A new modeling framework highlights that urban greening is a sustainable solution to achieve environmental co-benefits in mitigating heat and carbon emissions.
Journal of Advances in Modeling Earth Systems (JAMES)
Posted inEditors' Highlights
Cumulus Cloud Botany in Large Domains
A new study provides a sample of shallow cumulus clouds simulated in domains 150-kilometers wide, enabling investigations of their structure and organization.
Posted inEditors' Highlights
New Aerosol Model Better Represents Black Carbon Properties
An improved representation for black carbon microphysical and optical properties alleviates overestimations of aerosol absorption efficiency in global climate models.
Posted inResearch Spotlights
Machine Learning Provides a Clearer Window into Ocean Motion
A new method could translate satellite information about sea surface heights into insights on current, heat flow, and—ultimately—climate change.
Posted inResearch Spotlights
Family Trees Clarify Relationships Among Climate Models
A new genealogy based on similarities in the computer codes of different climate models could improve studies that combine projections from multiple models.
Posted inEditors' Highlights
Using Bayesian Inference to Improve Sediment-Transport Models
A new Bayesian approach is used for the estimation and uncertainty quantification of unobservable parameters required to model tracer evolution in ocean sediment transport and tracer concentrations.
Posted inEditors' Highlights
Simulating the Journey of Pollen in the Atmosphere
A new study couples an emission and transport scheme of pollen from vegetation, and explores pollen’s evolution in different atmospheric conditions and its impacts on clouds and precipitation.
Posted inEditors' Highlights
Simulating Clouds on Arbitrary Grids in Any Spatial Direction
A new non-column based spectral element implementation of cloud microphysics enables full 3D flexibility in computing clouds and improves computational efficiency.
Posted inEditors' Highlights
Going with the Flow: New Tool Improves Sea Level Projections
By bringing together multiple data sources a new statistical method aims to improve the accuracy with which we might predict future ice melt in Greenland.
Posted inEditors' Highlights
New Machine Learning Parameterization Tested on Atmospheric Model
For the first time, a neural network parameterization of subgrid momentum transport is developed by training on a coarse-grained high-resolution atmospheric simulation.