Annual Seminar 2025

This talk will highlight some of the key ECMWF research directions in the context of its new strategy.

The ECMWF Vision is "World-leading monitoring and predictions of the Earth System enabled by cutting-edge physical, computational and data science, resulting from a close collaboration between ECMWF and the members of the European Meteorological Infrastructure, will contribute to a safe a thriving society".

As well as emphasizing the ultimate purpose and the importance of collaborations, this wording very deliberately emphasizes all of physical, computational and data science. In terms of physical science, the needs to continue to improve physical models (for use across timescales) and data assimilation systems (e.g. to more fully exploit satellite data over difficult surfaces) remains. Moving to higher resolution is an exciting opportunity, but also needs developments in physical and computational science (e.g. adapting sustainably to evolving computer architectures and handling ever-increasing amounts of data). Finally, the revolution in the application of artificial intelligence in the meteorological field means that physics-based systems will increasingly have a hybrid component, and fully data-driven systems are confidently expected to be a major and possibly dominant contributor to operational systems in the coming years. These systems typically learn from reanalysis - which will remain a major ECMWF focus both for this purpose and for climate monitoring - and hence remain anchored on the physics-based systems.

Extreme weather events are causing unprecedented floods, droughts, fires, and ecosystem damage on all scales. This necessitates better early warnings and climate and weather services, especially for transitioning to carbon-neutral economies. These challenges demand faster innovation, technological advancements, and stronger interdisciplinary collaboration, with a significant role for the private sector. The UN's "Early Warnings for All' initiative aims to establish crucial early warning systems.

Urbanization and our changing global economy have increased the need for accurate projections of climate change and improved seamless numerical weather predictions of disruptive and potentially beneficial weather events at the convective scale. Technological innovations are also leading to an evolving and growing role for the private sector in the weather and climate enterprise. The challenges faced in accelerating advances in weather and climate forecasting propose a vision for key actions needed across the private, public, and academic sectors.

Actions span:

(i) Utilizing new observational and computing ecosystems;
(ii) Developing strategies to advance Earth system models;
(iii) Exploring ways to benefit from the growing role of artificial intelligence;
(iv) Implementing practices to improve the communication of forecast information and decision support in our age of the internet and social media;
(v) Addressing the need to reduce the relatively large and detrimental impacts of weather and climate on all nations, especially low-income nations.

Regarding artificial intelligence, there will be a need to increase investment in data science, as well as a better understanding of predictability, physical and dynamical processes. We will illustrate this in the context of subseasonal-to-seasonal predictions.

to be decided

This presentation will explore how the use of storylines, employing spectral nudging, is advancing our ability to generate detailed, physically plausible narratives of how climate change is impacting weather and environmental events, including extremes. Spectral nudging selectively constrains the evolution of large-scale atmospheric winds (i.e., the jet stream) in global coupled climate simulations to follow observed trajectories while allowing finer, localized details, thermodynamical processes, as well as the ocean and sea ice to evolve freely. It will be shown that this approach creates accessible, tangible scenarios for everyday weather, heatwaves (on land and at sea), storms, or droughts—that support effective planning and decision-making.
Additionally, it will be shown that spectral nudging in global coupled climate models supports rigorous model evaluation using data from high-quality field campaigns, such as MOSAiC. By aligning model trajectories closely with observed conditions, this approach facilitates direct comparisons between model outputs and field data, strengthening model validation and improvement efforts.

This paper presents progress and plans on coupled data assimilation developments conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to enhance the exploitation of interface observations in support of climate monitoring and weather prediction.

We describe the ECMWF coupled data assimilation approach. It relies on the atmospheric 4D-Var outer loop cycling methodology which enables to account for parallel minimisations in multiple Earth system components. Increments from each component are applied to initialise subsequent outer loop coupled model trajectories, so assimilated observations have an impact across the Earth system components within each data assimilation window. It is being developed and progressively implemented consistently for land/atmosphere and ocean/sea-ice/atmosphere coupling for operational applications across the ECMWF systems.

Latest developments, results and plans on ocean-atmosphere coupled data assimilation are presented, including work towards coupled sea-ice and sea surface temperature analysis based on passive microwave and infrared radiance assimilation.

We also present progress and plans on coupled land-atmosphere data assimilation conducted in the context of the CERISE (Copernicus Climate Change Service Evolution) and CORSO Horizon Europe Projects. Unified ensemble-based land assimilation combined with outer loop coupled land-atmosphere data assimilation results are shown, along with work to assimilate new observation types over land surfaces, such as Solar Induced Fluorescence, to constrain water and carbon fluxes representations for climate monitoring and forecasting applications.

For both land and ocean coupling, Data Assimilation and Numerical Testing for Copernicus Expansion Missions (DANTEX) activities are introduced and preliminary results shown.

This talk will provide an overview on current developments in Earth system modelling at ECMWF and summarise challenges and opportunities. It will outline how recent developments in machine learning will change how physics-based models are used in the future, and what future Earth system modelling systems and model development pipelines will need to look like.

Extreme event attribution (EEA) gathers methods from atmospheric sciences and statistics to quantify how exceptional climate or meteorological events can be, and how their outstanding features are connected to climate change. One of the societal expectations of EEA is to provide quick and reliable statements when extreme events occur. Several approaches have been developed towards this endeavor, like the the World Weather Attribution or ClimaMeter, which are the “visible parts of the iceberg”. Although the practical application of fast attribution is debatable for adaptation to climate change, several climate simulation tools have been developed for EEA, which help understand and anticipate the most extreme events, in a changing climate. I will discuss those climate simulation approaches and illustrate them on cases of heatwaves and cold spells in Europe.

Recent climate model developments, established through increased model resolution, have led to improvements in model simulations of the coupled Earth system. However, Earth System models will always produce climate features and variability that differ from the real world and will be prone to biases. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion and associated parameter estimation. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements, soil moisture and carbon data, to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate data assimilaiton (DA) will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Climate DA must aim to enhance climate knowledge through the improved ability to simulate and predict the real world by optimally combining Earth system models and most available global observations from different Earth system components and domains. In the future, arguably the most important aspects of climate DA and ML in support of model improvement and enhancing predictive capabilities might become optimizing initial conditions, model parameters and model structure to mitigate model biases and thereby improve models’ skill in simulating the observed climate, as well as to enhance model skill for climate projections.

A crucial component of any seasonal forecast system is its retrospective forecasts, or hindcasts, from past years which are used to estimate skill and calibrate predictions. Typically, operational hindcasts cover a span of 20–30 years. Such small hindcast periods are problematic for several reasons. For example, the limited sample size constrains the statistical evaluation of sporadic drivers of seasonal predictability, such as ENSO events, which occur only every few years. In regions with high interannual variability, such as the Euro-Atlantic sector, the limited number of hindcast years is a large contributor to uncertainty for skill estimation and calibration. Furthermore, hindcast skill assessments over extended historical period reveal non-stationarity in skill with fluctuations on multi-decadal timescales. These variations include periods of high skill (e.g., the early 20th century) and low skill (e.g., the mid-20th century), with important implications for seasonal predictability in a changing climate.

In the second part of the talk, I will explore a potential new role for seasonal forecasts in the future. Reliable and successful predictions can provide the testbed for assessing the role of anthropogenic (or otherwise externally driven) climate change for extreme and impactful seasons. As an example, the heavy rainfall during the summer 2022 over Pakistan which led to devastating widespread flooding where thousands of people lost their lives or were injured, will be discussed. Experiments with ECMWF’s seasonal forecasting system SEAS5 with altered CO2 concentrations result in a nonlinear rainfall response to the forcing. A decomposition of the underlying large-scale circulation signals into their atmospheric CO2 and SST-induced responses highlights how the balance of their relative changes controls the future dynamical response. Such forecast-based attribution methods could provide critical information for reliable regional climate adaptation strategies.

Land data assimilation aims to monitor the evolution of soil and vegetation variables. These variables are driven by climatic conditions and anthropogenic factors such as agricultural practices. Land surface monitoring includes a number of variables of the soil-vegetation system such as land cover, snow depth, surface albedo, soil water content and leaf area index (LAI). These variables can be monitored by integrating satellite observations into models through data assimilation. Monitoring land variables is particularly important in a changing climate, as unprecedented environmental conditions and trends emerge. Unlike atmospheric variables, land variables are not chaotic per se, but rapid and complex processes affecting the land carbon budget, such as forest management (thinning, deforestation, ...), forest fires and agricultural practices, are not easily predictable with good temporal precision. They cannot be accurately monitored without integrating observations as they become available. Because data assimilation is able to balance information from contrasting sources and account for their uncertainties, it can produce an analysis of terrestrial variables that is the best possible estimate. Data assimilation can involve several techniques, such as 'model parameter tuning', variational assimilation or sequential Kalman filtering methods. The latter are used in meteorology and in some land modelling frameworks to improve initial conditions (e.g. root zone soil moisture) at a given time. New research is being undertaken to assess the impact of improving vegetation initial conditions, as vegetation, like soil moisture, has a memory of past environmental conditions. Vegetation variables such as LAI control the amount of evapotranspiration and their initial conditions have a predictive capability. Examples are given of how data assimilation can be implemented on a global scale by regularly updating model state variables through a sequential assimilation approach. The focus is on LAI assimilation and the use of machine learning techniques to build observation operators that allow the direct assimilation of new vegetation sensitive observations such as microwave backscatter and brightness temperature or solar induced fluorescence. We show that the analysis of LAI together with root zone soil moisture is necessary to monitor the effects of irrigation, drought and heat waves on vegetation, and that LAI can be predicted after proper initialisation. We also show that machine learning can be used to derive new variables (e.g. surface albedo, vegetation moisture) from those already calculated by the land surface model. This paves the way for new developments such as more interactive assimilation of land variables into numerical weather prediction and seasonal forecasting models, as well as atmospheric chemistry models.

Weather forecasting has a long history and has become indispensable for decision-making across various sectors. In recent decades, seasonal forecasts have gained traction due to improvements in predictive skill and the development of sophisticated applications. However, tools addressing the critical time horizon of 10 days to one month – known as sub-seasonal to seasonal (S2S) forecasts – have been less prevalent, primarily due to the limited predictive skill of weather models. The gap is not for a lack of need; there exists significant demand for forecasts beyond the typical weather prediction time frame and shorter than forecasts.

In my work, I have engaged with numerous stakeholders and decision-makers who express a need for S2S forecast information. In this talk, I will present examples from our work at the Climate Futures center in Norway and from past and ongoing climate services projects in Africa.

Motivated by the public and partner requests within Climate Futures, the Norwegian Meteorological Institute has developed a 21-day forecast now integrated into the widely used YR weather app and available through a free API. I will discuss the decision-making process informing the design of this service, and elaborate on how the API is utilized for both commercial and non-commercial products.

In Africa, where reliance on rain-fed agriculture is critical, the timing of the rainy seasons is vital. For farmers, a dry spell following initial rainfall can ruin crops, severely affecting food security and household economics, particularly for subsistence farmers who cannot afford to purchase new seeds. I will detail our ongoing efforts developing "packages" for at-risk communities in Malawi, Ethiopia, and Madagascar. These packages combine 21-day forecasts of dry spells with other resilience-enhancing tools such as training, insurance, cash support, and soil moisture instruments, aiming to improve community robustness to weather fluctuations.

In summary, we see vast potential for S2S products and services to significantly benefit both Europe and Africa, addressing critical needs and fostering resilience in diverse communities.

The stratosphere represents an important source of predictability for the extratropics in winter and spring, especially after extreme stratospheric events. These events tend to be followed by persistent temperature and precipitation anomalies at the surface, including extreme weather events. While the influence of the stratosphere on the troposphere has long been established, the predictability arising from this downward influence is less straightforward to quantify due to the strong case-by-case and geographic variability that models cannot fully reproduce. It further remains unclear how the coupling between the stratosphere and the surface may have changed over time in the recent past and how it will change under climate change. Such changes may affect both the dynamical vertical coupling as well as the predictability arising from stratospheric sources. This contribution will provide an overview of dynamical stratosphere - troposphere coupling and its impacts on extended-range prediction.

Sub-seasonal prediction has been operational at ECMWF since 2004. Over the past 20 years, the forecast skill at week 3 and 4 has significantly improved, particularly in the Tropics, thanks to improved modelling, model complexity, initialization and model ensemble configuration. However, further progress is impeded by the presence of model biases which affect the prediction of key sources of sub-seasonal predictability, their interactions and their impacts. For instance, despite significant improvements in the representation and prediction of the Madden Julian Oscillation since 2004, the representation of its impacts in the tropics and Extratropics in S2S models remains problematic and a major obstacle for further skill improvement at weeks 3 and 4 over Europe. Machine learning represent an opportunity to overcome these issues by providing improved post-processing, replacing the dynamical model, helping correcting models errors or helping better understand causality at the S2S timescale.

TBC

Data assimilation is the process that connects observations to numerical models to produce initial conditions for prediction. Advancements in data assimilation have been some of the foundations for realizing improved predictions from operational numerical weather prediction over the past several decades. This presentation will provide an overview of some of the major advancements in data assimilation that have led to drastically improved operational predictions. These include developments to perform direct assimilation of satellite radiances (instead of meteorological retrievals) and the adaptive bias correction thereof, extensions to 4-dimensional assimilation including techniques such as 4DVar, and incorporation of ensembles to produce flow-dependent estimates of uncertainties. An overview of the current state of the science for data assimilation at operational numerical weather prediction centers will be presented. Finally, the possibilities for the future will be discussed, including the prospects for moving toward systems based entirely on artificial intelligence.

As the national meteorological and climatological service MeteoSwiss has the mandate to provide weather and climate services for the safety and benefit of Switzerland. Being one of the founding members in 1975, Switzerland uses global medium-range weather forecasts and climate reanalysis since the beginnings of ECMWF. However, the country’s complex orography and local weather phenomena in the Alps impose a special challenge in providing accurate and timely local weather and climate information across scales.
In this lecture I will present examples of seamless weather and climate services tackling the challenges imposed by complex orography. An emphasis will be laid on products using ECMWF data as input. Some of the latest developments concern seamless workflows for weather and climate monitoring, wind climatology and the provision of a national drought platform.

Data-driven models (DDMs) are rapidly emerging as an alternative to Numerical Weather Prediction (NWP) models. The success of global DDMs is driving increased efforts towards tailoring DDMs also for regional applications. National meteorological and hydrological services, such as MET Norway, primarily depend on high-resolution regional NWP models for delivering weather forecasts to the public and industries affected by weather conditions. Given Norway's complex topography and coastline, forecasts must be provided at a higher spatial resolution than what is commonly provided by global DDMs.

A DDM suitable for regional weather forecasting applications is presented. This model extends the Artificial Intelligence Forecasting System (AIFS) by integrating a stretched-grid architecture. This architecture allows for a higher resolution in targeted regional areas, while maintaining a lower resolution globally. The model is based on graph neural networks, which support various multi-resolution grid configurations. Using this framework we will discuss the potential of machine learning in addressing the multiple challenges associated with delivering location-specific forecasts to end users. These include generating forecasts on kilometer scale with hourly temporal resolution, incorporating other observation sources such as extensive networks of crowd-sourced observations, providing probabilistic forecasts, and forecasting rare events.

The Madden-Julian oscillation is one of the most important sources of subseasonal predictability in the mid-latitudes as it modulates the occurrence of long-lived and quasi-stationary weather regimes through teleconnections. This modulation of the mid-latitude flow in turn has consequences for the activity of low pressure systems in the mid-latitudes, which themselves can significantly influence the dynamics and predictability. By evaluating reanalysis data as well as idealized experiments, we show in the first part of this study that the release of latent heat in midlatitude warm conveyor belts (WCBs) significantly modifies known canonical teleconnections or even suppresses them. In state-of-the-art subseasonal forecast systems, the modulation of the frequency of WCBs conditioned on the MJO state is erroneously represented. Accordingly, we hypothesize that an improved representation of this link could have a positive impact on subseasonal weather regime forecasts during active periods of the MJO. In the second part of the study, we investigate the potential of artificial intelligence-based weather prediction models to identify source regions of forecast errors that are particularly detrimental to the subseasonal forecast skill. Using the hybrid Neural GCM, we find that the model is similarly sensitive to different relaxation regions as reference experiments with numerical weather prediction models.

Machine learning (ML) is transforming the field of weather and climate forecasting, offering innovative approaches to enhance accuracy, efficiency, and utility. This presentation provides an overview of how ML is being integrated into forecasting workflows, highlighting its transformative potential and diverse applications.

We will examine ML's role in improving forecast skill, from emulating complex physical processes to accelerating data assimilation and building ensemble systems. ML facilitates the exploitation of vast Earth observation datasets, perhaps enabling the use of even more Earth-system data in developing and running forecasting systems.

In the context of the 50th anniversary, we will look out into the future, and try to forecast the role for machine learning in weather and climate in the years to come.

AI-driven weather forecast models are now more accurate and much faster than the best physics-based models. Can similar technology be used to develop AI climate models for reliably simulating decades to centuries across a range of climates? Groups around the world are making exciting progress toward this goal using a range of approaches. This talk focuses on the open-source Ai2 Climate Emulator (ACE). ACE can be trained to accurately emulate both daily weather variability (including extremes) and climate of historical reanalysis (ERA5), or of a global atmospheric model,. ACE has 100-1000x smaller computational cost of running a physics-based model of comparable grid resolution. ACE can also accurately emulate the weather and climate of finer-grid physics-based models. ACE can be forced by specified sea-surface temperatures (AMIP) or coupled to a slab ocean model; coupling to an AI emulator of a dynamical ocean model is ongoing. When trained on reference model simulations spanning multiple climates forced by changed CO2 concentrations, ACE can accurately emulate the climate change response of the reference model. Lastly, we note some key remaining challenges to general-purpose use of ACE for climate modeling applications.

AI applications to weather and climate are growing exponentially and there is a critical need to ensure that these methods are trustworthy before they are deployed. In this talk, I will describe work from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focusing on studies with forecasters to determine what they need for an AI forecast to be trustworthy. We discuss the elements of trust as seen by the forecasters and how this affects the AI development lifecycle. Finally, I end with a discussion of developing trustworthy AI model verification via Extreme Weather Bench.

Eastern Africa experiences frequent extreme weather/climate events, such as droughts, floods, and heatwaves, which are projected to intensify due to global warming. To address these challenges, a variety of forecasting techniques have been developed to predict weather and climate conditions across different timescales. These methods include dynamical, statistical, and hybrid approaches, each tailored to meet the unique needs of the region and users.
For weekly timescales, dynamical downscaling techniques are employed to produce tailored forecasts, such as exceptional rainfall events, temperature and rainfall anomalies, and mean conditions. At sub-seasonal to seasonal timescales, statistical downscaling methods are used to downscale outputs from global climate models, providing actionable information for stakeholders in critical sectors like agriculture, health, and disaster risk reduction. Since 2019, the IGAD Climate Prediction and Applications Centre has been utilizing objective forecasts, which are useful to drive the impact models and produce tailored forecasts.
In recent years, machine learning has emerged as a promising tool in the region's forecasting efforts. Conditional Generative Adversarial Networks (cGAN) have been developed for short-range timescales, enabling National Meteorological and Hydrological Services (NMHSs) to objectively provided probabilistic forecasts and calculate probabilities of exceeding specific weather thresholds. In addition to the short-range forecasts, machine learning techniques have also been developed for the seasonal timescales. These advancements are likely to significantly improve the early warning systems over the region.
By integrating diverse forecasting techniques and advancing machine learning capabilities, Eastern Africa is building a more robust and adaptive forecasting system, essential for mitigating the impacts of extreme weather events and enhancing climate resilience in the face of a warming planet.

El Nino-Southern Oscillation is a major mode of climate variability at interannual time scales, with worldwide impacts in weather patterns. This lecture will provide an overview of the processes influencing ENSO, their positive and negative feedbacks and the interaction between different time scales. It will discuss the implications for ENSO forecasting and the impact of climate change. The discussions will be illustrated by the comparative analysis of major recent El Nino events.

Advances in pre-exascale computing and model scalability have enabled the use of Earth system models with grid spacing of a few kilometres (km-scale models) to forecast weather and simulate climate over decades. Km-scale models provide more accurate local data and better capture the physical processes conducive of extreme weather events and interactions between model components, which coarser models often miss.

This presentation review the projects at ECMWF that have made possible the development and implementation of km-scale models and assess their current performance. Key achievements include better representation of extreme weather events, like tropical cyclones and orographic precipitation, and improved predictability of large-scale atmospheric patterns.

We also address remaining challenges, such as improving the parameterization of moist physics at the km scale for greater physical realism without reducing NWP skill, advancing non-hydrostatic models, enhancing data assimilation for km-scale resolution, and quantifying uncertainty despite high simulation cost.

To fully exploit km-scale information, application models must be seamlessly integrated into their workflow. This integration is a fundamental aspect of the Digital Twins for weather and climate developed in Destination Earth, enabling the delivery of more relevant, impact-oriented and actionable information.

The production of ECMWF's latest atmospheric reanalysis, ERA6, will begin in 2025. ERA6 benefits from 8 years of research and development since the start of it's predecessor, ERA5, encompassing improvements to the forecast model, data assimilation system and the exploitation of observations. The horizontal resolution, at 14km (TCo799), represents a factor of two improvement over ERA5 (30km, TL639). Of particular relevance for ERA6 is the improved treatment of biases in the stratosphere through the use of weak constraint 4DVar and the introduction of outer-loop coupling of ocean and atmosphere. The provision of ocean forcing from ECMWF's latest ocean reanalysis, ORAS6, represents a significant improvement over ERA5. ERA6 will also benefit from the assimilation of many reprocessed datasets, both conventional and satellite, which have been the subject of extensive testing in recent years. Based on pre-production testing the step change in re-forecast quality of ERA6 relative to ERA5 is similar to that achieved in the the upgrade from ERA-Interim to ERA5. It is expected that the initial production streams, covering the period from 2006-present, will be published in the second half of 2026.

The rapid evolution of societal needs, driven by climate change, technological advancements, and shifting user expectations, presents significant challenges for traditional weather services. In response to these developments, weather services need to transform from traditional forecast providers to becoming decision-making consultants that empower diverse and also new user groups.
The presentation examines the transformation required to address this paradigm shift, focusing on the integration of sector-specific expertise and developments in co-design with key customers. This includes discussing critical hurdles such as rapidly evolving customer expectations versus the existing pace of internal renewal. This will lead us to examine organizational and personal challenges, highlighting strategies for fostering innovation, collaboration, and continuous renewal in weather services.

Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to improve early warning and anticipatory action ahead of high impact events. However, fully realising this potential predictability requires reliable forecasts that are communicated effectively so that they can support appropriate preparedness action. I will reflect on the African SWIFT (Science for Weather Information and Forecasting Techniques) S2S forecasting testbed which brought together researchers, forecast producers and forecast users from a range of African and UK institutions. The testbed used a co-production approach to pilot the provision of real-time bespoke S2S forecast products for applications. The S2S testbed supported decision-makers in a range of sectors and contexts. For example, informing food security decisions and hydropower energy planning in East Africa, supporting agricultural decision-making across West Africa, and, in health applications, increasing the lead-time for potential disease outbreaks.

I will critically reflect on the benefits and challenges of the co-production process within the S2S applications context. Specifically, while having direct access to the real-time S2S data allowed user-guided iterations to products to make them more actionable for their specific context. Some key lessons for effective co-production emerged. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions.
I will also talk about opportunities to develop this thinking in two new projects: 1) ACACIA (Anticipatory Climate Adaptation for Communities in Africa) and 2) POWER-Kenya (Potential of sub-seasonal for Operational Weather and climate information for building Energy Resilience in Kenya).

Climate models show a warming trend in the eastern Tropical Pacific that is inconsistent with observations, an error which has consequences for the estimation of climate sensitivity. Similar trends are detected in hindcasts of ECMWF’s operational seasonal forecasting system SEAS5 from 1993 onward. Results show that SEAS5 hindcasts strongly underestimate the observed strengthening of equatorial easterly and cross-equatorial southerly winds. Associated with this, hindcasts exhibit spurious trends towards anomalously warm sea surface temperature (SSTs) in the equatorial and southeastern tropical Pacific, consistent with a tendency towards overprediction of probabilities for El Niño towards the end of the hindcast period. This tendency persisted during the 2020-2022 triple-dip La Niña, with forecast errors reminiscent of the long-term trend errors. Uncoupled hindcasts with prescribed SSTs exhibit similar wind trend errors as SEAS5, suggestive of an underestimation of the wind-evaporation-SST feedback by the atmospheric model. Spurious cloud and shortwave radiation trends contribute to SST trend errors as well. Results also indicate that a persistent positive precipitation bias in the Western Pacific warm pool keeps the model in a regime where equatorial Pacific winds are too insensitive to changes in zonal gradients. In coupled mode, trend errors of the atmospheric model amplify through the Bjerknes feedback. The excessive warming of the equatorial ocean subsurface in the ocean initial conditions also plays a role. Further reduction of model biases are thus needed to tap the source of predictive skill represented by climate trends, and seasonal forecasts are an efficient test bed for climate model development.

Sooner or later all forecasts will be wrong. This is given by the nature of the atmosphere being chaotic. A chaotic system is sensitive to initial condition errors meaning that if the prediction starts to differ from the state of the nature, the evolution the two will eventually be uncorrelated even if the initial errors are very small. How these differences growth is highly situation dependent. The error growth is also affecting the smaller scales much faster than longer scales.

However, there are also other contributors to the forecast errors. This includes approximation in the model or missing processes, spatial representation differences between the model and the anticipated forecast, and interpretation errors. Finally in long-range forecasts, model errors are playing an important role in creating errors. In this presentation we will discuss the different aspects of the forecast errors in the light of severe weather predictions, and also give some perspective of the future in terms of predictability related to data-driven forecasting.

After more than quarter of century of operational physical-model-based seasonal forecasting, seasonal prediction remains a challenging problem. The basics of the forecast problem are well understood, and at least in recent decades we can initialise our forecasts moderately well. Many aspects of variations in seasonal weather can be predicted with a demonstrable level of skill. And yet. Although there have been steady improvements in model fidelity, the pace of progress on critical problems has been slow, relative to what is needed for forecasts to be credible and reliable. As a consequence, predictability limits, which are most conveniently studied by model experimentation, are poorly known.

How might progress in seasonal forecasting be accelerated? Can we create realistic physics-based models of the relevant parts of the earth’s climate system, given computer constraints? Can we improve reanalyses of past decades? And can we pool information from the past and present, knowledge of climate forcings, physical models and machine-learning techniques to transform what seasonal forecasting is capable of?

ECMWF has developed a machine-learned weather prediction model, the Artificial Intelligence Forecasting System (AIFS). We will describe the current state of AIFS, its architecture and framework, and discuss ensemble generation methods, via probabilistic score optimisation and diffusion-based training. AIFS produces highly skilled forecasts ranging from medium-range to sub-seasonal time scales. Both, a deterministic AIFS version as well as an ensemble version of AIFS are now run in experimental mode alongside ECMWF’s physics-based NWP model. AIFS forecasts are available to the public under ECMWF’s open data policy.

Climate services can be considered as the provision and use of climate information in decision-making. As with weather services based on weather information, climate information has long been used to inform decisions. However, the development, delivery and use of climate information became a global endeavour in the wake of World Climate Conference-3 in 2009 when heads of state, government ministers, industry representatives, and scientific and technical experts from many fields of practice recognised the growing importance of climate services. We have experienced huge developments across the climate service value chain, including the underpinning observations, research and modelling capability providing the climate information, through to the service delivery and uptake for decision-making. But of course, more needs to be done, particularly as we continue to see climate-related events impacting society and economies worldwide against the backdrop of climate change. This presentation will introduce climate services, considering some history, the landscape, the drivers, and developments, highlighted with examples. The presentation will also highlight current and future challenges.

Subseasonal to decadal prediction science and services have advanced rapidly in recent years, with the WCRP-involved S2S project and Grand Challenge in Near-term Climate Prediction having laid key groundwork for the establishment of WMO services via its Lead Centres for Sub-seasonal Predictions Multi-Model Ensemble (SSPMME) and Annual-to-Decadal Climate Prediction (ADCP), respectively. Ongoing WCRP groups and initiatives such as its Working Group on Subseasonal to Interdecadal Prediction (WGSIP), Decadal Climate Prediction Project (DCPP) and Explaining and Predicting Earth System Change (EPESC) are further exploring scientific frontiers across the spectrum of prediction time scales between those of numerical weather prediction and long-term projections. This presentation will briefly summarize these activities within WCRP, and then share multifaceted perspectives on two broad frontier topics: predicting a rapidly changing Earth system, and the AI/ML revolution—where will it lead?

The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities through the provision of innovative climate information on multi-decadal timescales, globally, at scales at which the impacts of climate change are observed. This initiative presents the first ever attempt to operationalise the production of global multi-decadal climate projections at resolutions of 5 to 10km, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models.

This lecture will give an overview of the Climate DT system, including the underlying models, the novel technological approaches, and data handling and analysis. It will also showcase how the model output is transformed into climate information by using impact sector models as part of the Climate DT.

https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/