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.