Abstract. The oceanic sink of carbon dioxide (CO₂) is an important part of the global carbon budget. Understanding uncertainties in the calculation of this net flux into the ocean is crucial for climate research. One of the sources of the uncertainty within this calculation is the parameterization chosen for the CO₂ gas-transfer velocity. We used a recently developed software toolbox, called the FluxEngine (Shutler et al., 2016), to estimate the monthly air–sea CO₂ fluxes for the extratropical North Atlantic Ocean, including the European Arctic, and for the global ocean using several published quadratic and cubic wind speed parameterizations of the gas-transfer velocity. The aim of the study is to constrain the uncertainty caused by the choice of parameterization in the North Atlantic Ocean. This region is a large oceanic sink of CO₂, and it is also a region characterized by strong winds, especially in winter but with good in situ data coverage. We show that the uncertainty in the parameterization is smaller in the North Atlantic Ocean and the Arctic than in the global ocean. It is as little as 5 % in the North Atlantic and 4 % in the European Arctic, in comparison to 9 % for the global ocean when restricted to parameterizations with quadratic wind dependence. This uncertainty becomes 46, 44, and 65 %, respectively, when all parameterizations are considered. We suggest that this smaller uncertainty (5 and 4 %) is caused by a combination of higher than global average wind speeds in the North Atlantic (> 7 ms⁻¹) and lack of any seasonal changes in the direction of the flux direction within most of the region. We also compare the impact of using two different in situ pCO₂ data sets (Takahashi et al. (2009) and Surface Ocean CO₂ Atlas (SOCAT) v1.5 and v2.0, for the flux calculation. The annual fluxes using the two data sets differ by 8 % in the North Atlantic and 19 % in the European Arctic. The seasonal fluxes in the Arctic computed from the two data sets disagree with each other possibly due to insufficient spatial and temporal data coverage, especially in winter.