Evaluating the ACE2 model in simulating extratropical climate and weather extremes

Among the several artificial intelligence global atmospheric models that have  recently put forward in the literature, the ACE2 stands out as a purely data-driven stable model that can be run over millennia, providing a rather reasonable representation of ENSO and to the global greenhouse gas forcing during the recent decades. In this talk, this model is evaluated in terms of its capability to generate its own internal variability, its representation of the local probability distribution of extreme winds and precipitation, the extratropical models of variability, such as the North Atlantic Oscillation and the Pacific North-America pattern, and their teleconnections to seasonal temperature and precipitation. 

Additionally, selected extratropical weather extremes have been simulated with the ACE2 model, using different initialization lead times. This set of extremes includes the Capella storm in the North Sea in January1976, the Ahr Valley flash flooding in Germany in July 2021, and others.

The ACE2 model in climate mode, trained with ERA5 reanalysis, is able to produce surprisingly realistic amplitude of internal climate variability and atmospheric teleconnection patterns involving near-surface temperature and precipitation, and atmospheric circulation in  upper tropospheric levels.

In weather prediction mode, the predictability of extremes benchmarked against the ERA5 data set is limited to a lead time of 2 days, and the simulated extremes may be temporally temporally shifted  by about one day. Their intensity is also somewhat weaker than the benchmark data set.

The model can also be used a extreme event attribution tool, as the model can be run under changed surface boundary conditions and greenhouse-gas forcing. A short assessment of its useful in this context will be also discussed in this presentation