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Showing 1–6 of 6 results
Advanced filters: Author: Janni Yuval Clear advanced filters

  • An observation-based storm-track proxy shows that the winter North Pacific storm track has shifted substantially polewards, suggesting regional impacts on precipitation and warming patterns, with implications for temperature variability and extreme events.

    • Rei Chemke
    • Janni Yuval
    Research
    Nature
    Volume: 649, P: 626-630

  • The degree to which the tropical circulation changes with warming is not well known. Here, the authors use an emergent constraint to show that the tropical Hadley circulation is weakening more intensely than previously thought, resulting in stronger precipitation increases in subtropical regions.

    • Rei Chemke
    • Janni Yuval
    Research
    Nature Climate Change
    Volume: 15, P: 287-292

  • A hybrid model that combines a differentiable solver for atmospheric dynamics with machine-learning components is capable of weather forecasts and climate simulations on par with the best machine-learning and physics-based methods.

    • Dmitrii Kochkov
    • Janni Yuval
    • Stephan Hoyer
    ResearchOpen Access
    Nature
    Volume: 632, P: 1060-1066

  • Analysis of sea-level pressure measurements shows that, in agreement with the latest suite of climate models, the Hadley circulation has considerably weakened in the Northern Hemisphere over recent decades, driven by anthropogenic emissions.

    • Rei Chemke
    • Janni Yuval
    Research
    Nature
    Volume: 617, P: 529-532

  • Southern mid-latitude winter storms are expected to intensify with emission increases, but it is unknown if such intensification has already emerged. Here, storms are shown to have intensified in recent decades, and current models considerably underestimate this, indicating more risk than projected.

    • Rei Chemke
    • Yi Ming
    • Janni Yuval
    Research
    Nature Climate Change
    Volume: 12, P: 553-557

  • Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.

    • Janni Yuval
    • Paul A. O’Gorman
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-10