Estimating summer sea surface pCO2 on a river-dominated continental shelf using a satellite-based semi-mechanistic model

Abstract

A satellite-based semi-mechanistic model was developed to estimate the sea surface partial pressure of CO2 (pCO2) on the river-dominated Louisiana Continental Shelf (LCS) in summer months using satellite-derived products including chlorophyll-a concentration (Chl-a_sat), sea surface salinity (SSS_sat), and sea surface temperature (SST_sat). The model analytically expresses pCO2 as the sum of physical (pCO2,mix) and biological (ΔpCO2,bio) contributions which are the primary controlling factors associated with the pCO2 variability on the LCS. The SSS_sat, derived from a locally calibrated retrieval algorithm, was used in a two-end member river-ocean mixing model to estimate the concentrations of dissolved inorganic carbon (DIC) and total alkalinity (TA) resulting from physical mixing. The mixing-induced DIC and TA were used together with SST_sat to calculate the pCO2,mix resulting from the mixing processes at the in situ temperature. The biological uptake of pCO2 through net community production (ΔpCO2,bio) was derived from a previously validated local Chl-a_sat product along with the difference between the pCO2,mix and the extensive in situ underway pCO2 data. The satellite-derived pCO2 agreed with in situ measurements with a median absolute difference of 37 μatm, mean difference of −5 μatm, and a root mean square difference of 58 μatm for five summer cruises spanning 2006–2009. The semi-mechanistic remote sensing model differentiates physical and biological influences on the pCO2 variability, which will help to better investigate the variability and the underlying controlling mechanisms of pCO2 and CO2 flux on the LCS. This study demonstrates that the use of locally calibrated satellite products (Chl-a_sat, SSS_sat) should be promoted to improve the satellite estimation of pCO2 in coastal waters. This method can potentially be applied in other river-dominated continental shelves with local tuning.