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.

The authors make innovative use of seismic data to gain new insights into ocean dynamics, particularly regarding nonlinear internal waves and mixing processes.

This is an interesting contribution to the long-running debate on the anisotropic scaling of atmospheric dynamics, confirming that it is neither 3D nor 2D, but rather 23/9 D. This is of interest to the whole atmospheric community.

This is a timely paper given the recent rise in data-driven and AI-based weather forecasting. It offers two key contributions. First, the paper provides (potentially the first, but at least one of the first) comparisons of AI-based and physics-based weather forecasting models based on station data (rather than the commonly used comparisons based on gridded ERA5 data). And second, the paper assesses and quantifies the effect of statistical post-processing on forecasts from AI-based weather models, which may also be the first of its kind.

This is a new and timely contribution to the blocking phenomena in atmospheric circulation and extremes that result. The latter are stimulating and of fairly general interest to the non-linear geoscience community and beyond.

This letter aims to synergistically combine multifractals and climate network theory to better understand geophysical processes. Multifractals quantify their own variability and intermittency across a wide range of scales, while climate networks reveal their own long-range nonlinear dependencies at the observational scale. This novel methodology is introduced in the context of the Indian Monsoon, highlighting the multifractality of climate networks and showing how to upscale them.