The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO<sub>2</sub> fugacity in support of air–sea gas flux studies

Abstract

Abstract. Climatologies, or long-term averages, of essential climate variables are useful for evaluating models and providing a baseline for studying anomalies. The Surface Ocean CO2 Atlas (SOCAT) has made millions of global underway sea surface measurements of CO2 publicly available, all in a uniform format and presented as fugacity, fCO2. As fCO2 is highly sensitive to temperature, the measurements are only valid for the instantaneous sea surface temperature (SST) that is measured concurrently with the in-water CO2 measurement. To create a climatology of fCO2 data suitable for calculating air–sea CO2 fluxes, it is therefore desirable to calculate fCO2 valid for a more consistent and averaged SST. This paper presents the OceanFlux Greenhouse Gases methodology for creating such a climatology. We recomputed SOCAT's fCO2 values for their respective measurement month and year using monthly composite SST data on a 1° × 1° grid from satellite Earth observation and then extrapolated the resulting fCO2 values to reference year 2010. The data were then spatially interpolated onto a 1° × 1° grid of the global oceans to produce 12 monthly fCO2 distributions for 2010, including the prediction errors of fCO2 produced by the spatial interpolation technique. The partial pressure of CO2 (pCO2) is also provided for those who prefer to use pCO2. The CO2 concentration difference between ocean and atmosphere is the thermodynamic driving force of the air–sea CO2 flux, and hence the presented fCO2 distributions can be used in air–sea gas flux calculations together with climatologies of other climate variables.

Key Uncertainties in the Recent Air‐Sea Flux of CO2

“…As described in section 5.4 of Goddijn‐Murphy et al (), the influence of the fortuitous timing and track of specific measurement cruises can be evaluated by a bootstrap experiment. We constructed an ensemble of ten synthetic data sets by selecting randomly with replacement from actual cruises (using the cruise ID within SOCAT).…”

“…The general pattern of fluxes is quite similar to many other studies (see, e.g., Roobaert et al, ) but with some local differences and a subtle general shift toward more predominantly downward fluxes. That shift can be traced to the treatment of temperature in the processing method of Goddijn‐Murphy et al (). Calculations at each grid cell require the reprocessed SOCAT values described above and other data.…”

Effect of gas-transfer velocity parameterization choice on air–sea CO<sub>2</sub> fluxes in the North Atlantic Ocean and the European Arctic

“…All the above results were obtained with the Takahashi et al (2009) pCO 2 climatology and for comparison, we have also calculated the air-sea CO 2 fluxes using the re-analysed SOCAT v1.5 and v2.0 data (which were converted to climatologies using methodology described in Goddijn-Murphy et al, 2015). Figure 8 shows the results using the N2000 k parameterization for all three of the data sets (Takahashi et al, 2009 and both SOCAT versions).…”

“…SOCAT is a community-driven data set containing 6.3 and 10.1 million surface water CO 2 fugacity values for v1.5 and v2.0, respectively, with a global coverage. The SOCAT databases have been re-analysed and then converted to climatologies using the methodology described in Goddijn-Murphy et al (2015). All the climatologies were calculated for year 2010 with the FluxEngine toolset.…”

Technical note: Evaluation of three machine learning models for surface ocean CO<sub>2</sub> mapping

“…To calculate the oceanic CO 2 uptake, various models have been used to interpolate scarce CO 2 measurements in the surface ocean spatially and temporarily to obtain basin-wide (e.g., Zeng et al, 2002;Lefèvre et al, 2005;Chierici et al, 2006;Sarma et al, 2006;Jamet et al, 2007;Friedrich and Oschlies, 2009;Telszewski et al, 2009;Takamura et al, 2010;Landschützer et al, 2013;Nakaoka et al, 2013;Iida et al, 2015;Goddijn-Murphy et al, 2015) and global ocean CO 2 maps (Takahashi et al, 2002(Takahashi et al, , 2009Park et al, 2010;Rö-denbeck et al, 2013;Sasse et al, 2013;Jones et al, 2015;Zeng et al, 2015). The Surface Ocean CO 2 Mapping (SO-COM) inter-comparison initiative revealed varying degrees of differences among 14 models , of which 5 used neural networks.…”