An observing system simulation experiment for Indian Ocean surface pCO2 measurements. (June 2021)

• Record Type: Journal Article
• Authors: Valsala, Vinu; Sreeush, M.G.; Anju, M.; Sreenivas, Pentakota; Tiwari, Yogesh K.; Chakraborty, Kunal; Sijikumar, S.
• Is Part Of: Progress in oceanography. Volume 194(2021)
• Publication Date: 2021-06
• Subjects: Observing System Simulation Experiment (OSSE) -- RAMA mooring -- OMNI mooring - Ship of Opportunity (SOOP) -- Bio-Argo - Indian Ocean pCO2; Oceanography -- Periodicals; 551.4605
• DOI: 10.1016/j.pocean.2021.102570
• Languages: English
• ISSNs: 0079-6611

Abstract

Highlights: OSSE is done for Indian Ocean (IO) pCO2 using RAMA + OMNI moorings, Bio-Argo floats and SOOP ship-tracks. pCO2 data from moorings and ship-tracks are tested for air-sea CO2 flux inversions. The maximum CO2 flux uncertainty reduction (UR) with potential pCO2 data is found. IO-UR with pCO2 data from moorings and ship-tracks are 30% and 62%, respectively. Ship-track pCO2 is more efficient than the mooring pCO2 data in CO2 flux inversion. Abstract: An observing system simulation experiment (OSSE) is conducted to identify potential locations for making surface ocean pCO2 measurements in the Indian Ocean using the Bayesian Inversion method. As of the SOCATv3 release, the pCO2 data is limited in the Indian Ocean. To improve our modeling of this region, we need to identify where and what observation systems would produce the most good or benefit for their cost. The potential benefits of installing pCO2 sensors in the existing RAMA and OMNI moorings of the Indian Ocean, the potential of Bio-Argo floats (with pH measurements), and the implementation of the ship of opportunity program (SOOP) for underway sampling of pCO2 are evaluated. A cost function of dissolved inorganic carbon as a model state vector and CO2 flux mismatch as the source of error is minimized, and the basin-wide CO2 flux uncertainty reduction is estimated for different seasons. The maximum flux uncertainty reduction achievable by installing pCO2 sensors in the existing RAMA and OMNI moorings is limited to 30% during different seasons. One may consider that around 20 Bio-Argos are still the right choice over installing mooring based pCO2 sensors and achieve uncertainty reduction up to 50% with additional benefit of profiling the sub-surface upto 1000–2000 m. However, a single track SOOP has the potential to reduce the uncertainty by approximately 62%. This study identifies vital RAMA and OMNI moorings and SOOP tracks for observing Indian Ocean pCO2 . Plain Language Summary. Surface ocean partial pressure of CO2 (pCO2 ) information is vital for estimating sea-to-air CO2 exchanges. This parameter is least available from the Indian Ocean as compared to other global tropical and southern oceans. There has been no effort made so far to measure surface ocean pCO2 in the Indian Ocean with routine monitoring such as by mounting instruments to moorings or by underway sampling via any ship of opportunity program. Therefore there is a considerable demand to start pCO2 observations in the Indian Ocean. However, one key question that emerges is where to deploy pCO2 instruments in the Indian Ocean to learn the most with limited resources. This study addresses this question with inverse modeling techniques. The study finds that the existing moorings of the Indian Ocean are capable of hosting pCO2 sensors, and data from those are useful to reduce the uncertainty in the surface sea-to-air CO2 flux estimation by a quarter magnitude. In contrast, the Bio-Argo floats with pH sensors, and the ship of opportunity underway sampling of pCO2 may benefit from reducing the same up to 50% and 62%, respectively.