Publications by authors named "Sungduk Yu"
Article Synopsis
- Climate change projections rely on physical models that struggle with small-scale processes, leading to uncertainties in predictions.
- Recent machine learning algorithms show potential for improving these models but often fail when applied to new climate conditions they weren't originally trained on.
- The proposed "climate-invariant" ML framework integrates physical knowledge into machine learning, enhancing accuracy and adaptability across various climate scenarios and improving Earth system modeling.
Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.
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