Mathematics and computing applied to oceanic and atmospheric flows
Mathematics and computing applied to oceanic and atmospheric flows

Recent events have raised questions on social and economic implications of anthropic alterations of the earth system, i.e. climate change and associated risk for increased extreme events. Ocean and atmosphere, coupled with other components (continent and ice) are building blocks of the earth system. A better understanding of the ocean atmosphere system is a key ingredient for improving our prediction of such implications. Numerical models are essential tools to understand processes, simulate and forecast events at various space and time scales. Geophysical flows generally have a number of characteristics that make it difficult to model them and that justify the development of specifically adapted mathematical and numerical methods:

  • Geophysical flows are strongly non-linear. Therefore, they exhibit interactions between different scales, and unresolved small scales (smaller than mesh size) of the flows have to be parameterized in the equations.

  • Geophysical fluids are non closed systems. They are open-ended in their scope for including and dynamically coupling different physical processes (e.g. atmosphere, ocean, continental water, etc). Coupling algorithms are thus of primary importance to account for potentially significant feedbacks.

  • Numerical models contain parameters which cannot be estimated accurately either because they are difficult to measure or because they represent some poorly known subgrid phenomena. There is thus a need for dealing with uncertainties.

  • The computational cost of geophysical flows simulations is huge, thus requiring the use of reduced models, multiscale methods and the design of algorithms ready for high performance computing platforms.

Given these distinguishing features, the general scope of the AIRSEA project-team is to develop mathematical and computational methods for the modeling of oceanic and atmospheric flows. The used mathematical tools involve both deterministic and statistical approaches. The domains of applications range from climate modeling to the prediction of extreme events.

Centre(s) inria
Inria Centre at Université Grenoble Alpes
In partnership with
Université de Grenoble Alpes,CNRS


Team leader

Luce Coelho

Team assistant