Estimation of Partial Pressure of Carbon Dioxide and Air-Sea Fluxes in Hooghly Estuary Based on In Situ and Satellite Observations

Remote estimation of surface pCO2 on the West Florida Shelf

“…Stephens et al (1995) North Pacific SST, LON MPR RMSE = +/-17 µatm (subtropical), RMS = +/-40µatm (subpolar) Coast Chierici et al (2009) Northern North Atlantic SST, CHL, MLD MPR RMSE = 10.8 µatm, R 2 = 0.72 Telszewski et al (2009) North Atlantic SST, CHL, MLD SOM RMSE = 11.6 µatm Friedrich and Oschlies (2009) North Atlantic SST, CHL KFM RMSE = 19 µatm Shadwick et al In terms of model inputs, most published works correlated surface pCO2 to physical and biological parameters such as sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD, m), and chlorophyll a concentration (CHL, mg m -3 ) (e.g., Stephens et al, 1995;Rangama et al, 2005;Wanninkhof et al, 2007;Watanabe et al, 2007;Berryman et al, 2008;Zhu et al, 2009;Friedrich and Oschlies, 2009;Hales et al, 2012;Tao et al, 2012;Signorini et al, 2013;Qin et al, 2014;Bai et al, 2015, Marrec et al, 2015Padhy et al, 2015;Moussa et al, 2016). These parameters all have the potential to affect surface pCO2, because: 1) SST and SSS can influence the solubility of CO2 and the dissociation constants of the carbonate system (Weiss, 1974;Lee et al, 1998;Millero et al, 2006); 2) CHL can be a good tracer of the influence of biological processes on surface pCO2 as CHL increases (e.g., in algal blooms) can cause significant decreases in surface pCO2 (Sarma et al, 2006;Jamet et al, 2007;Friedrich and Oschlies, 2009); and 3), MLD can be a good indicator of wind stress and convective mixing, and as a result, carbonate properties of subsurface waters brought to surface by strong mixing are usually different from those of the surface (Jamet et al, 2007;Chierici et al, 2009;Signorini et al, 2013).…”

“…Several other studies correlated surface pCO2 with latitude and longitude (Olsen et al, 2004;Jo et al, 2012;Marrec et al, 2014). (Ono et al, 2004;Sarma et al, 2006;Jamet et al, 2007;Telszewski et al, 2009;Nakaoka et al, 2013;Marrec et al, 2015;Padhy et al, 2015;Moussa et al, 2016). Such parameterizations have provided pCO2 with Root Mean Square Errors (RMSE) less than 17 µatm.…”

Spatiotemporal Visualization of Time-Series Satellite-Derived Co2 Flux Data Using Volume Rendering and Gpu-Based Interpolation On A Cloud-Driven Digital Earth

“…Visualization techniques are regarded as a powerful manner to analyze and present climate changes and environmental simulations Ziolkowska and Reyes, 2016). In previous studies, the spatial distributions of ocean carbon were usually demonstrated by thematic maps while the temporal variations were displayed by diagrams (Benallal, 2017;Padhy, 2016;Parard, 2016;Schuster, 2013).…”