Cloud microphysics affect precipitation extremes on multiple time scales in climate models.
Researchers used deep learning methods to estimate the subsurface permeability of a watershed from readily available stream discharge measurements.
Monitoring data find that small spatial differences in snow cover, vegetation, and other factors shape how permafrost thaws.
A bottom-up approach quantifies the contributions of human-caused heating from building energy use during extreme heat events.
Computational work uses a Chicago neighborhood to understand and quantify climate effects on building energy use from changes in urban design.
A new way of representing river-groundwater exchanges paves the way for next-generation river network modeling.
Researchers find that fungal spores are most abundant during initial growth, while bacteria predominate during flowering and fruit development.
Machine Learning offers New Insights and New Parameterization for the path from Drizzle Drops to Warm Rain
Fine roots grow dramatically faster in an experimentally warmed peatland
Computers learn from a combination of experimental and evolutionary data to enhance the function of useful proteins.
A new model predicts small-scale differences in methane emissions from tropical soils on a hillside during drought and recovery.
Scientists demonstrate the value of a new global atmosphere model for the Energy Exascale Earth System Model.
