Research Highlights

US CLIVAR aims to feature the latest research results from the community of scientists participating in our interagency-sponsored projects, working groups, panels, science teams, and workshops. Check out the collection of research highlights below and sort by topic on the right. 

A new collection of single model initial-condition large ensembles (SMILEs) can test statistical assumptions with a cleaner separation between sources of uncertainty in climate projections. In particular, the potential bias at regional scales or for variables with a lot of internal variability can be greatly reduced.

Internal variability and anthropogenic forcing have contributed to the widening of the tropical belt during the last several decades. To better understand the effect of individual anthropogenic drivers (including aerosols) on the tropical belt, Zhao, Allen, and coauthors utilize idealized simulations with very large single forcing perturbations in comprehensive coupled ocean-atmosphere models from the PDRMIP.

Zelinka and coauthors compared ECS values derived from CO2 quadrupling experiments conducted with CMIP6 and CMIP5 models and found that the latest models warm more than their predecessors by about 0.5˚C. The primary culprit for the enhanced warming was shown to be clouds.

A recent study uses large ensembles of an idealized general circulation model to demonstrate how episodic surface warming in the Arctic can lead to delayed responses in the stratosphere that persist for about two months, even in the absence of stationary waves.