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.
Modeling
Posted inEditors' Highlights
A City’s Challenge of Dealing with Sea Level Rise
A well-developed case study in Ho-Chi Min City, Vietnam, exemplifies how other mega-cities located on deltas could face the major challenge of adapting to rising sea-level.
Posted inResearch Spotlights
New Model Simulates Faults and Folds Shaping Each Other
A new model simulates how faulting and folding deep in Earth’s crust shape the way rocks fold and cause earthquakes.
Posted inResearch Spotlights
Next-Generation Climate Models Could Learn, Improve on the Fly
Scientists propose development of new models that use machine learning techniques to reduce uncertainties in climate predictions.
Posted inEditors' Highlights
Improving Temperature Forecasts in the Upper Atmosphere
Scientists are blending output from multi-year model runs to improve temperature forecasts in regions where satellites experience “drag,” in the hopes of avoiding future spacecraft collisions.
Posted inScience Updates
Modeling Global Change Ecology in a High–Carbon Dioxide World
Ignite-style Session, Ecological Society of America Annual Meeting; Portland, Oregon, 11 August 2017
Posted inResearch Spotlights
Modeling Storm Evolution
A “moist shell” makes all the difference in how some storms evolve.
Posted inEditors' Highlights
A Powerful New Tool to Analyze and Calibrate Earth System Models
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.
Posted inOpinions
Climate Models Are Uncertain, but We Can Do Something About It
Model simulations of many climate phenomena remain highly uncertain despite scientific advances and huge amounts of data. Scientists must do more to tackle model uncertainty head-on.
Posted inEditors' Vox
The Challenges of Drought Prediction
Advances in dynamical modeling and the use of hybrid methods have improved drought prediction, but challenges still remain to improve the accuracy of drought forecasting.