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 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.

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 study suggests a necessary shift in the paradigm of nonlinear dynamical systems analysis in climate science, ecology, and beyond. The author delves into a relatively unexplored facet of critical transitions, which is of great importance in examples within the Earth and environmental sciences. In particular, she explains mechanisms based on these ideas in examples of systems that describe population dynamics and climate transitions.

This paper is a review of very high quality of works done on an aspect which is absolutely fundamental for climate science

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.

Climate change is causing rapid temperature increases in the polar regions. A fundamental question is whether these temperature increases are reversible. If we control carbon dioxide emissions will the temperatures revert, or will we have passed a tipping point beyond which return to the present state is impossible? We address this question with a column model of the Arctic atmosphere based on mathematical representations of the primary physical features, including the Schwarzschild two-stream equations for the long wave radiation, the Clausius-Clapeyron equation for water vapour concentrations, atmosphere absorption characteristics dependent on carbon dioxide and water vapour concentration levels, and nonlinear albedo response to surface temperature. We analyzed solutions of our model under four representative carbon pathways (RCPs) extending into the future. Under the strongest mitigation pathway (which represents far swifter carbon emission reductions than presently seen), the Arctic shows a mild increase in temperature that is reversible. Under the highest carbon pathway (which represents a continuation of the status quo with regard to emissions), the Arctic climate undergoes an irreversible bifurcation to a warm steady state, corresponding to annually ice-free conditions, prior to the end of this century. Under the two intermediate pathways, temperatures increase substantially and the system enters a state of bi-stability where an external perturbation could possible cause an irreversible switch to warm, ice-free state.