Climate & Environmental Modelling

Climate science involves modelling enormously complex systems - atmosphere, oceans, ice sheets, ecosystems - with countless interacting variables across different timescales. Traditional climate models require immense computational resources and still operate at relatively coarse resolutions. AI is helping in two key ways: improving the speed and resolution of existing models, and finding patterns in observational data that improve predictions. Machine learning can emulate expensive physics-based simulations at a fraction of the computational cost, allowing researchers to run more scenarios and explore uncertainties more thoroughly. AI is also being applied to weather forecasting, where models like Google DeepMind's GraphCast have shown they can produce accurate short-term forecasts faster than traditional methods. For environmental monitoring, AI analyses satellite imagery to track deforestation, ice melt, ocean temperatures, and biodiversity changes at a scale that manual analysis could never achieve. The limitation is that climate systems involve genuinely novel conditions - we are heading into territory the Earth hasn't experienced before - and AI models trained on historical data may struggle with truly unprecedented scenarios.