Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr ${}^{-1}$ over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive.

Syntheses of carbonate chemistry spatial patterns are important for predicting ocean acidification impacts, but are lacking in coastal oceans. Here, we show that along the North American Atlantic and Gulf coasts the meridional distributions of dissolved inorganic carbon (DIC) and carbonate mineral saturation state (Ω) are controlled by partial equilibrium with the atmosphere resulting in relatively low DIC and high Ω in warm southern waters and the opposite in cold northern waters. However, pH and the partial pressure of CO₂ (pCO₂) do not exhibit a simple spatial pattern and are controlled by local physical and net biological processes which impede equilibrium with the atmosphere. Along the Pacific coast, upwelling brings subsurface waters with low Ω and pH to the surface where net biological production works to raise their values. Different temperature sensitivities of carbonate properties and different timescales of influencing processes lead to contrasting property distributions within and among margins.

Planktonic communities are shaped through a balance of local evolutionary adaptation and ecological succession driven in large part by migration. The timescales over which these processes operate are still largely unresolved. Here we use Lagrangian particle tracking and network theory to quantify the timescale over which surface currents connect different regions of the global ocean. We find that the fastest path between two patches—each randomly located anywhere in the surface ocean—is, on average, less than a decade. These results suggest that marine planktonic communities may keep pace with climate change—increasing temperatures, ocean acidification and changes in stratification over decadal timescales—through the advection of resilient types.

Three different trajectory schemes for oceanic and atmospheric general circulation models are compared in two different experiments. The theories of the trajectory schemes are presented showing the differential equations they solve and why they are mass conserving. One scheme assumes that the velocity fields are stationary for set intervals of time between saved model outputs and solves the trajectory path from a differential equation only as a function of space, i.e. stepwise stationary. The second scheme is a special case of the stepwise-stationary scheme, where velocities are assumed constant between general circulation model (GCM) outputs; it uses hence a fixed GCM time step. The third scheme uses a continuous linear interpolation of the fields in time and solves the trajectory path from a differential equation as a function of both space and time, i.e. a time-dependent scheme. The trajectory schemes are tested offline, i.e. using the already integrated and stored velocity fields from a GCM. The first comparison of the schemes uses trajectories calculated using the velocity fields from a high-resolution ocean general circulation model in the Agulhas region. The second comparison uses trajectories calculated using the wind fields from an atmospheric reanalysis. The study shows that using the time-dependent scheme over the stepwise-stationary scheme greatly improves accuracy with only a small increase in computational time. It is also found that with decreasing time steps the stepwise-stationary scheme becomes increasingly more accurate but at increased computational cost. The time-dependent scheme is therefore preferred over the stepwise-stationary scheme. However, when averaging over large ensembles of trajectories, the two schemes are comparable, as intrinsic variability dominates over numerical errors. The fixed GCM time step scheme is found to be less accurate than the stepwise-stationary scheme, even when considering averages over large ensembles.

The sea‐air biological O₂ flux assessed from measurements of surface O₂ supersaturation in excess of Ar supersaturation (“O₂ bioflux”) is increasingly being used to constrain net community production (NCP) in the upper ocean mixed layer. In making these calculations, one generally assumes that NCP is at steady state, mixed layer depth is constant, and there is no O₂ exchange across the base of the mixed layer. The object of this paper is to evaluate the magnitude of errors introduced by violations of these assumptions. Therefore, we examine the differences between the sea‐air biological O₂ flux and NCP in the Southern Ocean mixed layer as calculated using two ocean biogeochemistry general circulation models. In this approach, NCP is considered a known entity in the prognostic model, whereas O₂ bioflux is estimated using the model‐predicted O₂/Ar ratio to compute the mixed layer biological O₂ saturation and the gas transfer velocity to calculate flux. We find that the simulated biological O₂ flux gives an accurate picture of the regional‐scale patterns and trends in model NCP. However, on local scales, violations of the assumptions behind the O₂/Ar method lead to significant, non‐uniform differences between model NCP and biological O₂ flux. These errors arise from two main sources. First, venting of biological O₂ to the atmosphere can be misaligned from NCP in both time and space. Second, vertical fluxes of oxygen across the base of the mixed layer complicate the relationship between NCP and the biological O₂ flux. Our calculations show that low values of O₂ bioflux correctly register that NCP is also low (<10 mmol m⁻² day⁻¹), but fractional errors are large when rates are this low. Values between 10 and 40 mmol m⁻² day⁻¹ in areas with intermediate mixed layer depths of 30 to 50 m have the smallest absolute and relative errors. Areas with O₂ bioflux higher than 30 mmol m⁻² day⁻¹ and mixed layers deeper than 40 m tend to underestimate NCP by up to 20 mmol m⁻² day⁻¹. Excluding time periods when mixed layer biological O₂ is undersaturated, O₂ bioflux underestimates time‐averaged NCP by 5%–15%. If these time periods are included, O₂ bioflux underestimates mixed layer NCP by 20%–35% in the Southern Ocean. The higher error estimate is relevant if one wants to estimate seasonal NCP since a significant amount of biological production takes place when mixed layer biological O₂ is undersaturated.