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| Titel |
Estimating the storage of anthropogenic carbon in the subtropical Indian Ocean: a comparison of five different approaches |
| VerfasserIn |
M. Álvarez, C. Monaco, T. Tanhua, A. Yool, A. Oschlies, J. L. Bullister, C. Goyet, N. Metzl, F. Touratier, E. McDonagh, H. L. Bryden |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 6, no. 4 ; Nr. 6, no. 4 (2009-04-27), S.681-703 |
| Datensatznummer |
250003647
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| Publikation (Nr.) |
 copernicus.org/bg-6-681-2009.pdf |
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| Zusammenfassung |
| The subtropical Indian Ocean along 32° S was for the first time
simultaneously sampled in 2002 for inorganic carbon and transient tracers.
The vertical distribution and inventory of anthropogenic carbon (CANT)
from five different methods: four data-base methods (ΔC*, TrOCA, TTD
and IPSL) and a simulation from the OCCAM model are compared and discussed
along with the observed CFC-12 and CCl4 distributions. In the surface
layer, where carbon-based methods are uncertain, TTD and OCCAM yield the
same result (7±0.2 molC m−2), helping to specify the surface
CANT inventory. Below the mixed-layer, the comparison suggests that
CANT penetrates deeper and more uniformly into the Antarctic
Intermediate Water layer limit than estimated from the much utilized ΔC*
method. Significant CFC-12 and CCl4 values are detected in bottom
waters, associated with Antarctic Bottom Water. In this layer, except for
ΔC* and OCCAM, the other methods detect significant CANT
values. Consequently, the lowest inventory is calculated using the ΔC*
method (24±2 molC m−2) or OCCAM (24.4±2.8 molC m−2)
while TrOCA, TTD, and IPSL lead to higher inventories (28.1±2.2,
28.9±2.3 and 30.8±2.5 molC m−2 respectively). Overall and
despite the uncertainties each method is evaluated using its relationship
with tracers and the knowledge about water masses in the subtropical Indian
Ocean. Along 32° S our best estimate for the mean CANT specific
inventory is 28±2 molC m−2. Comparison exercises for data-based
CANT methods along with time-series or repeat sections analysis should
help to identify strengths and caveats in the CANT methods and to
better constrain model simulations. |
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