AI IN SUSTAINABILITY
Speed, emissions and sustainable compute Traditional numerical weather prediction requires vast supercomputing resources, consuming large amounts of energy to run models at high resolution and high frequency. By contrast, once a machine learning model is trained, generating new forecasts – known as inference – can be tens of thousands of times faster and much less energy‐intensive.
“ AI models are not just slightly faster,” Kirstine stresses.“ They’ re tens of thousands of times faster than physics‐based models.” Fast inference opens up three major sustainability advantages:
• First, it enables forecasts to be run closer to real time, so more of the latest observational data can be assimilated, improving relevance for climate‐sensitive sectors such as renewable energy and agriculture.
• Second, the lower computational load allows models to run on edge devices closer to users, rather than exclusively on centralised supercomputers. This can reduce data transfer, cut latency and open up resilient local forecasting for regions with weaker infrastructure.
132 April 2026