Extratropical climate variability and extremes

In extratropical regions, atmospheric perturbations with different spatial and time scales interact with each other. Our objective is to study such interactions with a focus made on processes leading to the formation of high-impact weather events (wind storms, heavy precipitations, cold spells, heat waves,...)


One of the key question addressed by the scientific community concerns the impact of climate change on high-impact weather events. For instance, the faster warmings of the Arctic and the continents relative to the ocean decrease horizontal thermal contrasts that could influence mid-latitude cyclogenesis or jet streams. In addition to climate change impact, our objective is to better understand how different factors influence the behavior of mid-latitude atmospheric perturbations like tropical convection, the stratosphere and the ocean. To study processes involved in mid-latitude atmospheric perturbations responsible for high-impact weather extremes, a combination of various approaches is used. It includes reanalysis data and a hierarchy of models ranging from most simple models like quasi-geostrophic models to intermediate-complexity models and more comprehensive models like fully-coupled climate models used in CMIP exercises.


Since climate change becomes apparent in reanalyses datasets, it is worth studying changes in atmospheric circulations and see if we can attribute them to climate change. European heat waves are usually triggered by atmospheric blockings which tend to warm the surface via adiabatic warming due to subsidence and clear-sky radiative forcing. The presence of a deep depression to the west may also help transporting heat from low latitudes as was the case for the June 2019 heat wave (see right panel of the figure). Changes in such circulations leading to extremes, like blockings or deep depressions are studied in our team. However, extreme events being rare, statistics are generally not robust when using reanalysis datasets or even when analysing simulations of climate models that are not long enough to study such rare events. Adequate tools need to be developed to study rare events. For instance, rare event algorithms are currently developed in our team to multiply the number of simulated rare events in a model to get statistically robust results. Machine learning algorithms are also used and developed to get probabilist prediction of extreme events from the knowledge of various meteorological variables.


Different tools and concepts have been recently developed to better assess impacts of extreme weather events, in particular in the context of climate change. The concept of compound events has been recently introduced and refers to the combination of multiple weather or climate drivers creating a societal or environmental risk. It may correspond to interference between events of different nature, successive events in time, or spatially distributed events that aggregate to form a risk. Storylines is another rather recent concept useful for adaptation to climate change as it allows a better interaction between scientists and adaptation stakeholders.

Permanent Staff

F. Bouchet, F. D'Andrea, M. Ghil, A. Jézéquel, G. Lapeyre, F. Lott, G. Rivière, T. Roberts

PhD students, postdocs

J. André, M. Besson, V. Deshmukh, J. Lac, A. Lancelin, L. Mandonnet, L. Pauget, C. Le Priol,