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| Titel |
Stratospheric age-of-air trends: Reanalysis v. climate models |
| VerfasserIn |
Beatriz Monge-Sanz, Dick Dee, Hans Hersbach, Adrian Simmons, Jose A. Parodi, Florian Haenel, Gabriele Stiller, Martyn Chipperfield, Wuhu Feng |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250151684
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| Publikation (Nr.) |
 EGU/EGU2017-16436.pdf |
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| Zusammenfassung |
| Knowing how the Brewer-Dobson circulation (BDC) has evolved in the recent past and will
continue to evolve is crucial for atmospheric composition in the UTLS and stratosphere, as
well as for feedbacks with climate. Most climate models have predicted an intensification of
the stratospheric circulation with the increase in greenhouse gases concentrations, which
translates into younger age-of-air (AoA) values modelled in the stratosphere. Nevertheless,
balloon and satellite observations do not agree with the widespread modelled trend
towards younger age-of-air for the recent past (Engel et al., 2009; Stiller et al., 2012;
Haenel et al. 2015). Furthermore, a few recent studies with chemistry transport
models (CTMs) driven by ERA-Interim reanalysis (Dee et al., 2011) have also shown
agreement with the observed trends and not with those from climate models (e.g.
Monge-Sanz et al., 2012; Diallo et al., 2012; Ploeger et al., 2015). To increase our
confidence in climate-chemistry projections, the causes for the apparent disagreement
in trends of age-of-air between observations and most climate models need to be
identified.
In this study we have carried out simulations with a CTM to assess the stratospheric
circulation with the ERA-Interim dataset produced by the European Centre for
Medium-Range Weather Forecasts (ECMWF), as well as with data produced from an
equivalent climate system. AoA trends from our model results with ERA-Interim fields are in
good agreement with the recent age-of-air studies based on observations and differ from the
results we obtain with the corresponding climate data. We will show that biases in the mean
AoA values are also different for these datasets compared to observations. In addition we
have used recent experimental datasets from the ECMWF system to identify potential causes
for the differences in AoA distribution and trends. The validation of our model results has
been performed against the new revised AoA dataset based on MIPAS SF6 observations
(Haenel et al., 2015).
References:
Dee et al., Q. J. R. Meteorol. Soc., doi:10.1002/qj.828, 2011.
Diallo et al., Atmos. Chem. Phys., doi:10.5194/acp–12–12 133–2012, 2012.
Engel et al., Nat. Geosci., doi:10.1038/ngeo388, 2009.
Haenel et al., Atmos. Chem. Phys., doi:10.5194/acp–15–13161–2015, 2015.
Monge-Sanz et al., Q. J. R .Meteorol. Soc., doi:10.1002/qj.1996, 2012.
Ploeger et al., J. Geophys. Res., doi:10.1002/2014JD022 468, 2015.
Stiller et al., Atmos. Chem. Phys., doi:10.5194/acp–12–3311–2012, 2012. |
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