A mechanistic semi‐analytical method for remotely sensing sea surface pCO₂ in river‐dominated coastal oceans: A case study from the East China Sea

Abstract

While satellite remote sensing has become a very useful tool contributing to assessments of sea surface partial pressure of carbon dioxide (pCO 2 ) that subsequently allow quantification of air-sea CO 2 flux, the application of empirical approaches in coastal oceans has proven challenging owing to the interaction of multiple controlling factors. We propose a ''mechanistic semi-analytic algorithm'' (MeSAA) to estimate sea surface pCO 2 in river-dominated coastal oceans using satellite data. Observed pCO 2 can be analytically expressed as the sum of individual components controlled by major factors such as thermodynamics (or temperature), mixing, and biology. With marine carbonate system calculations, temperature and mixing effects can be predicted using thermodynamic principles and by assuming conservative two endmember mixing of total dissolved inorganic carbon and total alkalinity (e.g., the Changjiang River and Kuroshio water in the East China Sea, ECS). Next, an integral expression for pCO 2 drawdown due to biological effects can be parameterized using the chlorophyll a concentration (chla). We demonstrate the validity and applicability of the algorithm in the ECS during summertime. Sensitivity analysis shows that errors in empirical coefficients and three input satellite parameters (salinity, SST, chla) have limited influence on the algorithm, and satellite-derived pCO 2 is consistent with underway data, even though no in situ pCO 2 data from the ECS shelves was used to train the algorithm. Our algorithm has more physical and biogeochemical mechanistic meaning than empirical methods, and should be applicable to other similar systems.

Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)

“…Note that the slope of 0.0423 was derived for the global ocean, and this can be confirmed with carbonate system calculations, although the slope varied slightly with TA, DIC, salinity and temperature [33]. Thus, we can analytically calculate the pCO2 at a certain temperature (T) by the exponential relationship as in Equation (9) [21,59,65].…”

“…Based on the principle of the MeSAA method proposed by Bai et al [33], sea surface pCO2 variation (△pCO2) can be expressed as the sum of individual pCO2 contributions associated with each process or controlling factor ( ∂ pCO2(factor-n)): air sea 2 therm 2 mix 2 bio 2 t h e r m m i x b i o therm mix bio 2 air sea 2 factor n factor n air sea factor n pCO pCO pCO pCO …”

“…Based on the principle of the MeSAA method proposed by Bai et al [33], sea surface pCO 2 variation (∆pCO 2 ) can be expressed as the sum of individual pCO 2 contributions associated with each process or controlling factor (BpCO 2(factor-n) ): …”

“…As DIC and TA are measured in total concentration, they are mass-conservative during mixing processes; and DIC is directly linked by stoichiometry to nutrient removal/release and O 2 production or consumption during biological processes. Thus, many studies ( [33], and references therein), especially marine carbonate models, calculate pCO 2 from DIC and TA variations by estimating contributions to DIC by specific processes of mixing, biological effect and air-sea CO 2 exchange. Because our desired final product is satellite-derived DIC and TA, we chose to estimate pCO 2 directly to avoid error propagation from estimations of satellite-derived DIC and TA, even though our MeSAA approach was built upon the principle of DIC and TA changes and we also used carbonate system calculations as part of our approach.…”

Evaluation of CMIP5 models over the northern North Atlantic in the context of forthcoming paleoclimatic reconstructions

“…Many studies show that SSTs in the highlatitude Arctic Ocean are largely governed by sea-ice and continental runoff, rather than by evaporation and precipitation controlling low-latitude tropical oceanic variability. In addition, global satellite analyses and models incorporating remotely observed SSTs may be inaccurate due to lack of direct measurements for calibrating satellite data (Bai et al 2015). On the other hand, due to lack of consistency in time, space and the number of SST measurements north of 60°N, the small variance shown by COBE2 on the east coast of Greenland and on the north coast of Iceland must be interpreted with care.…”