National Oceanic and
Atmospheric Administration
United States Department of Commerce


FY 2009

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

Álvarez, M., C. Lo Monaco, T. Tanhua, A. Yool, A. Oschlies, J.L. Bullister, C. Goyet, N. Metzl, F. Touratier, E. McDonagh, and H.L. Bryden

Biogeosciences, 6(4), 681–703, doi: 10.5194/bg-6-681-2009 (2009)

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 (1C*, 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 carbonbased methods are uncertain, TTD and OCCAM yield the same result (7±0.2 molCm−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 1C* method. Significant CFC-12 and CCl4 values are detected in bottom waters, associated with Antarctic Bottom Water. In this layer, except for 1C* and OCCAM, the other methods detect significant CANT values. Consequently, the lowest inventory is calculated using the 1C* method (24±2 molCm−2) or OCCAM (24.4±2.8 molCm−2) while TrOCA, TTD, and IPSL lead to higher inventories (28.1±2.2, 28.9±2.3 and 30.8±2.5 molCm−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 molCm−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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