model sensitivity studies of the decrease in atmospheric carbon tetrachloride
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ID: 149935
2016
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Abstract
Carbon tetrachloride (CCl4) is an ozone-depleting substance, which is controlled by
the Montreal Protocol and for which the atmospheric abundance is decreasing.
However, the current observed rate of this decrease is known to be slower
than expected based on reported CCl4 emissions and its estimated overall
atmospheric lifetime. Here we use a three-dimensional (3-D) chemical
transport model to investigate the impact on its predicted decay of
uncertainties in the rates at which CCl4 is removed from the atmosphere
by photolysis, by ocean uptake and by degradation in soils. The largest sink
is atmospheric photolysis (74 % of total), but a reported 10 %
uncertainty in its combined photolysis cross section and quantum yield has
only a modest impact on the modelled rate of CCl4 decay. This is partly
due to the limiting effect of the rate of transport of CCl4 from the
main tropospheric reservoir to the stratosphere, where photolytic loss occurs.
The model suggests large interannual variability in the magnitude of this
stratospheric photolysis sink caused by variations in transport. The impact
of uncertainty in the minor soil sink (9 % of total) is also relatively
small. In contrast, the model shows that uncertainty in ocean loss (17 %
of total) has the largest impact on modelled CCl4 decay due to its
sizeable contribution to CCl4 loss and large lifetime uncertainty range
(147 to 241 years). With an assumed CCl4 emission rate of
39 Gg year−1, the reference simulation with the best estimate of loss
processes still underestimates the observed CCl4 (overestimates the
decay) over the past 2 decades but to a smaller extent than previous
studies. Changes to the rate of CCl4 loss processes, in line with known
uncertainties, could bring the model into agreement with in situ surface and
remote-sensing measurements, as could an increase in emissions to around
47 Gg year−1. Further
progress in constraining the CCl4 budget is partly limited by systematic
biases between observational datasets. For example, surface observations from
the National Oceanic and Atmospheric Administration (NOAA) network are larger
than from the Advanced Global Atmospheric Gases Experiment (AGAGE) network but have shown a
steeper decreasing trend over the past 2 decades. These differences imply a
difference in emissions which is significant relative to uncertainties in the
magnitudes of the CCl4 sinks.
| Reference Key |
chipperfield2016atmosphericmodel
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| Authors | ;M. P. Chipperfield;M. P. Chipperfield;Q. Liang;Q. Liang;M. Rigby;R. Hossaini;S. A. Montzka;S. Dhomse;W. Feng;W. Feng;R. G. Prinn;R. F. Weiss;C. M. Harth;P. K. Salameh;J. Mühle;S. O'Doherty;D. Young;P. G. Simmonds;P. B. Krummel;P. J. Fraser;L. P. Steele;J. D. Happell;R. C. Rhew;J. Butler;S. A. Yvon-Lewis;B. Hall;D. Nance;F. Moore;B. R. Miller;J. W. Elkins;J. J. Harrison;J. J. Harrison;C. D. Boone;E. L. Atlas;E. Mahieu |
| Journal | Journal of agricultural and food chemistry |
| Year | 2016 |
| DOI |
10.5194/acp-16-15741-2016
|
| URL | |
| Keywords |
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