Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

A Georgia Tech-led review paper recently published in Nature Reviews Physics is exploring the ways machine learning is revolutionizing the field of climate physics — and the role human scientists ...

Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based ...

Deep learning is increasingly being used to emulate cloud and convection processes in climate models, offering a faster alternative to computationally intensive cloud-resolving simulations. However, ...

Global climate models capture many of the processes that shape Earth's weather and climate. Based on physics, chemistry, fluid motion and observed data, hundreds of these models agree that more carbon ...

Vassili Kitsios is a senior research scientist at CSIRO, a co-chair of the Machine Learning for Climate and Weather Working Group of the Australian Climate Community Earth System Simulator National ...

As Raipur expands, the Kharun River Basin faces intensifying floods and sediment loads. Explore how climate change and ...

The accelerating impacts of climate change threaten the progress of sustainable development by exacerbating environmental ...