Estimation of the partial pressure of carbon dioxide (pCO2) 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 pCO2 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 non-linear regression (MPNR) for the estimation of global-scale distribution of pCO2 on the ocean surface is developed using continuous in-situ measurements of pCO2, 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 pCO2 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 pCO2 levels over different spatial and temporal scales. In order to account for regional differences in the influences of these processes on pCO2, 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 pCO2 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 pCO2 with dominant processes were established: for example, an inverse correlation of the pCO2 with Chla, SST and SSS in polar and sub-polar regions, a positive correlation of the pCO2 with SST and SSS and an inverse correlation of the pCO2 with Chla in tropical and subtropical regions, and an inverse correlation of the pCO2 with SST and a positive correlation of the pCO2 with Chla and SSS in equatorial regions. This indicates that the relationship of pCO2 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 pCO2 trends. Thus, changes in Chla, SST and SSS were systematically analyzed as they account for biological and physical effects on pCO2 and best-constrained based upon their strong relationships with pCO2 using the MPNR regression approach. The accuracy of the MPNR was assessed using independent in-situ data and satellite pCO2 data derived from global Level-3 Chla, SST, and SSS data. Validation results showed that satellite-derived pCO2 data agreed with direct in-situ pCO2 measurements with an RMSE 6.68-7.5 µatm and a relative error less than 5% which is significantly small as compared to the errors associated with earlier satellite pCO2 computations. The

Total alkalinity (TA) is a key parameter to understand the dynamics of biogeochemical properties in the global ocean and the effects of climate change on ocean acidification, ocean carbon cycle, and carbonate chemistry. To date, global surface ocean distributions of TA were investigated using multiple regional regression approaches which require smoothening techniques due to severe boundary effects in different oceanic regions/basins across latitudes/longitudes. To reduce the uncertainties and produce spatially and temporally consistent TA products, a novel single linear regression (SLR) approach was developed in this study to estimate TA fields in the global surface ocean waters. The SLR formulation was derived using the continuous in-situ measurements of sea surface salinity (SSS) collected from the different oceans. The performance of the SLR was assessed using independent in-situ/satellite derived TA data and the results from three existing algorithms. In general, the SLR-based global surface ocean TA fields from both in-situ and satellite data agreed well with in-situ measured TA data with a mean relative error less than 1%, which is much lower compared to the error with the existing algorithms. Studies were also conducted to examine the spatiotemporal variability and trends in the global surface ocean climatology of SSS and TA fields in the context of current climate change impacts.

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