Barnes Elizabeth A.

No Thumbnail Available
Last Name
Barnes
First Name
Elizabeth A.
ORCID
0000-0003-4284-9320

Search Results

Now showing 1 - 2 of 2
  • Article
    Daily to decadal modulation of jet variability
    (American Meteorological Society, 2018-01-29) Woollings, Tim ; Barnes, Elizabeth ; Hoskins, Brian ; Kwon, Young-Oh ; Lee, Robert W. ; Li, Camille ; Madonna, Erica ; McGraw, Marie ; Parker, Tess ; Rodrigues, Regina ; Spensberger, Clemens ; Williams, Keith
    The variance of a jet’s position in latitude is found to be related to its average speed: when a jet becomes stronger, its variability in latitude decreases. This relationship is shown to hold for observed midlatitude jets around the world and also across a hierarchy of numerical models. North Atlantic jet variability is shown to be modulated on decadal time scales, with decades of a strong, steady jet being interspersed with decades of a weak, variable jet. These modulations are also related to variations in the basinwide occurrence of high-impact blocking events. A picture emerges of complex multidecadal jet variability in which recent decades do not appear unusual. An underlying barotropic mechanism is proposed to explain this behavior, related to the change in refractive properties of a jet as it strengthens, and the subsequent effect on the distribution of Rossby wave breaking.
  • Article
    Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
    (National Academy of Sciences, 2022-11-22) Po-Chedley, Stephen ; Fasullo, John T. ; Siler, Nicholas ; Labe, Zachary M. ; Barnes, Elizabeth A. ; Bonfils, Céline J. W. ; Santer, Benjamin D.
    Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.