The Spatiotemporal Dynamics of the Sources and Sinks of CO₂ in the Global Coastal Ocean

Abstract

In contrast to the open ocean, the sources and sinks for atmospheric carbon dioxide (CO2) in the coastal seas are poorly constrained and understood. Here we address this knowledge gap by analyzing the spatial and temporal variability of the coastal air‐sea flux of CO2 (FCO2) using a recent high‐resolution (0.25°) monthly climatology for coastal sea surface partial pressure in CO2 (pCO2). Coastal regions are characterized by CO2 sinks at temperate and high latitudes and by CO2 sources at low latitude and in the tropics, with annual mean CO2 flux densities comparable in magnitude and pattern to those of the adjacent open ocean with the exception of river‐dominated systems. The seasonal variations in FCO2 are large, often exceeding 2 mol C m−2 year−1, a magnitude similar to the variations exhibited across latitudes. The majority of these seasonal variations stems from the air‐sea pCO2 difference, although changes in wind speed and sea ice cover can also be significant regionally. Globally integrated, the coastal seas act currently as a CO2 sink of −0.20 ± 0.02 Pg C year−1, with a more intense uptake occurring in summer because of the disproportionate influence of high‐latitude shelves in the Northern Hemisphere. Combined with estimates of the carbon sinks in the open ocean and the Arctic, this gives for the global ocean, averaged over the 1998 to 2015 period an annual net CO2 uptake of −1.7 ± 0.3 Pg C year−1.

Consistency and Challenges in the Ocean Carbon Sink Estimate for the Global Carbon Budget

“…Thus, ideally, these gaps would be closed by data collection or data sharing for these regions, as well as mapping. This correction is on the order of 0.1-0.15 PgC yr −1 and is considered conservative as it is smaller than the estimate for the Arctic Ocean of 0.12 ± 0.06 PgC yr −1 (Schuster et al, 2013) and the global coastal ocean carbon sink of 0.2 PgC yr −1 (Roobaert et al, 2019), which, however, overlaps partly with the area covered by the global data-products (37% of the area in the coastal product is already represented in the global product of Landschützer et al, 2016) and by the Arctic Ocean. This simple approach uses the maximally covered area of the data-products, i.e., regions which are mapped in some months of the year are not filled (e.g., parts of the Southern Ocean which are mapped in summer but not in winter).…”

“…While this approach might tend to overestimate the flux in the permanently ice-covered parts of the Arctic Ocean, the region north of 80 • N covers only 1% of the global ocean area. The area correction is dominated by the coastal ocean, which has a similar flux density as the open ocean (0.39 mol C m −2 yr −1 coastal south of 60 • N vs. 0.35 mol C mr −2 yr −1 globally Wanninkhof et al, 2013;Roobaert et al, 2019). The simplistic area-scaling approach to fill data gaps is hence considered conservative, also in comparison to the 60% higher area correction from a time-resolved gap-filling using ocean models (McKinley et al, 2020).…”

Seasonal variability of net sea-air CO2 fluxes in a coastal region of the northern Antarctic Peninsula

“…However, the relatively low monthly and interannual coverage in most of these studies may have biased the integrated FCO 2 budget throughout the year. This is particularly true if we take into account recent estimates of FCO 2 from long-term climatology for global coastal regions 4 . In this climatology, the NAP, as well as the Weddell Sea and much of the Atlantic and Indian sectors of the Southern Ocean, was a net CO 2 source between 1998 and 2015.…”

Strengths and weaknesses of three Machine Learning methods for pCO<sub>2</sub> interpolation

“…The very limited sampling for these regions is consistent with the state space for pCO 2 being inadequately represented in the data available for algorithm training. For similar reasons, algorithms built specifically for the coastal regions also appear to be needed (Roobaert et al, 2019;Laruelle et al, 2017). Specialized approaches could focus on localized patterns or incorporating additional information into the training process to achieve a better representation of these areas.…”