Turbocharging the race to protect nature and climate with AI

Understanding climate complexity for better forecasting

If hurricane warnings were taken more seriously because they were highly accurate, could more lives be saved? If we'd foreseen the current state of our climate fifty years ago, could more have been done to curb global temperature rise?

As the climate warms, the Earth's natural systems are starting to behave in increasingly unpredictable ways. The models behind both short and long-term climate forecasts are getting more complex, and huge amounts of data being gathered, as scientists scramble to work out what's going on. Machine learning, and software engineers, are becoming vital.

"Reliable forecasts of future climate trends - like temperature rise and sea-level change - are crucial for policy-makers trying to plan for the impacts of climate change," saysDr Joe Wallwork, a Research Software Engineer at Cambridge's Institute of Computing for Climate Science (ICCS), adding:

"We need better early warning systems to accurately forecast when and where extreme events will occur."

"A lot of climate models are limited in resolution," adds Dr Jack Atkinson, also a Research Software Engineer at ICCS. "Climate processes can happen at very small scales, and machine learning can help us improve the representation of these processes in models."

Atkinson is lead developer of FTorch , a software that bridges the gap between traditional climate models and the latest machine learning tools. This breakthrough is improving climate predictions by better representing small-scale processes that are challenging to capture in models. FTorch is now used by major institutions, including the National Center for Atmospheric Research, to enhance climate simulations and inform global policy.

Models in development by the team, which use FTorch, use fewer assumptions and more real climate data. They're powering the science of many climate studies - from sea-ice change, to cloud behaviour, to greenhouse gases in the atmosphere.

They're helping to make climate predictions faster , which has important climate implications too. "It's an unfortunate irony that climate models require quite a bit of energy to run," says Wallwork. "With machine learning we can make the models more efficient, running faster so we can potentially lower our emissions."