Machine-learning prediction of tipping with applications to the Atlantic Meridional Overturning Circulation

The paper’s claims and methodology (parameter-adaptable reservoir computing trained on noisy pre-tipping data; detection via “abnormal behavior”) are consistent with its own models and reported windows for AMOC collapse. The candidate solution independently derives the deterministic bifurcation structures (1D fold; 2D SNLC via an explicit amplitude equation) and provides a more detailed identifiability argument for why small noise enables learning the drift pre-tipping. Its quantitative windows match the paper’s figures. Minor presentation ambiguities in the paper (e.g., m’s definition in the 1D model; 2040–2065 vs 2040–2066) do not affect correctness. Overall, both are correct; the candidate employs a more explicit dynamical-systems proof sketch than the paper.