Research Highlights

Subseasonal to seasonal climate forecasts in the US depends heavily on atmospheric and oceanic conditions in the tropical Indian and Pacific Ocean regions. While ENSO dominates seasonal predictability, the primary source of global predictability on subseasonal timescales is the MJO. To understand how ENSO and MJO interact, the authors isolated both MJO and ENSO signals and found that depending on the simultaneous location of the MJO convection and the background state of ENSO, the two signals can either enhance or mask each other.

To improve atmospheric and oceanographic monitoring, a new type of autonomous marine vehicle, the Saildrone, has been developed and deployed in over 40 cruises from which data are publicly available. Coupled with data from other sources such as satellites, Saildrone measurements could be useful for future algorithm and numerical model improvements, particularly at the fine spatial scale and in complex and previously data-sparse ocean regions.

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.