Climate Model

Climate models aim to simulate Earth's climate system, primarily to project future climate change and understand its impacts. Current research heavily focuses on integrating machine learning (ML) techniques, such as neural networks (including convolutional, recurrent, and transformer architectures), and reinforcement learning, into traditional physics-based models to improve accuracy, efficiency, and uncertainty quantification. This hybrid approach addresses limitations in representing subgrid-scale processes and computationally expensive simulations, ultimately enhancing the reliability of climate projections and informing mitigation and adaptation strategies.