Speaker
Description
Comprehensive Earth System Models (ESMs) are the key tools to model the dynamics of the Earth system and its climate, and in particular to estimate the impacts of increasing atmospheric greenhouse gas concentrations in the context of anthropogenic climate change. ESMs couple general circulation models (GCMs) of ocean and atmosphere with models of land surface processes, hydrology, ice, vegetation, atmosphere and ocean chemistry and carbon cycle model. Despite their remarkable success in reproducing observed characteristics of the Earth’s climate system, such as the spatial patterns of the increasing temperatures of the last century, there remain many great challenges for state-of-the-art Earth System Models such as the representation of extreme events, the multistability of components and the reduction of climate uncertainties in the models.
The recent years saw the advance of purely data-driven AI models. In Earth System Science, we have now AI weather models such as GenCast, Aurora or Pangu Weather that are competitive with process-based weather models. Despite their remarkable success in weather forecasting, they are ultimately limited on the longer time scales and scenarios we need for climate projections.
Can we still make use of AI models to address some of the aforementioned challenges in ESMs? In this talk, we will get an initial glimpse into how ESMs work, and what both the potentials and challenges are when we want to integrate AI approaches into these models.
