A task force of researchers set out to bridge the gap between the 2-week weather forecast and long-term climate predictions; their findings could help in forecasting the likelihood of extreme events.
Simulating the dynamic nature of plant root profiles in Earth system models improves the representation of the carbon and water cycles.
An evolving set of tools helps land surface model developers optimize the realism of their parameterizations for the next generation of weather and climate models.
The deformation of continents by groundwater can be measured locally by GPS or detected from satellites, but more precisely monitored when measurements are combined with a hydrologic model.
Atmosphere and land model development has historically been segregated but coupled processes crucial to prediction and extremes can be properly represented only with a holistic approach.
Polynomial chaos and Bayesian compressive sensing are applied to a land surface model to understand how large numbers of tunable parameters interact and may be optimized.
Soil moisture can elevate overnight temperatures, offsetting daytime cooling, especially over areas of strong land-atmosphere interactions.
A new model better explains seasonal variations in biomass.