General Circulation Model

General Circulation Models (GCMs) are complex computer simulations used to predict weather and climate, but they struggle with accurately representing small-scale processes like cloud formation. Current research focuses on improving GCMs by integrating machine learning techniques, such as neural networks and Gaussian processes, to better capture sub-grid variability and correct biases in existing models. This hybrid approach, combining physics-based modeling with data-driven methods, aims to enhance the accuracy and efficiency of climate projections, particularly for extreme weather events and long-term climate change scenarios. Improved GCMs will lead to more reliable predictions, informing crucial decisions in areas like disaster preparedness and resource management.