Research Highlights
We aim to feature the latest research results from US scientists whose published paper features work that is sponsored by one or more sponsoring agency programs of US CLIVAR (NASA, NOAA, NSF, DOE, ONR). Check out the collection of research highlights below and sort by topic on the right. Interested in submitting an article for consideration? See our Research Highlight Submission Guidelines page for more information.
Published in Journal of Climate, Karnauskas (2025) re-examined a collection of historical estimates made since 1961, as well as drifters and other modern observations and state estimates, to provide new constraints on this key variable.
Shackelford et al. (2025) present a framework for identifying state-dependent prediction skill using neural networks and self-organizing maps (SOMs), and offer an example application to prediction of North Atlantic atmospheric circulation.
Larson and McMonigal (2025) analyze two climate model ensemble experiments, one with internal WDOC variability and one without, to determine how much ocean circulation variability impacts when anthropogenic SST signals emerge. They find that overall, internal WDOC variability delays the time of emergence of anthropogenic SST signals everywhere, with the longest delays occurring in the global tropics and other dynamically active regions of the ocean.
Despite rising greenhouse gas concentrations, sea surface temperature (SST) in the eastern equatorial Pacific has shown little to no warming since the mid-20th century. The peculiar cold tongue response and the associated strengthening of the zonal SST gradient stand in stark contrast to most climate model simulations, which typically simulate enhanced warming in the east and a weakening of the zonal SST gradient. Understanding this discrepancy is critical for improving projections of tropical Pacific climate and its global teleconnections. Jiang et al. (2025) examining long-term trends over 1958 to 2022, identifies a key reason for the mismatch: models fail to generate subsurface cooling through realistic wind-driven circulation changes or to effectively communicate that cooling to the surface through upwelling and mixing.
Ehsan et al. analyzed 253 real-time Niño3.4 index forecasts—a key ENSO indicator measuring sea surface temperature anomalies in the central-eastern Pacific (5°S–5°N, 120°–170°W). The study found that DYN models (multimodal means of dynamical models) outperformed STAT models (multimodal means of statistical models), particularly for forecasts initiated between late boreal winter and spring months—known as the “spring predictability barrier,” a notoriously difficult period for ENSO forecasting. DYN models achieved 60% accuracy in predicting El Niño onsets up to three months in advance, whereas STAT models performed significantly worse.