictions is assessed. These predictions consist of a grand ensemble of 71 members derived from 6 forecast systems. They are initialized every 5 years from 1960 onward and run ahead for 20 years. Our analysis uses an elaborate drift- and bias-correction method that accounts for the correct representation of trends. Importantly, we show significant skill against observations for a number of variables (fields and indices), even in the second decade of the forecasts. The origin of such predictability is discussed together with the limitations of these 20-year predictions. The respective experimental protocol was defined in the framework of the ASPECT EU project and has been proposed as a tier-2 Decadal Climate Prediction Project (DCPP) protocol for the Coupled Model Intercomparison Project phase 7 (CMIP7).

How to cite: Nicolì, D., Roncoroni, S., Mueller, W. A., Pohlmann, H., Brune, S., Donat, M., Mahmood, R., Yeager, S., Merryfield, W. J., Sospedra-Alfonso, R., and Athanasiadis, P. J.: Skill assessment of a multi-system ensemble of initialized 20-year predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11024, https://doi.org/10.5194/egusphere-egu25-11024, 2025.

Reducing the uncertainty associated with internal climate variability over the coming decades is crucial, as this time frame aligns with the strategic planning needs of stakeholders in climate-vulnerable sectors. Three sources of information are available: non-initialized ensembles of climate projections, initialized decadal predictions, and observations. Non-initialized ensembles of climate projections span seamlessly from the historical period to the end of the 21st century, encompassing the full range of uncertainty linked to internal climate variability. Initialized decadal predictions aim to reduce uncertainty from internal climate variability by initializing model simulations with observed oceanic states, phasing the simulated and observed climate variability modes. However, they are usually limited to 5 to 10 years, with small added value after a few years, and they are also subject to drift due to the shock from the initialization. Finally, we can also use observations that can provide information to constrain the climate evolution over the next decades. Providing the best climate information at regional scale over the next decades is therefore challenging. Previous methods addressed this challenge by using information from either the observations or the decadal predictions to constrain uninitialized projections. In this study, we propose a new method to make use of the different sources of information available to provide relevant information about near-term climate change with reduced uncertainty related to internal climate variability. First, we select a sub-ensemble of non-initialized climate simulations based on their similarity to observed predictors with multi-decadal signal potential over Europe, such as Atlantic multi-decadal variability (AMV). Then, we further refine this sub-ensemble of trajectories by selecting a subset based on its consistency with decadal predictions. We present a case study focused on predicting near-term future surface temperatures over Europe. To evaluate the effectiveness of this method in providing reliable climate information, we conduct a retrospective analysis over the historical period.

How to cite: Bonnet, R., Boé, J., and Sanchez, E.: Constraining near-term climate projections by combining observations with decadal predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11511, https://doi.org/10.5194/egusphere-egu25-11511, 2025.

El Niño-Southern Oscillation (ENSO) is the leading mode of global climate variability. Through its associated teleconnections, ENSO can impact the climate of numerous regions worldwide at seasonal timescales, highlighting its role as the main source of seasonal predictability. Numerous studies have demonstrated a significant influence of ENSO on the climate of the Euro-Atlantic sector, but the impacts and mechanisms of the teleconnection in early-winter (November-December) remain unclear. Besides, in early-winter, ENSO teleconnections involve tropospheric pathways, which may change in response to different background states of the ocean. Thus, a crucial research question to address is whether the early-winter teleconnection to the Euro-Atlantic sector has changed under the different background states of sea surface temperature (SST) over the Pacific Ocean. 

This work aims to analyse the ENSO early-winter teleconnection to the Euro-Atlantic sector from a nonstationary perspective. Specifically, the teleconnection is analysed under different background states of SST over the Pacific Ocean, related to changes in the phase of the Pacific Decadal Oscillation (PDO). Using observational and reanalysis datasets for the period 1950-2022, results reveal that the tropospheric pathways of the teleconnection change under the different Pacific SST background states, leading to distinct responses of the North Atlantic atmospheric circulation to ENSO. We also confirm that these distinct responses in the North Atlantic entail significantly different impacts of ENSO on the surface climate across Europe, particularly on surface air temperature. Furthermore, the teleconnection is analysed in the SEAS5 state-of-the-art dynamical seasonal prediction model. The analysis within the model is also conducted from a nonstationary perspective, and aims to determine whether the model successfully reproduces a shift in the teleconnection in the late 1990s identified in reanalysis and observations. Results show that the model accurately captures the spatial pattern of the teleconnection impacts across Europe after the late 1990s, but not before. In turn, significant changes in the skill of seasonal forecasts are observed between before and after the late 1990s. However, skill after the late 1990s is just moderate due to a significant underestimation of the teleconnection impacts. 

The results of this study shed light on the nonstationary behaviour of the early-winter teleconnection to the Euro-Atlantic sector and have important implications on seasonal predictability in Europe. Particularly, the nonstationarity of the teleconnection gives rise to the emergence of windows of opportunity for seasonal forecasting, in which forecast skill may be greater than initially expected from a stationary analysis.

How to cite: Fernández-Castillo, P., Losada, T., Rodríguez-Fonseca, B., García-Maroto, D., Mohino, E., and Durán, L.: Multidecadal variability of the ENSO teleconnection to Europe in early-winter and implications for seasonal forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11166, https://doi.org/10.5194/egusphere-egu25-11166, 2025.

Previous studies have identified multi-decadal variations in the skill of winter seasonal forecasts of large-scale climate indices, including ENSO, the PNA, and NAO. Forecast skill is significantly lower in the middle of the 20^th century (1940—1960) than at the start or end of the century. We hypothesise that tropospheric aerosol forcing, which is spatially and temporally heterogeneous and poorly constrained in the hindcasts used in previous studies, contributes to this low skill mid-century period.

This study assesses the sensitivity of ECMWF’s state-of-the-art seasonal forecasting model to tropospheric aerosol forcing, using a newly developed aerosol forcing dataset based on CEDS emissions data. We analyse DJF hindcasts initialised every November from 1925—2010, each with 21 ensemble members. For each year, we run hindcasts with ‘best guess’, doubled, and halved aerosol forcing (perturbing both anthropogenic and natural aerosols). All experiments exhibit similar multi-decadal variability in skill for large-scale climate indices. Aerosol forcing has no significant impact on forecast skill but some impacts on mean biases, suggesting other factors drive the mid-century skill minimum.

Aerosol forcing has large regional impacts. Increasing aerosol forcing leads to cooler 2m temperature and SSTs globally, with amplified cooling in regions with large aerosol forcings, such as northern India and North Africa. Dynamical responses include an ‘anti-monsoon’ circulation over Africa, with a weakening of the trade winds and Atlantic Walker circulation, and local southwards shift of the ITCZ. The magnitude of the response increases when ocean initial conditions are perturbed to represent the cumulative impact of aerosol forcing, suggesting that coupling enhances the atmospheric response.

These results highlight the model’s sensitivity to tropospheric aerosols, with large differences in bias and mean state after four months, despite limited impact on skill. The circulation changes over Africa warrant further investigation, with implications for future aerosol scenarios. Planned experiments will explore the impact in summer and quantify the timescale of the response to aerosols.

How to cite: Wright, M., Weisheimer, A., Woollings, T., Senan, R., and Stockdale, T.:  Investigating the sensitivity of 20th century seasonal hindcasts to tropospheric aerosol forcing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6006, https://doi.org/10.5194/egusphere-egu25-6006, 2025.

For the last 9 years, the Met Office has issued forecasts of the annual increment in atmospheric carbon dioxide measured at Mauna Loa, accounting for both anthropogenic emissions and the effects of El Niño Southern Oscillation (ENSO) on natural carbon sinks and sources. The first forecast was produced when the 2015-2016 El Niño was emerging, and correctly predicted the largest annual CO₂ increment on record at the time. In most years, the inclusion of ENSO provides a more skilful forecast than just considering emissions alone, except for 2022-2023 when La Niña conditions in late 2022 were followed by an early emergence of El Niño conditions in the second quarter of 2023. The impacts of interannual differences in emissions on the CO₂ rise are usually smaller than those of ENSO variability, except in 2020 when the emergence of an unexpected large drop in global emissions due to societal responses to the COVID-19 pandemic required the forecast to be re-issued with a new estimate of the annual profile of emissions. Our forecast methodology also provides a simple means of tracking the changes in anthropogenic contributions to the annual atmospheric CO₂ rise against policy-relevant scenarios. The Met Office forecast for 2023-2024 predicted a relatively large annual CO₂ rise, but the observed rise was even larger, with exceptional wildfires in the Americas a likely contributor to the additional increase. Even without the effects of El Niño and other climatic influences on carbon sinks, the human-driven rise in CO₂ in 2023-2024 would have been too fast to remain compatible with IPCC AR6 scenarios that limit global warming to 1.5°C with little or no overshoot. While the 2024-2025 rise is predicted to be smaller than 2023-2024, it will still be above these 1.5°C scenarios.

How to cite: Betts, R., Jones, C., Knight, J., Kennedy, J., Keeling, R., Jin, Y., Pope, J., and Sandford, C.: Forecasting the annual CO2 rise at Mauna Loa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13668, https://doi.org/10.5194/egusphere-egu25-13668, 2025.

Understanding monthly-to-annual climate variability is essential for improving climate forecast products as well as adapting to future climate extremes. Previous studies show, that European summer climate, including temperature and precipitation extremes, is modulated by hemispheric large-scale circulation patterns, which themselves are connected to Earth system components such as sea surface temperature across temporal scales. Nevertheless, it remains unclear as to how stationary these teleconnections are and if their predictive power is potent across multiple centuries and background climates. By combining d18O isotopes from a European tree ring network with independent paleo-climate reanalyses, we highlight precursors and atmospheric dynamics behind European summer climate over the last 400 years.

We further present evidence that centennial ensemble seasonal climate forecasts capture the causality of the atmospheric
dynamics behind these teleconnections in the 20th century. Our results suggest that tropical sea surface temperature anomalies trigger specific precipitation and diabatic heating patterns which are dynamically connected to extratropical Rossby wave trains and the formation of a circumglobal teleconnection pattern weeks later.

How to cite: Wegmann, M. and Brönnimann, S.: Bridging paleoclimate and seasonal climate prediction: The case of European summer climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3747, https://doi.org/10.5194/egusphere-egu25-3747, 2025.

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year-to-year contributions to multi-model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well-forecast and poorly-forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well-forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi-model seasonal forecasting perspective.

How to cite: Baker, L., Shaffrey, L., Weisheimer, A., and Johnson, S.: Intermittency of seasonal forecast skill for the wintertime North Atlantic Oscillation and East Atlantic Pattern , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5880, https://doi.org/10.5194/egusphere-egu25-5880, 2025.

Climate models suffer from long-standing biases that degrade climate prediction skills. While radically increasing resolution offers promise, we are still many years away from being able to perform operational climate predictions with models that can explicitly resolve the most important physical processes. Here we demonstrate that supermodelling can enhance climate predictions through better using the current generation of models. A supermodel connects different models interactively so that their systematic errors compensate. It differs from the standard non-interactive multi-model ensembles, which combines model outputs a-posteriori. We have developed an ocean-connected Earth System model using NorESM, CESM, and MPIESM in their CMIP5 versions. The model radically improves the simulation of tropical climate, strongly reducing SST and double ITCZ biases. We perform seasonal predictions for the period 1990-2020, initialized through (EnOI) data assimilation of SST. We have performed one forecast per season but are currently extending the ensemble size to ten members. The supermodel shows marked improvement in prediction skill for forecasts started before boreal spring, significantly overcoming the spring predictability barrier. Initial investigation indicates the skill enhancement is connected to better simulation of ocean-atmosphere interaction during the first part of the year, which also leads to improved initial conditions. Our results indicate the importance of better representing the signal-to-noise in the western and central Pacific during boreal spring.

How to cite: Keenlyside, N., Singh, T., Chiu, P.-G., Counillon, F., and Schevenhoven, F.: Overcoming the spring predictability barrier with a supermodel, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12107, https://doi.org/10.5194/egusphere-egu25-12107, 2025.

Intense warm spells, such as heatwaves, can significantly impact human health, the environment, and socio-economic systems. Although heatwaves are typically associated with summer, the occurrence of warm spells during cold seasons can also have profound effects on various sectors. While some effects, such as reduced cold-related mortality, can be considered beneficial, the long-term consequences, e.g. on ecosystems, forests, and agriculture, are concerning. Warm spells during the cold seasons can alter the natural dormancy cycles of plants, causing premature sprouting, flowering, or growth and negatively affecting crop yield and quality. In addition, cold season warm spells can reduce snow accumulation in mountainous regions, potentially affecting downstream water availability. As climate change drives increases in the frequency, intensity, and duration of warm spells, their impacts are becoming more severe and far-reaching. This makes predicting such events a key priority for climate science and risk management.

Climate forecast models offer the potential to predict extreme events like warm spells weeks to months in advance, becoming increasingly relevant for decision-making across various socio-economic sectors. This study examines the predictive skill of the downscaled German Climate Forecast System Version 2.1 (GCFS2.1) for warm spells in Germany on a seasonal scale, encompassing both warm seasons (spring and summer) and cold seasons (autumn and winter).  The analysis relies on hindcast data from the 1991-2020 base period, statistically downscaled to 5 km resolution. It evaluates multiple extreme temperature climate indices, as for example the Warm Spells Duration index, and applies various statistical metrics to assess the predictive skill. The findings reveal high heterogeneity in the ability of the (downscaled) GCFS2.1 to forecast warm spells across seasons, with higher predictive skill during the cold seasons but more limited for the warm seasons.

How to cite: Castino, F., Geiger, T., Pasternack, A., Paxian, A., Dalelane, C., and Kreienkamp, F.: On the predictive skill for warm spells in Germany across seasons , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8904, https://doi.org/10.5194/egusphere-egu25-8904, 2025.

This study investigates the decadal predictability of cold surge frequency (CSF) in East Asia, including Korea, Japan, and Taiwan, through the lens of the North Atlantic Oscillation (NAO) index. The findings suggest that extreme events such as cold surges can be predicted on decadal timescales when the teleconnection mechanism is robustly established. The study revisits and consolidates the dynamical mechanisms underlying wave propagation and the teleconnection between the NAO and the East Asian trough, highlighting their role in creating a winter environment conducive to cold surges in Taiwan and East Asia. The study demonstrates the skill of climate models in capturing the NAO's decadal variability, and develops a statistical-dynamical hybrid approach. This method integrates decadal prediction datasets with a statistical model to enhance the prediction of extreme cold surge occurrences on a multi-annual timescale. The results of the study underscore the scientific significance of merging climate dynamical mechanisms with decadal prediction systems for extreme events, and introduce a hybrid framework that combines numerical decadal climate predictions with statistical regression models. This addresses the challenges posed by biases in climate prediction models and advances the capability to predict regional extreme events such as cold surges.

How to cite: Tseng, W.-L., Wang, Y.-C., Chen, Y.-T., Wang, Y.-H., Hsu, H.-H., and Hong, C.-C.: Robust decadal predictability of cold surge frequency in Taiwan and East Asia through teleconnection of North Atlantic Oscillation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7163, https://doi.org/10.5194/egusphere-egu25-7163, 2025.

Initialized climate predictions are skillful in predicting regional climate conditions in several parts of the globe, but also suffer from different issues arising from imperfect initializations and inconsistencies between the model and the real world climate and processes. In particular, a so-called signal-to-noise paradox has been identified in recent years. This ‘paradox’ implies that the models can predict observations with higher skill than they predict themselves, despite some physical inconsistencies between modeled and real world climate. This is often interpreted as an indicator of model deficiencies.

Here we present a perfect-model decadal prediction experiment, where the predictions have been initialized using climate states from the model's own transient simulation. This experiment therefore avoids issues related to model inconsistencies, initialization shock and the climate drift that affect real-world initialized climate predictions. We find that the perfect-model decadal predictions are highly skillful in predicting the near-surface air temperature and sea level pressure of the reference run on decadal timescales. Interestingly, we also find signal-to-noise issues, meaning that the perfect-model reference run is predicted with higher skill than any of the initialized prediction members with the same model. This suggests that the signal-to-noise paradox may not be due just to model deficiencies in representing the observed climate in initialized predictions, but other issues that affect the statistical properties of the predictions. We illustrate that this signal-to-noise problem is related to analysis practices that concatenate time series from different discontinuous initialized simulations, which introduces inconsistencies compared to the continuous transient climate realizations and the observations. In particular, the concatenation of predictions initialized independently into a single time series breaks the auto-correlation of the time series.

How to cite: Donat, M. G., Mahmood, R., Doblas-Reyes, F. J., and Tourigny, E.: A perfect-model perspective on the signal-to-noise paradox in initialized decadal climate predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15772, https://doi.org/10.5194/egusphere-egu25-15772, 2025.

The expansion of and increasing dependency on renewable energy that exploit climate variables, such as wind and precipitation, are highly sensitive to climate variability and weather extremes. Climate Futures is a Center of Research-based Innovation that aims to “co-produce new and innovative solutions for predicting and managing climate risks from sub-seasonal-to-seasonal (S2S) and seasonal-to-decadal (S2D) time scales with a cluster of partners in climate- and weather-sensitive sectors”, including the renewable energy sector, through long-term cooperation between businesses, public organizations and research groups.

The aim of the cross-sectoral collaboration is for renewable energy companies to integrate improved climate predictions into their decision making. The long-term implications are a more resilient energy sector and stable power production. Examples of ongoing projects within the center include (1) using large ensemble climate model simulations to estimate near-future changes in precipitation variability, and (2) estimating future wind power production and variability using state-of-the-art decadal climate predictions. These results are important for future wind- and hydropower operations and infrastructure planning.

How to cite: Svendsen, L.: Climate services for and with the renewable energy sector in Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12574, https://doi.org/10.5194/egusphere-egu25-12574, 2025.

The timescale of decadal climate predictions, from a year-ahead up to a decade, is an important planning horizon for stakeholders in the energy sector. With power systems transitioning towards a greater share of renewables, these systems become more vulnerable to the impacts of both climate variability and climate change. As decadal predictions sample both the internal variability of the climate and the externally forced response, these forecasts can provide useful information for the upcoming decade. 

There are two main ways in which decadal predictions can benefit the energy sector. Firstly, they can be used to try to predict how a variable of interest, such as average temperature, may evolve over the coming year or decade. Secondly, a large ensemble of decadal predictions can be aggregated into a large synthetic event set to explore physically plausible extremes, such as winter wind droughts. 

We find predictive skill at decadal timescales for surface variables over Europe during both winter (ONDJFM) and summer (AMJJAS). Although this skill is patchy, there are regions of relevance to the energy sector, such as over the UK for temperature, where this skill emerges. We find significant skill when using pattern-based (e.g., NAO) approaches to make predictions of European energy indicators during the extended winter, including Northern Europe offshore wind generation, Spanish solar generation, and Scandinavian precipitation. For predicting UK electricity demand, we find significant skill when directly using the model predictions of surface temperature. Our results highlight the potential for operational decadal predictions for the energy system, with potential benefits for both the planning and operation of the future power system. 

How to cite: Hutchins, B., Brayshaw, D., Shaffrey, L., Thornton, H., and Smith, D.: Decadal prediction for the European Energy Sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8693, https://doi.org/10.5194/egusphere-egu25-8693, 2025.

The most dangerous and costly meteorological hazards in Europe are extreme extra-tropical cyclones and associated windstorms (EUWS) in winter. Recent studies have shown that seasonal prediction systems can skilfully predict the seasonal frequency of EUWS with a one-month lead time using November initialisations. Given that many seasonal prediction systems produce seasonal forecasts at the start of each month, this raises the question whether pre-November initialised seasonal forecasts could provide usable information in predicting seasonal activity of EUWS.

In this study, we will present preliminary results of an approach aimed at extending the predictive horizon of seasonal EUWS activity. While the direct outputs of the pre-November initialised seasonal predictions of EUWS do not have the sufficient skill, skilful predictions of seasonal EUWS activity can be obtained by an approach that utilises the information of the upper ocean mean potential temperature from seasonal prediction systems. Based on our approach, skilful predictions of seasonal EUWS activity becomes possible as early as October.

How to cite: Leckebusch, G. C., Ng, K. S., Sriver, R., Degenhardt, L., Barrie, E., and Spreitzer, E.: Extending the Lead Time for European Winterstorm Activity Predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18643, https://doi.org/10.5194/egusphere-egu25-18643, 2025.

Long-range winter predictions over the Euro-Atlantic sector have demonstrated significant skill but suffer from systematic signal-to-noise errors. Here, we examine sources of early winter seasonal predictability in across state-of-the-art seasonal forecasting systems. As in previous studies, these systems demonstrate skill in the hindcasts of the large-scale atmospheric circulation in early winter, associated with the East Atlantic pattern. The predictability is strongly tied to the ENSO teleconnection to the North Atlantic, though the systems' response to ENSO is systematically too weak. The hindcasts of the East Atlantic index exhibit a substantial signal-to-noise errors, with the systems' predicted signal generally being smaller than would be expected for the observed level of skill, though there is substantial spread across systems. The signal-to-noise errors are found to be strongly linked to the strength of the ENSO teleconnection in the systems, those with a weaker teleconnection exhibit a larger signal-to-noise problem. The dependency on modelled ENSO teleconnection strength closely follows a simple scaling relationship derived from a toy model. Further analysis reveals that the strength of the ENSO teleconnection in the systems is linked to climatological biases in the behaviour of the North Atlantic jet. 

How to cite: O'Reilly, C.: Signal-to-noise errors in early winter Euro-Atlantic predictions linked to weak ENSO teleconnections and pervasive North Atlantic jet biases, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18821, https://doi.org/10.5194/egusphere-egu25-18821, 2025.