Harmonization of global surface ocean pCO<sub>2</sub> mapped products and their flux calculations; an improved estimate of the ocean carbon sink

Abstract

Abstract. Air-sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air-sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate varies greatly, unnecessarily enhancing the uncertainties. Here we reduce some of these uncertainties by harmonizing an ensemble of products that interpolate surface ocean CO2 observations to near global coverage. We propose a common methodology to fill in missing areas in the products and to calculate fluxes and present a new estimate of the net flux. The ensemble data product, SeaFlux (Fay et al. (2021), doi.org/10.5281/zenodo.4133802, https://github.com/luke-gregor/SeaFlux), accounts for the diversity of the underlying mapping methodologies. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations that can result in an average error of 15 % in global mean flux estimates. We address differences in spatial coverage of the surface ocean CO2 between the mapping products which ultimately yields an increase in CO2 uptake of up to 19 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of an appropriately scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we derive an improved ensemble of surface ocean pCO2 and air-sea carbon flux estimates. The SeaFlux ensemble suggests a global mean uptake of CO2 from the atmosphere of 1.92 +/- 0.35 PgC yr-1. This work aims to support the community effort to perform model-data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.

A seamless ensemble-based reconstruction of surface ocean pCO<sub>2</sub> and air–sea CO<sub>2</sub> fluxes over the global coastal and open oceans

“…For a better assessment of the reconstruction uncertainty, future studies should include realistic uncertainties of these data, and also of local (sub-)skin effects of temperature and salinity as suggested in Watson et al (2020). Additional sources of uncertainty in the computation of air-sea fluxes are discussed by Wanninkhof (2014); Woolf et al (2019); Fay et al (2021). These studies have demonstrated the strong impact of different wind field products and model parameterizations on the gas transfer velocity k in Eq.…”

A monthly surface pCO<sub>2</sub> product for the California Current Large Marine Ecosystem

“…This variability is a result of ocean mixing, equilibration kinetics between the atmosphere and ocean, biological processes, and thermal effects on pCO2(sw). Filling temporal and spatial data gaps in the observational coverage of pCO2(sw) can therefore be challenging (Hauck et al, 2020;Fay et al, 2021) and a variety of strategies have been attempted over several decades (Takahashi et al, 1993;Rödenbeck et al, 2015), becoming even more prevalent and varied in the literature over time.…”

“…Briefly, statistical interpolations (Takahashi et al, 1993;Rödenbeck et al, 2014;Jones et al, 2015;Shutler et al, 2016), multiple linear regressions (Schuster et al, 2013;Iida et al, 2015;Becker et al, 2021), machine-learning-based regression methods (Landschützer et al, 2013;2014;2016;Nakaoka et al, 2013;Zeng et al, 2014;Laruelle et al, 2017;Ritter et al, 2017;Gregor et al, 2017;Chen et al, 2019;Denvil-Sommer et al, 2019), and biogeochemical-model-based approaches (Valsala and Maksyutov, 2010;Majkut et al, 2014;Verdy and Mazloff, 2017) have been common tactics, each one with its own strengths and weaknesses. Recently, ensemble averages of multiple data-or model-based approaches have become a popular option as well (Gregor et al, 2019;Lebehot et al, 2019;Fay et al, 2021).…”

“…The data products provided by Laruelle et al (2017) and Landschützer et al (2020b) -hereafter L17 and L20, respectively Most data-based estimates of oceanic CO2 uptake have considered only the open ocean (e.g., Rödenbeck et al, 2013;Landschützer et al, 2014;Iida et al, 2015;Denvil-Sommer et al, 2019;Gregor et al, 2019;Watson et al, 2020); however, coastal ocean CO2 uptake is estimated to be about 10% of the open-ocean figure (Laruelle et al, 2010;2014;Bourgeois et al, 2016;Roobaert et al, 2019;Chau et al, 2021) and may be changing at a different rate relative to open-ocean CO2 uptake (Laruelle et al, 2018). Therefore, augmenting global open-ocean pCO2(sw) data products to include the coastal ocean is quite valuable (Fay et al, 2021). Despite the greater spatial coverage and temporal resolution offered by these new gap-filled pCO2(sw) data products, significant challenges remain for accurately representing pCO2(sw).…”

“…seasonal patterns in pCO2(sw) revealed by RFR-CCS, and discuss the physical and biological processes that likely produce those patterns. Finally, we compare air-sea CO2 flux computed from RFR-CCS to that from a recently released CO2 flux product (Gregor and Fay, 2021), and discuss the implications of sporadic sampling on calculations of CO2 flux in the coastal ocean.…”