Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Irrigation accounts for~ 70% of global freshwater withdrawals and~ 90% of consumptive
water use, driving myriad Earth system impacts. In this Review, we summarize how irrigation …

Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …

Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

Rapid advances in hardware and software, accompanied by public-and private-sector
investment, have led to a new generation of data-driven computational tools. Recently, there …

Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical
transformations. Despite a key role in regulating the Earth system, terrestrial ecology has …

Deep learning (DL)-assisted inverse map** has shown promise in hydrological model
calibration by directly estimating parameters from observations. However, the increasing …

Climate over land—where humans live and the majority of food is produced—is changing
rapidly, driving severe impacts through extreme heat, wildfires, drought and flooding. Our …

Abstract Machine learning models can emulate chemical transport models, reducing
computational costs and enabling more experimentation. We developed emulators to predict …