Climate Simulation

Climate simulation aims to accurately model Earth's climate system, primarily to understand and predict climate change impacts. Current research heavily utilizes machine learning, employing architectures like neural networks (including graph convolutional and diffusion models) to create faster, more efficient surrogate models that emulate computationally expensive Earth System Models (ESMs). This focus on data-driven approaches addresses the limitations of traditional ESMs, particularly in uncertainty quantification and high-resolution simulations, enabling more comprehensive analyses of climate scenarios and improved predictions of extreme weather events. The resulting improvements in efficiency and accuracy have significant implications for climate science and informing climate adaptation and mitigation strategies.