Job Information
Scientist – Ocean and Sea Ice Variability and Predictability
Job Description
Your role
We are searching for a highly motivated Scientist (A2) to conduct scientific research on evaluating (1) the representation of ocean and sea ice model uncertainty and mesoscale features in ECMWF coupled predictions and (2) understanding their local and remote impacts on weather and climate variability. You will perform numerical experimentation with stochastically perturbed parameters (SPP) in the ocean and sea ice model, to assess potential improvements to the ECMWF climate predictions in affordable eddy-permitting configurations. This will involve (1) diagnostic work on ensemble reliability, climate mean and trend and (2) evaluation of air-sea-ice interactions at the ocean mesoscale and impacts on the large-scale atmosphere circulation. The impact of stochastic parameterisations on air-sea-ice interactions and the associated atmospheric response, will be evaluated and compared with results from benchmark eddy-rich ocean-atmosphere simulations (including IFS-FESOM and IFS-NEMO) and idealised IFS sensitivity experiments .
The research and development will take place in close collaboration with colleagues in the Earth System Predictability Section and with external partners in the ACCIBERG and EERIE projects.
About the Earth System Predictability Section
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 systems. This involves both exploring the predictability horizon of the earth system, as well as identifying those elements limiting the actual forecast skill. The aim is to guide future development of the ECMWF Seamless Earth-System forecasting system.
Within the Earth System Predictability Section, the extended-range Prediction Team is responsible for the design of the ECMWF extended-range prediction system, which currently covers forecasts up to 46 days ahead. The team conducts predictability research to inform on the representation of sources of sub-seasonal predictability, as well as identifying critical elements to translate predictability into prediction 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 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.
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. We appreciate the need for flexibility in the way our staff work and staff have the option to mix office work and teleworking up to 10 days/month.
See www.ecmwf.int for more info about what we do.
Your responsibilities
- Develop and apply diagnostics to understand the atmospheric response to air-sea-ice interactions in (1) eddy-permitting configurations with and without ocean/ice SPP, (2) benchmark eddy-rich simulations, and (3) idealised IFS sensitivity experiments
- Perform numerical experimentation to study the potential benefits of implementing stochastic physics in the ocean and sea ice on ECMWF's subseasonal-to-seasonal forecasts
- Contribute to timely delivery and high quality of relevant ACCIBERG and EERIE results, as well as their contribution to ECMWF’s goals
- Communicate and document scientific results and software developments in technical reports, journal publications, conferences and meetings as appropriate
What we're looking for
- Ability to work efficiently and complete diverse tasks in a timely manner
- Self-motivated and passionate about the work
- Excellent interpersonal and communication skills
- Excellent analytical and problem-solving skills, with a proactive approach
- Ability to succeed both independently and in close collaboration with others
Education
- An advanced university degree (EQF level 7 or above) in physical, mathematical or environmental science
Experience
- Experience in working with large geophysical datasets on high-performance computing platforms in Unix/Linux-based environments
- Experience in working with numerical general circulation models
- Proficiency in object-oriented coding in Python and experience of shell scripting in Unix or Linux environments is required
Knowledge and skills
The following skills and experience would be an advantage.
- Knowledge of weather and climate variability and predictability
- Knowledge of probabilistic forecasting systems
- Knowledge of air-sea-ice interaction processes
We encourage you to apply even if you feel you don't precisely meet all these criteria in terms of experience, knowledge and skills.
Candidates must be able to work effectively in English. A good knowledge of one of the Centre’s other working languages (French or German) is an advantage.
Other information
Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. The annual basic salary will be GBP 71,451 (Reading/UK) or EUR 86,824 (Bonn/Germany) NET annual basic salary (ECMWF salaries are exempt of national income tax). In addition to basic salary, ECMWF also offers an attractive package of benefits and entitlements as defined in the ECMWF Staff Regulations. To find out more about working with us and for full details of salary scales and allowances, please visit www.ecmwf.int/en/about/jobs/working-ecmwf.
Starting date: As soon as possible
Interviews will take place via videoconference (MS Teams). If you require any special accommodations in order to participate fully in our recruitment process, please contact us.
To contact the ECMWF Recruitment Team, please email jobs@ecmwf.int.
Who can apply
Applicants are invited to complete the online application form by clicking on the apply button below.
At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.
Applications are invited from nationals from ECMWF Member States and Co-operating States, as well as from all EU Member States.
ECMWF Member and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.
In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy.
Applications from nationals from other countries may be considered in exceptional cases.