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| Titel |
Attributing Future Changes in Surface Temperature Variability to Thermal Advection |
| VerfasserIn |
Caroline Ely, Tim Woollings, Hylke de Vries, Ed Hawkins |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250097765
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| Publikation (Nr.) |
 EGU/EGU2014-13374.pdf |
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| Zusammenfassung |
| Assessing the projected changes in variability of surface temperature is a key step towards
assessing the future probability of extreme events such as cold spells and heat waves.
Furthermore, understanding the driving mechanisms behind such changes in variability
enables more confidence to be placed in model projections. A large fraction of present day
temperature variance is associated with thermal advection, as anomalous winds blow across
the land-sea temperature contrast for instance. This study investigates the extent to which this
mechanism may also explain projected changes in temperature variability up to the end of the
21st century.
Under greenhouse gas forcing there is expected to be an increase in land-sea temperature
contrasts in summer and a decrease in winter. In winter, the northern hemisphere
will also see decreased large scale meridional temperature gradients due to Arctic
amplification of the warming signal. In this study, it is found that the associated changes in
thermal advection are expected to lead to widespread changes in daily and monthly
temperature variability by the end of the twenty-first century. The study uses a
multiple regression analysis applied to ESSENCE, a 17 member ensemble of the
ECHAM5/MPI-OM climate model, to separate the contributions from changing
temperature gradients and changing circulation patterns. It will be shown that many
changes can be explained using only the changes in seasonal mean temperature
gradient.
A comparison with the CMIP5 suite of models will also be presented to highlight which
changes in variability are robust across climate models, and to demonstrate the temporal
evolution of the variability signal in model projections. |
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