Estimating the storage of anthropogenic carbon in the subtropical Indian Ocean: a comparison of five different approaches

Álvarez, M.; Lo Monaco, C.; Tanhua, T.; Yool, A. ORCID: https://orcid.org/0000-0002-9879-2776; Oschlies, A.; Bullister, J.L.; Goyet, C.; Metzl, N.; Touratier, F.; McDonagh, E.; Bryden, H.L.. 2009 Estimating the storage of anthropogenic carbon in the subtropical Indian Ocean: a comparison of five different approaches. Biogeosciences, 6 (4). 681-703. https://doi.org/10.5194/bg-6-681-2009

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Official URL: http://www.biogeosciences.net/6/681/2009/bg-6-681-...

Abstract/Summary

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

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/bg-6-681-2009
ISSN:	1726-4170
Date made live:	11 Jan 2010 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/171936

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.5194/bg-6-681-2009

ISSN:

1726-4170

Date made live:

11 Jan 2010 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/171936