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.

进行了观测系统模拟实验（OSSE），以识别使用贝叶斯反演方法在印度洋进行海表pCO ₂测量的潜在位置。从SOCATv3版本开始，印度洋的pCO ₂数据有限。为了改善我们对该区域的建模，我们需要确定在哪里以及哪种观测系统将为其成本带来最大的好处或收益。在印度洋现有的RAMA和OMNI系泊系统中安装pCO ₂传感器的潜在好处，Bio-Argo浮标的潜力（使用pH测量）以及实施正在进行中的pCO采样的机会船计划（SOOP）_2个被评估。将溶解的无机碳的成本函数作为模型状态向量，并将CO ₂通量失配作为误差源最小化，并估算了不同季节在整个盆地范围内CO ₂通量不确定性的降低。通过在现有的RAMA和OMNI系泊设备中安装pCO ₂传感器，可以在不同季节将最大通量不确定性降低至30％。人们可能会认为，与安装基于系泊的pCO ₂传感器相比，大约20种Bio-Argos仍然是正确的选择，并且可以将不确定性降低多达50％，并具有在1000-2000 m的表面下进行轮廓分析的其他好处。但是，单轨SOOP有可能将不确定性降低约62％_。这项研究确定了重要的RAMA和OMNI系泊设备以及SOOP轨道，以观察印度洋的pCO ₂。

普通语言摘要。

CO ₂（pCO ₂）信息的表面海洋分压对于估算海空CO ₂交换至关重要。与其他全球热带和南部海洋相比，该参数从印度洋可获得的最少。迄今为止，还没有通过常规监测来测量印度洋的地表海洋pCO ₂，例如通过将仪器安装到系泊设备上或通过任何机会船进行采样来进行。因此，在印度洋开始pCO ₂观测的需求很大。但是，出现的一个关键问题是在哪里部署pCO ₂印度洋上的各种乐器，可以用有限的资源来学习最多的东西。这项研究使用逆建模技术解决了这个问题。研究发现，印度洋现有的系泊设备能够容纳pCO ₂传感器，而来自这些传感器的数据可将地表海-空CO ₂通量估算的不确定性降低四分之一。相反，Bio-Argo带有pH传感器的浮子，正在进行的pCO ₂采样船可能会分别受益于将其降低多达50％和62％而受益。