A mechanistic semi-analytical method for remotely sensing sea surface
pCO2 in river-dominated coastal oceans: A case study from the East China
Sea
Yan Bai1,5, Wei-Jun Cai2, Xianqiang He1, Weidong Zhai3,4, Delu Pan1,5, Minhan Dai3,
Peisong Yu1
1
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of
Oceanography, State Oceanic Administration, Hangzhou, China,
2
School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA,
3
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen,
China, 4National Marine Environmental Monitoring Centre, Dalian, China,
5
Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou,
China
While satellite remote sensing has become a very useful tool contributing to assessments
of sea surface partial pressure of carbon dioxide (pCO2) that subsequently allow
quantification of air-sea CO2 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
pCO2 in river-dominated coastal oceans using satellite data. Observed pCO2 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 pCO2 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 pCO2 is consistent with underway data, even though no in situ pCO2
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.