The coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO2) and releasing nitrous oxide (N2O) and methane (CH4). Major advances have improved our understanding of the coastal air-sea exchanges of these three gasses since the first phase of the Regional Carbon Cycle Assessment and Processes (RECCAP in 2013), but a comprehensive view that integrates the three gasses at the global scale is still lacking. In this second phase (RECCAP2), we quantify global coastal ocean fluxes of CO2, N2O and CH4 using an ensemble of global gap-filled observation-based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO2 in both observational products and models, but the magnitude of the median net global coastal uptake is ~60% larger in models (-0.72 vs. -0.44 PgC/yr, 1998-2018, coastal ocean area of 77 million km2). We attribute most of this model-product difference to the seasonality in sea surface CO2 partial pressure at mid- and high-latitudes, where models simulate stronger winter CO2 uptake. The global coastal ocean is a major source of N2O (+0.70 PgCO2-e /yr in observational product and +0.54 PgCO2-e /yr in model median) and of CH4 (+0.21 PgCO2-e /yr in observational product), which offsets a substantial proportion of the net radiative effect of coastal \co uptake (35-58% in CO2-equivalents). Data products and models need improvement to better resolve the spatio-temporal variability and long term trends in CO2, N2O and CH4 in the global coastal ocean.

Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions’ ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.

We use a statistical emulation technique to construct synthetic ensembles of global and regional sea-air carbon dioxide (CO2) flux from four observation-based products over 1985-2014. Much like ensembles of Earth system models that are constructed by perturbing their initial conditions, our synthetic ensemble members exhibit different phasing of internal variability and a common externally forced signal. Our synthetic ensembles illustrate an important role for internal variability in the temporal evolution of global and regional CO2 flux and produce a wide range of possible trends over 1990-1999 and 2000-2009. We assume a specific externally forced signal and calculate the likelihood of the observed trend given the distribution of synthetic trends during these two periods. Over the decade 1990-1999, three of the four observation-based products exhibit small negative trends in globally integrated sea-air CO2 flux (i.e., enhanced ocean CO2 absorption with time) that are highly probable (44-72% chance of occurrence) in their respective synthetic trend distributions. Over the decade 2000-2009, however, three of the four products show large negative trends in globally integrated sea-air CO2 flux that are somewhat improbable (17-19% chance of occurrence). Our synthetic ensembles suggest that the largest observation-based positive trends in global and Southern Ocean CO2 flux over 1990-1999 and the largest negative trends over 2000-2009 are somewhat improbable (<30% chance of occurrence). Our approach provides a new understanding of the role of internal and external processes in driving sea-air CO2 flux variability.

The oceans are a major carbon sink. Sea surface temperature (SST) is a crucial variable in the calculation of the air-sea carbon dioxide (CO2;) flux from surface observations. Any bias in the SST or any upper ocean vertical temperature gradient (e.g., the cool skin effect) potentially generates a bias in the CO2 flux estimates. A recent study suggested a substantial increase (~50% or ~0.9 Pg C yr-1) in the global ocean CO2 uptake due to this temperature effect. Here, we use a gold standard buoy SST dataset as the reference to assess the accuracy of in-situ SST used for flux calculation. A physical model is then used to estimate the cool skin effect, which varies with latitude. The bias-corrected SST (assessed by buoy SST) coupled with the physics-based cool skin correction increases the average ocean CO2 uptake by ~35% (0.6 Pg C yr-1) for 1982 to 2020, which is significantly smaller than the previous correction. After these temperature considerations, we estimate an average net ocean CO2 uptake of 2.2 +- 0.4 Pg C yr-1 for 1994 to 2007 based on an ensemble of surface observation-based flux estimates, in line with the independent interior ocean carbon storage estimate corrected for the river induced natural outgassing flux (2.1 +- 0.4 Pg C yr-1).

The science guiding the \EURECA campaign and its measurements are presented. \EURECA comprised roughly five weeks of measurements in the downstream winter trades of the North Atlantic — eastward and south-eastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, \EURECA marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or, or the life-cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso (200 km) and larger (500 km) scales, roughly four hundred hours of flight time by four heavily instrumented research aircraft, four global-ocean class research vessels, an advanced ground-based cloud observatory, a flotilla of autonomous or tethered measurement devices operating in the upper ocean (nearly 10000 profiles), lower atmosphere (continuous profiling), and along the air-sea interface, a network of water stable isotopologue measurements, complemented by special programmes of satellite remote sensing and modeling with a new generation of weather/climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that \EURECA explored — from Brazil Ring Current Eddies to turbulence induced clustering of cloud droplets and its influence on warm-rain formation — are presented along with an overview \EURECA’s outreach activities, environmental impact, and guidelines for scientific practice.