The Centre for Polar Observation and Modelling (CPOM) at University College London invites applications for a PostDoc position in seasonal ice forecasting with a focus on development of new remote sensing products and statistical models.
The shrinking Arctic sea-ice cover has captured the attention of the world. The downward September trend has accelerated over the last decade, with the 10 lowest September sea-ice extents occurring in the last 10 years. An essentially ice-free Arctic during summer is expected by mid-century. Loss of the sea ice cover has profound consequences for ecosystems and human activities in the Arctic, so there is an urgent need to advance sea-ice predictions in all seasons at both the pan-Arctic and regional scales. A key finding that emerged from the first Sea Ice Prediction Network (SIPN) effort is that predictions of September sea-ice extent tend to have less skill in extreme years that strongly depart from the trend line. The objective of proposed research under Phase 2 of SIPN (SIPN2) is to improve forecast skill through adopting a multi-disciplinary approach that includes modeling, new products, data analysis, scientific networks, and stakeholder engagement.
The candidate will join the international SIPN team to advance seasonal forecasting using statistical methods and development of new products. Feedback from SIPN members has identified a number of variables that are key for improving sea ice predictions: sea ice thickness, surface roughness, melt pond fraction, floe size distribution and snow depth. CPOM UCL is leading the way with sea ice thickness and snow depth products, and wants to expand to other sea ice parameters needed to improve seasonal forecasts. These data sets can then be explored through statistical forecast approaches, such as links between melt pond fraction and September sea ice extent.
1) Take an active role in developing new remote sensing sea ice data sets useful for seasonal ice forecasting.
2) Explore sources of predictability of regional and pan-Arctic sea ice extent, dates of ice retreat and ice advance, including atmospheric and oceanic contributions.
3) Disseminate results in peer-reviewed scientific journals and at international conferences.
1) The position requires a PhD in physics, meteorology, oceanography or a related field.
2) Experience in remote sensing and/or climate modelling, and a good knowledge in climate physics and dynamics with a focus on the atmosphere and/or sea ice are required.
3) Profound knowledge in scientific programming and good English skills are expected.
4) The candidate will be expected to disseminate results in peer-reviewed scientific journals and at international conferences.
1) Experience in image processing
2) Experience in machine learning
3) Experience with atmospheric reanalysis data
4) Experience contributing to writing research proposal
For more information, please contact Prof. Julienne Stroeve ([hidden email])
Employment is limited to 24 months. The start date is 1 April 2018, or as soon as possible thereafter. The salary will be paid in accordance with the
the UCL Grade 7 Salary rate of £38,183 with a £2,980. The place of employment will be in London.
Please forward your applications with the standard documentation (cover letter with motivation, CV, publication record and two references) by January 31st 2018 viaemail (all documents merged into one PDF file) to: Leisa Clemente ([hidden email])
Job Title: PostDoc position at CPOM UCL (reference 1707962 - Research Associate in Remote Sensing)
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