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The Makassar Strait, the main passageway of the Indonesian Throughflow (ITF), is an important component of Indo‐Pacific climate through its inter‐basin redistribution of heat and freshwater. Observational studies suggest that wind‐driven freshwater advection from the marginal seas into the Makassar Strait modulates the strait's surface transport. However, direct observations are too short (<15 years) to resolve variability on decadal timescales. Here we use a series of global ocean simulations to assess the advected freshwater contributions to ITF transport across a range of timescales. The simulated seasonal and interannual freshwater dynamics are consistent with previous studies. On decadal timescales, we find that wind‐driven advection of South China Sea (SCS) waters into the Makassar Strait modulates upper‐ocean ITF transport. Atmospheric circulation changes associated with Pacific decadal variability appear to drive this mechanism via Pacific lower‐latitude western boundary current interactions that affect the SCS circulation.

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio‐macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

The deep ocean has long been recognized as the reservoir that stores the carbon dioxide (CO₂) removed from the atmosphere during Pleistocene glacial periods. The removal of glacial atmospheric CO₂into the ocean is likely modulated by an increase in the degree of utilization of macronutrients at the sea surface and enhanced storage of respired CO₂in the deep ocean, known as enhanced efficiency of the biological pump. Enhanced biological pump efficiency during glacial periods is most easily documented in the deep ocean using proxies for oxygen concentrations, which are directly linked to respiratory CO₂levels. We document the enhanced storage of respired CO₂during the Last Glacial Maximum (LGM) in the Pacific Southern Ocean and deepest Equatorial Pacific using records of deglacial authigenic manganese, which form as relict peaks during increases in bottom water oxygen (BWO) concentration. These peaks are found at depths and regions where other oxygenation histories have been ambiguous, due to diagenetic alteration of authigenic uranium, another proxy for BWO. Our results require that the entirety of the abyssal Pacific below approximately 1,000 m was enriched in respired CO₂and depleted in oxygen during the LGM. The presence of authigenic Mn enrichment in the deep Equatorial Pacific for each of the last five deglaciations suggests that the storage of respired CO₂in the deep ocean is a ubiquitous feature of late‐Pleistocene ice ages.