Abstract

Abstract. The Arabian Sea (AS) was confirmed to be a net emitter of CO2 to the atmosphere during the international Joint Global Ocean Flux Study program of the 1990s, but since then few in situ data have been collected, leaving data-based methods to calculate air–sea exchange with fewer and potentially out-of-date data. Additionally, coarse-resolution models underestimate CO2 flux compared to other approaches. To address these shortcomings, we employ a high-resolution (1/24∘) regional model to quantify the seasonal cycle of air–sea CO2 exchange in the AS by focusing on two main contributing factors, pCO2 and winds. We compare the model to available in situ pCO2 data and find that uncertainties in dissolved inorganic carbon (DIC) and total alkalinity (TA) lead to the greatest discrepancies. Nevertheless, the model is more successful than neural network approaches in replicating the large variability in summertime pCO2 because it captures the AS's intense monsoon dynamics. In the seasonal pCO2 cycle, temperature plays the major role in determining surface pCO2 except where DIC delivery is important in summer upwelling areas. Since seasonal temperature forcing is relatively uniform, pCO2 differences between the AS's subregions are mostly caused by geographic DIC gradients. We find that primary productivity during both summer and winter monsoon blooms, but also generally, is insufficient to offset the physical delivery of DIC to the surface, resulting in limited biological control of CO2 release. The most intense air–sea CO2 exchange occurs during the summer monsoon when outgassing rates reach ∼ 6 molCm-2yr-1 in the upwelling regions of Oman and Somalia, but the entire AS contributes CO2 to the atmosphere. Despite a regional spring maximum of pCO2 driven by surface heating, CO2 exchange rates peak in summer due to winds, which account for ∼ 90 % of the summer CO2 flux variability vs. 6 % for pCO2. In comparison with other estimates, we find that the AS emits ∼ 160 Tg C yr−1, slightly higher than previously reported. Altogether, there is 2× variability in annual flux magnitude across methodologies considered. Future attempts to reduce the variability in estimates will likely require more in situ carbon data. Since summer monsoon winds are critical in determining flux both directly and indirectly through temperature, DIC, TA, mixing, and primary production effects on pCO2, studies looking to predict CO2 emissions in the AS with ongoing climate change will need to correctly resolve their timing, strength, and upwelling dynamics.

Highlights

  • The global ocean represents a major reservoir of inorganic carbon on the planet’s surface (40× atmosphere) and up to the present has on average acted to uptake ∼ 23 % of the 11 Gt excess anthropogenic carbon (Friedlingstein et al, 2020; Ciais et al, 2013; Khatiwala et al, 2009)

  • Joint Global Ocean Flux Study (JGOFS) data still represent the greatest source of data for current de facto standard global products, such as Takahashi et al (2009), who produced a global climatology of pCO2 and CO2 flux gridded onto a 4◦ × 5◦ grid using a horizontal advection–diffusion scheme

  • dissolved inorganic carbon (DIC), and total alkalinity (TA) all show their usual nutrientlike profiles, while oxygen is its minimum within the oxygen minimum zone (OMZ)

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Summary

Introduction

The global ocean represents a major reservoir of inorganic carbon on the planet’s surface (40× atmosphere) and up to the present has on average acted to uptake ∼ 23 % of the 11 Gt excess anthropogenic carbon (Friedlingstein et al, 2020; Ciais et al, 2013; Khatiwala et al, 2009). JGOFS carbon data were first used to create linear statistical models, which were extrapolated over a greater region of the AS to produce larger-scale estimates of seasonal CO2 flux showing emission to the atmosphere (Sabine et al, 2000; Sarma, 2003; Bates et al, 2006). Neural networks have been applied instead of simpler statistical models to likewise produce global climatologies, such as Landschützer et al (2015) (hereafter L15) on an increasedresolution 1◦ × 1◦ grid. All these different methodologies, of differing sophistication, still rely on the availability of in situ data

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