The paper presents a valuable review of state-of-the-art large ensemble methodology, comparing observational-based analogues and model-generated perturbations with the aim of studying the impact of long-term variability on European past, present and future climate. This work is helpful as a theoretical and methodological benchmark for a number of open issues in ensemble modeling, that are currently the object of intense discussions in the research community. Given that this topic very much relates to the work done within the Coupled Model Intercomparison Project, and ultimately to the redaction of the IPCC Assessment Reports, the paper is of major interest for the research community as well as for the broader public.

This paper tests the ability of Artificial Intelligence methods, and more specifically Deep Learning, to eliminate the Gaussian noise that disturbs the data of a dynamic system. The authors demonstrate this using a highly chaotic model as a hard test case.