A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO(2) Using Satellite Oceanographic Data
|Author(s)||Krishna Kande Vamsi1, Shanmugam Palanisamy1, Nagamani Pullaiahgari Venkata2|
|Affiliation(s)||1 : IIT Madras, Dept Ocean Engn, Ocean Opt & Imaging Lab, Chennai 600036, Tamil Nadu, India.
2 : ISRO, Natl Remote Sensing Ctr NRSC, Ocean Satellite Grp, Hyderabad 500037, India.
|Source||Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing (1939-1404) (Ieee-inst Electrical Electronics Engineers Inc), 2020 , Vol. 13 , P. 6220-6235|
|WOS© Times Cited||5|
|Keyword(s)||Carbon dioxide, chlorophyll-a, multiparametric algorithm, partial pressure of carbon dioxide, satellite oceanography, sea surface salinity, sea surface temperature|
Estimation of the partial pressure of carbon dioxide (pCO(2)) and its space-time variability in global surface ocean waters is essential for understanding the carbon cycle and predicting the future atmospheric CO2 concentration. Until recently, only basin-scale distribution of pCO(2) has been reported by using satellite-derived climatological data due to the lack of models for global-scale applications. In the present work, a multiparametric nonlinear regression (MPNR) for the estimation of global-scale distribution of pCO(2) on the ocean surface is developed using continuous in-situ measurements of pCO(2), chlorophyll-a (Chla) concentration, sea surface temperature (SST), and sea surface salinity (SSS) obtained on a number of cruise programs in various regional oceanic waters. Analysis of these measurement data showed strong relationships of pCO(2) with Chla, SST, and SSS, because these three parameters are governed by the complex interactions of oceanographic (physical, biological, and chemical) and meteorological processes and thus influence pCO(2) levels over different spatial and temporal scales. In order to account for regional differences in the influences of these processes on pCO(2), model parameterizations are derived as a function of Chla, SST, and SSS data with different boundary conditions. Because the strength of each influencing parameters on pCO(2) differed at different Chla, SST, and SSS ranges, measurement data were grouped with reference to the Chla, SST, and SSS ranges and significant correlations of the pCO(2) with dominant processes were established: for example, an inverse correlation of the pCO(2) with Chla, SST, and SSS in polar and subpolar regions, a positive correlation of the pCO(2) with SST and SSS and an inverse correlation of the pCO(2) with Chla in tropical and subtropical regions, and an inverse correlation of the pCO(2) with SST and a positive correlation of the pCO(2) with Chla and SSS in equatorial regions. This indicates that the relationship of pCO(2) versus biological and physical parameters is more complex and an individual parameter alone would not serve as an accurate estimator of basin- and global-scale pCO(2) trends. Thus, changes in Chla, SST, and SSS were systematically analyzed as they account for biological and physical effects on pCO(2) and best constrained based upon their strong relationships with pCO(2) using theMPNR regression approach. The accuracy of theMPNR was assessed using independent in-situ data and satellite pCO(2) data derived from global Level-3 Chla, SST, and SSS data. Validation results showed that satellite-derived pCO(2) data agreed with direct in-situ pCO(2) measurements with an RMSE 6.68-7.5 mu atm and a relative error less than 5%, which is significantly small as compared to the errors associated with earlier satellite pCO(2) computations. The distribution and magnitude of spatial and temporal (monthly and seasonal) amplitude of satellite-derived pCO(2) in climatic zones and ocean basins were further examined and agreed well with the shipboard pCO(2) observations and climatological surface ocean pCO(2) data.