The federation
Intranet
Models
The C2H team develops / participates in the development / or uses various numerical models. Here is the list of these models :
MAR is a model representing the dynamical and thermodynamical evolution of the atmosphere over a regional domain. MAR is mainly developed in France (IGE, C2H team) and in Belgium (U. Liège), and includes a detailed representation of the surface processes in snow covered regions. The CryoDyn team uses MAR to simulate the surface conditions on ice caps and glaciers, such as the surface mass balance that controls glacier dynamics and the melting that influences the stability of ice shelves. Contact : Nicolas Jourdain Hubert Gallée
LMDZ is an atmospheric general circulation model developed since the 1970s at the Dynamical Meteorology Laboratory, with variations giving terrestrial and planetary versions (Mars, Titan, Venus, giant planets, exo-planets). In its terrestrial version, LMDZ is the atmospheric component of the Integrated Climate Model of the IPSL. LMDZ applications at the IGE are mainly centered on the Arctic and the Antarctic.
WRF (Weather Research and Forecasting) model is a numerical weather prediction model, also used in atmospheric research as a dynamical downscaling tool. Its limited area (non-hydrostatic) configuration allows to perform simulations with high spatio-temporal resolution - e.g. 1km ; 1h - in order to model local atmospheric processes associated for example with a complex orography.
ORCHIDEE is a global surface model that represents the core of hydrology and vegetation processes at scales relevant to climate modeling at scales beyond the kilometer ; it allows the simulation and analysis of energy, water and carbon flows at the continental surface. It is coupled to LMDZ and is therefore part of the IPSL coupled climate models, but it can also be used in uncoupled mode.
SMRT is a new generation microwave model to compute thermal emission and backscatter of snowpacks. Initiated in an ESA project on snow microstructure in 2015, it is nowaday a comprehensive framework to conduct simulations for a wide range of media and electromagnetic conditions. The model is highly modular allowing switching between different scattering theories, snow microstructure representation, ice permittivity formulations, radiative transfer solving methods, etc. The model is also highly extensible and fully open to community developments. The model is equipped with a rich ecosystem of tools and a comprehensive documentation to lower the learning barrier for beginners.
ALPGM is a parameterized model of glacier evolution, based on machine learning. The mass balances (MB) are simulated with a deep learning or Lasso (regularized multilinear regression) neural network. Glacier dynamics are parameterized using glacier-specific delta-h functions (Huss et al. 2008). The model has so far been implemented in the French Alps, using climatic forcing for past periods (SAFRAN, Durand et al. 1993) and future periods (ADAMONT, Verfaillie et al. 2018).