Company

EcmwfSee more

addressAddressReading, Berkshire
CategoryScientific

Job description

Your role

We are looking for a motivated scientist to conduct research on advancing the understanding of the mechanisms by which physical processes govern regional climate changes on interannual to multi-decadal time scales. The research will address fundamental knowledge gaps related to atmospheric circulation in the extratropics and their links to tropical sources which represents major limitations in current prediction and projections, in particular for understanding changes in European summer hazards like heat waves, drought and precipitation extremes.

The work will involve conducting targeted numerical experimentation with the ECMWF coupled forecasting systems as well as developing and applying innovative diagnostic tools, e.g. exploring suitable explainable AI methods, to attribute regional circulation changes. The research will be carried out in collaboration with other teams within the Earth System Predictability Section, in the ECMWF research department.

ECMWF delivers operational numerical weather and climate forecasts to its stakeholders on a range of time scales, including for several seasons up to one year in advance. With the climate system rapidly changing and some regions experiencing increases in extremes beyond what prediction models simulate, trustworthy assessments and predictions of such changes will need to be developed. An integral part of these developments is the capability to attribute changes to physical processes that govern these changes.

The position is funded by a collaborative Horizon Europe project, which has as its main goal the development of a prototype capability for integrated attribution and prediction of regional climate change signals from interannual to multi-annual time scales. This ambitious goal is closely aligned with the WCRP Lighthouse Activity on Explaining and Predicting Earth System Change.

About the Earth System Predictability Section / Seasonal Forecasting Team

The Earth System Predictability Section forms part of ECMWFs 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 Seasonal Forecasting Team is responsible for the design of the ECMWF seasonal prediction system, which currently covers forecasts from 7 to 13 months ahead, which will be extended to 24 months in the next operational system. The team conducts predictability research to inform on the representation of sources of 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 ML 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 Climate Change and Atmosphere Monitoring Services of theEU Copernicus Programme.We also contribute to the Copernicus Emergency Management Service. 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.


In this role you will:

  • Deliver ECMWFs contribution to the project for the prediction, attribution and hazard work tasks
  • Perform novel set of global coupled retrospective attribution experiments using ECMWFs modelling system
  • Perform integrated attribution and prediction analysis for changes in tropical processes
  • Develop methods to detect and attribute extratropical atmospheric summer circulation changes and related temperature and precipitation extremes
  • Collaborate with other project partners on the delivery of joint results and project reports

What we're looking for:

  • Sound knowledge of meteorology, atmospheric dynamics and climate physics
  • 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
  • Passionate, self-motivated and able to work independently
  • Excellent interpersonal and communication skills
  • Ability to maintain effective communication and documentation with the rest of the team and external project partners

Education:

  • A good university degree (EQF Level 6) and doctorate degree (EFQ Level 8) in climate science, mathematics, physics or related field

Knowledge, skills and experience:

The following knowledge, skills and experience would be an advantage. However, we encourage you to apply even if you feel you do not meet all these criteria.

  • Knowledge of concepts of predictability of weather and climate
  • Experience with statistical techniques to evaluate ensemble simulations
  • Ability to conduct numerical experimentation with GCMs in HPC environments.
  • Strong programming and scripting skills
  • Experience in large data analysis
  • Knowledge of ML for explainable AI

Candidate must be able to work effectively in English and interviews will be conducted in English

Other information

Grade remuneration: The successful candidates will be recruited at theA2 grade, according to the scales of the Co-ordinated Organisations. The position is assigned to the employment categorySTF-PL as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date: As soon as possible

Length of contract: The contract duration is expected to be 25 months with the possibility of extension (subject to funding)

Location: This position can be located at either ECMWF's duty station in Reading, UK, or in Bonn, Germany. Candidates are expected to relocate to the duty station. As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility for staff to mix office working and teleworking, including working away from the duty station (within the area of our member states and co-operating states) for up to 10 working days per month.

Interviews by videoconference (MS Team) are expected to take place during April/May 2024. If you require any special accommodations in order to participate fully in our recruitment process, please contact us.

Who can apply

Applicants are invited to complete the online application form by clicking on theapplybutton 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. 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.

ECMWF Member States 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.

Refer code: 3040714. Ecmwf - The previous day - 2024-03-21 23:48

Ecmwf

Reading, Berkshire
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