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

• Published in: Biogeosciences
• Main Authors: Álvarez, M., Lo Monaco, C., Tanhua, T., Yool, A., Oschlies, A., Bullister, J.L., Goyet, C., Metzl, N., Touratier, F., McDonagh, E., Bryden, H.L.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2009
• Subjects: Antarctic; The Antarctic; Indian; Antarc*
• Online Access: https://eprints.soton.ac.uk/71936/; http://www.biogeosciences.net/6/681/2009/bg-6-681-2009.html

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 (?C*, 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 ?C* method. Significant CFC-12 and CCl4 values are detected in bottom waters, associated with Antarctic Bottom Water. In this layer, except for ?C* and OCCAM, the other methods detect significant CANT values. Consequently, the lowest inventory is calculated using the ?C* 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