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Closing the sea level budget (between observed rise and the sum of its causes) has been a challenge, is an ongoing effort and has primarily concerned the global mean. Here, the authors use machine learning to identify sub-areas with similar trends to close the sea level budget on a regional level, with much reduced errors compared with 1-degree grid points.

The paper is a guideline especially for educators that highlights the potential of a transformation towards diversified knowledge systems in curricula.

In the last decade it was discovered that autoxidation of monoterpenes produces highly oxidised organic molecules (HOM) in the atmosphere. These have low volatility and produce secondary organic aerosols that are relevant to climate and human health. Autoxidation involves organic peroxy radicals which undergo one or more intramolecular H-shifts with subsequent O2 addition leading to the formation of HOMs. The experimental and theoretical elucidation of the mechanism is challenging due to the large number and isomerism of possible intermediates and their numerous reaction pathways. In the present study, selective isotope labeling was combined with high-resolution mass spectrometry to greatly enhance the possibilities to identify relevant reaction pathways. Selective isotopic labeling of organic molecules is a powerful method for studying reaction mechanisms, and it is currently underutilized in the community. This is a great example of using this method, hopefully paving the way to more studies of this sort to come.