Generalization ability of emulators in reproducing the physical parameterizations of the IPSL model

In an Atmospheric General Circulation Model (AGCM), the representation of subgrid-scale physical phenomena, also referred to as physical parameterizations, requires computational time which constrains model numerical efficiency. However, the development of emulators based on Machine Learning offers a promising alternative to traditional approaches.We have developed offline emulators of the atmospheric component named ICOLMDZ (for DYNAMICO and LMDZ) of the IPSL climate model, in an idealized aquaplanet configuration, with the aim of emulating all the parameterizations, i.e. the LMDZ atmospheric physics component. While the results are quite promising, some fundamental questions are raised, particularly in terms of the generalization of the emulation process to meteorological conditions not seen by the emulator. This step is important for adopting the emulator as a substitute for traditional parameterizations.This question of generalization, which relates to the ability of emulators to infer and adapt to new system states, has been studied in experiments linked to climate change. Indeed, we first investigated the performance of our emulators, trained on an aquaplanet configuration, in extrapolating the emulation process to new aquaplanets where boundary conditions are modified in order to simulate climates that are warmer and colder than the climate on which emulators are trained. The results reveal the potential of our aquaplanet emulators to reproduce the physical parameterizations of new climates. However, we also showed the limitations of these aquaplanet emulators since they encountered difficulties to generalize on a realistic configuration, i.e. when continents, topography and sea ice area are included.This study encourages the coupling of emulators with the dynamic parts called DYNAMICO in order to better assess the relevance of the learning process, while analyzing the stability of the simulations obtained.