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

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BIOGEOSCIENCES
卷 6, 期 4, 页码 681-703

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COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/bg-6-681-2009

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  1. Natural Environment Research Council [soc010008] Funding Source: researchfish
  2. NERC [soc010008] Funding Source: UKRI

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The subtropical Indian Ocean along 32 degrees 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 (Delta 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 Delta C* method. Significant CFC-12 and CCl4 values are detected in bottom waters, associated with Antarctic Bottom Water. In this layer, except for Delta C* and OCCAM, the other methods detect significant CANT values. Consequently, the lowest inventory is calculated using the Delta 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 degrees 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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