Basin-scale pCO₂ distribution using satellite sea surface temperature, Chl a and climatological salinity in the North Pacific in spring and summer

Abstract

An empirical method is presented for the estimation of basin-scale distribution of partial pressure of carbon dioxide (pCO₂) in the North Pacific using satellite-derived Sea Surface Temperature (SST), chlorophyll-a concentrations (chl a) and climatological Sea Surface Salinity (SSS). In this approach, multiple regression equations were developed to compute mixed layer Dissolved Inorganic Carbon (DIC) based on SST, SSS and Chl a, whereas mixed layer Total Alkalinity (TA) was linearly regressed with SSS. The DIC-SST relation exhibited three different slopes at SST < 20°, 20° < SST < 27.5° and SST> 27.5°C. Therefore data have been grouped with reference to SST. Regression equations were developed for two seasons (spring and summer). The regression errors for DIC and TA were 10.5 and 5 μmol kg⁻¹, respectively. The pCO₂) was computed from the estimated DIC and TA using dissociation constants given by Mehrbach et al. (1973), refit by Dickson and Millero (1987). The derived pCO₂) agreed with the shipboard pCO₂) observations within an error of 17–23 μatm. The sensitivity test on the regression equations for DIC estimation indicated that SSS is the most influencing parameter, followed by SST and Chl a. Using the monthly average SST and Chl a fields derived from the Advanced Very High Resolution Radiometer (AVHRR) and SeaWiFS (Sea-viewing Wide Field of view Sensor), respectively, and climatological SSS, monthly basin-scale pCO₂) fields were computed. The statistical model derived pCO₂) results are in agreement with underway pCO₂) in the North Pacific. This study strongly suggests that satellite-based techniques are promising tools for estimation of pCO₂) fields on a basin scale but the associated error bars are larger than required to study anthropogenic carbon uptake by the oceans. Incorporation of more in situ shipboard data may help in refining the estimating equations and reducing the errors further.