Department
Georges Lemaîter Centre for Earth and Climate Research, Earth and Life Institute
Organization
Université catholique de Louvain
Email
francois.massonnet@uclouvain.be

Location

Louvain-la-Neuve
Belgium

Bio

Dr Massonnet obtained his PhD in Sciences in 2014 from the Université catholique de Louvain
(UCL) under the supervision of Profs. Thierry Fichefet and Hugues Goosse. During his PhD, he
developed various metrics to evaluate sea ice models used in the framework of climate
reconstructions, predictions and projections. He participated as a contributing author to the IPCC
WG1 AR5 and was involved in several national and international research projects about climate
prediction and predictability. He also implemented ensemble data assimilation methods in large-
scale sea ice models for state and parameter estimation.
Immediately after his thesis, he joined the Climate Prediction Group of the Barcelona
Supercomputing Center Earth Sciences Department (BSC-ES) and worked under the guidance of
Dr Virginie Guemas and Prof. Francisco Doblas-Reyes. There, he explored various aspects of
seasonal-to-decadal polar and extra-polar prediction, including attribution of extreme events,
improved initialization techniques, novel aspects of forecast verification and linkages of the Arctic
system with lower latitudes. In particular, he implemented an ensemble coupled sea ice data
assimilation method in the General Circulation Model EC-Earth for initialization of near-term
predictions in both the Arctic and Antarctic regions.

Interests

Sea Ice, Physical Science, Interdisciplinary Research, Science Education

Science Specialties

Data assimilation, climate prediction, climate model and forecast evaluation, sea ice, high latitudes, climate change

Current Research

Dr Massonnet is a F.R.S.-FNRS Postdoctoral Researcher at the Université
catholique de Louvain and is also affiliated to the Earth Sciences Department of the Barcelona Supercomputing Center. His work is focused on the use and assessment of climate general circulation models for prediction at time scales from the month to the decade.