The role

We are looking for a motivated scientist to help us drive forward improvements in multi-system seasonal forecasts for climate services.

You will join a team at ECMWF and collaborate with colleagues from Member States, other partners and the wider scientific community to help us make further progress in the accuracy and reliability of seasonal forecast systems. Design and development of ensemble seasonal forecasting systems is complex and involves the scientific and computational balance of multiple components, including initialization, suitability of the forecast model, boundary conditions, ensemble generation and verification techniques. The very limited number of past cases available for model assessment, and the non-stationarity of the climate are serious challenges when it comes to evaluation and system design. The work is becoming ever more important as the impacts of climate change grow and past experience becomes an increasingly poor guide as to what weather to expect for the coming season.

Your work will focus on research to guide the design and evolution of future seasonal forecasting systems. This includes exploring hypotheses using numerical experimentation, diagnosing the performance of individual forecasting systems and developing additional metrics for system performance. Further research will include methods to extract predictable signals from the multi-system ensemble, and study of the impact of climate non-stationarity on climate predictability and methods to account for this.

This position is funded as part of the Copernicus Climate Change Service (C3S), and your work will support both the ECMWF contribution to the multi-system seasonal forecast ensemble and the wider development of C3S seasonal forecast activities.

The team

The Long-range Forecasting Team, part of the Earth System Predictability Section, is responsible for the design and scientific underpinnings of the ECMWF seasonal prediction configuration, which is expected to cover the forecast ranges from one month to two years ahead. The team conducts predictability research to improve our understanding and representation of sources of seasonal predictability. The team also provides scientific support to the C3S multi-system seasonal forecast component.

The Earth System Predictability Section is part of ECMWF’s Research Department. The Section explores relevant directions to improve the skill of the ECMWF forecasting system across timescales. This involves both exploring the Earth-system predictability horizon and identifying elements limiting present-day forecast skill.

About ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world-leader in weather and environmental forecasting. As an international organisation, we serve our members and the wider community with global weather predictions and data that is critical for understanding and solving the climate crisis. We function as a 24/7 research and operational centre with a focus on medium and long-range predictions, holding one of the largest meteorological data archives in the world. The success of our activities builds on the talent of our scientists and experts, strong partnerships with 35 Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies and machine learning across our operations. ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/supercomputer in Bologna, Italy, and a large presence in Bonn, Germany.

ECMWF is a global leader in machine learning for Earth system application and investigates machine learning throughout the weather forecast value chain including for observation processing, data assimilation, forecasting and post-processing. ECMWF has also developed a machine learned global forecast model – the Artificial Intelligence Forecasting System (AIFS) – that is used for daily weather predictions.

ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.

See www.ecmwf.int for more info about what we do.

The Copernicus Programme

Copernicus is the earth observation component of the European Union (EU) space programme. Based on the exploitation of spaced based and in situ (earth-based) observations and scientific models, Copernicus provides information services for land, marine, atmospheric and climate monitoring, as well as emergency management and security. These services, and their free, open and quality assured data and tools, support a range of environmental and security applications across sectors and policy domains. For details, see www.copernicus.eu

The Copernicus Atmosphere Monitoring Service (CAMS) service provides consistent and quality-controlled information related to air pollution and health, solar energy, greenhouse gases and climate forcing, everywhere in the world. For details, see https://atmosphere.copernicus.eu

The Copernicus Climate Change Service (C3S) service provides authoritative information about the past, present and future climate, as well as tools to enable climate change mitigation and adaptation strategies by policy makers and businesses. For details, see https://climate.copernicus.eu

Main duties and responsibilities

• To share in the scientific and technical activities of the long-range team, and to explore possible innovations to enhance the seasonal forecasting system
• To conduct numerical experimentation to advance the understanding of predictability at the seasonal time scale, and to test hypotheses related to improving forecast systems
• To develop and implement appropriate additional diagnostics to enhance performance metrics for model and forecast assessment
• To participate in research on the representation of climate non-stationarity in seasonal forecast systems, and methods to account for this in the creation of products
• To contribute to research on the predictable signals contained within model forecasts, and methods to extract these from the C3S multi-system ensemble
• To support users in the appropriate use and interpretation of seasonal model outputs and products

What we’re looking for

• Strong analytical and problem-solving skills, with a proactive approach
• Attention to detail but capability of, and focus on, understanding the overarching problems
• Demonstrated curiosity, drive and ability to perform novel research of international standing
• Passion, self-motivation and the ability to work independently
• Excellent interpersonal and communication skills

Education

• A very good university degree and doctorate degree in climate science, mathematics, physics or a related field

Knowledge, skills and experience

• Sound knowledge of meteorology, climate dynamics, climate variability and change
• Knowledge of concepts of predictability of weather and climate
• Experience with statistical techniques, including evaluation of ensemble simulations
• Ability to conduct numerical experimentation with GCMs in HPC environments
• Strong programming and scripting skills (python, bash, ideally also fortran)
• Experience working with large datasets, familiarity with ECMWF data formats
• You must be able to work effectively in English and interviews will be conducted in English

We encourage you to apply even if you don’t feel you meet precisely all these criteria.