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).

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth’s radiation budget. However, a knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to understand the factors controlling surface ocean DMS and its impact on climate. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 57,810 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of lat-lon coordinates, time-of-day, time-of-year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, silicate, and oxygen. Linear regressions of DMS against the environmental parameters show that on a global scale mixed layer depth and solar radiation are the strongest predictors of DMS, however, they capture 14% and 12% of the raw DMS data variance, respectively. The multi-linear regression can capture more (29%) of the raw data variance, but strongly underestimates high DMS concentrations. In contrast, the ANN captures ∼61% of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in DMS concentration and sea-to-air flux. The highest concentrations (fluxes) occur in the high-latitude oceans during the summer. We estimate a lower global sea- to-air DMS flux (17.90±0.34 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used.