Assessing the internal consistency of the CARINA database in the Indian sector of the Southern Ocean

Abstract. Carbon and carbon-relevant hydrographic and hydrochemical ancillary data from previously not publicly available cruises were retrieved and recently merged to a new data base, CARINA (CARbon IN the Atlantic). The initial North Atlantic project, an international effort for ocean carbon synthesis, was extended to include the Arctic Mediterranean Seas (Arctic Ocean and Nordic Seas) and all three sectors of the Southern Ocean. Of a total of 188 cruises, 37 cruises are part of the Southern Ocean. The present work focuses on data collected in the Indian sector (20° S–70° S; 30° E–150° E). The Southern Indian Ocean dataset covers the period 1992–2004 and includes seasonal repeated observations. Parameters including salinity, dissolved inorganic carbon (TCO₂), total alkalinity (TA), oxygen, nitrate, phosphate and silicate were examined for cruise-to-cruise and overall consistency. In addition, data from an existing, quality controlled data base (GLODAP) were introduced in the CARINA analysis to improve data coverage in the Southern Ocean. A global inversion was performed to synthesize the information deduced from objective comparisons of deep measurements (>1500 m) at nearby stations (generally <220 km). The corrections suggested by the inversion were allowed to vary within a fixed envelope, thus accounting for ocean interior variability. The adjustments applied to CARINA data and those recommended for GLODAP data, in order to obtain a consistent merged dataset, are presented and discussed. The final outcome of this effort is a new quality controlled data base for TCO₂ and other properties of the carbon system that can now be used to investigate the natural variability or stability of ocean chemistry and the accumulation of anthropogenic carbon. This data product also offers an important new synthesis of seasonal to decadal observations to validate ocean biogeochemical models in a region where available historical data were very sparse.