Description du sujet de thèse

The thesis will focus on the modeling of convection and its coupling with the sub-grid distribution of water vapor in the LMDZ global climate model.
The first step will be to complete and evaluate a new “prognostic” parameterization of the subgrid distribution of water vapor, used to predict the fraction of a model grid cell covered by clouds (cloud fraction). The evaluation will be carried out in single-column mode: a simulation of a convection case will be compared with very high-resolution (~100m) 3D "Large Eddy Simulations" (LES) of the same scene, using imposed forcings.
The second part will involve modifying the LMDZ model by making water variance a state variable. This will involve both developing missing parameterizations and taking into account the variance of water vapor as a state variable of the model, advected by the large scale dynamics, which will force us to rethink more fundamentally the meaning of the state variables associated with water vapor in these different phases.
The third part will involve modifying the convection parameterization in LMDZ to take account of the sub-grid variability of water.

Once the parameterization has been completed, the candidate will study the impact of taking this coupling into account on the simulation of the convective life cycle and the transition between shallow and deep convection. Analysis of LES simulations of convective events using the non-hydrostatic MESO-NH model will also be used to explore possible relationships between water variability and convective plume properties (collaboration with Catherine Rio, CNRM).

Contexte de travail

Completion of a thesis at IPSL on the Jussieu site (Paris) under the supervision of the scientific manager (LMD/IPSL).
The thesis work will contribute to PC7 of the Traccs project on improving the physics of global climate models. It will benefit from the collective nature of the work on the LMDZ model, and from the organization of the national community around these issues within the framework of the Dephy national research group. It will also benefit from the machine learning tools developed by the team in collaboration with MétéoFrance and the University of Exeter, enabling model parameters to be automatically readjusted after the introduction of a new parameterization. The arrival of these tools has reopened work on parameterizations, which had been particularly active for some years.

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.