Data Driven Climate

Data-driven climate modeling uses machine learning to improve the accuracy and efficiency of climate simulations. Current research focuses on developing stable and physically consistent models, often employing neural networks (including convolutional and Fourier neural operators) to learn complex climate dynamics, particularly subgrid-scale processes. A key challenge is ensuring these models generalize well across diverse climate regimes and avoid spurious correlations, leading to efforts incorporating physical constraints into the learning process. This approach holds significant potential for refining climate projections and reducing uncertainties in climate change predictions.