Estimating Surface pCOsub2/sub in the Northern Gulf of Mexico: Which Remote Sensing Model to Use?

Shuangling Chen, Chuanmin Hu, Wei-Jun Cai, Bo Yang

Research output: Contribution to journalArticlepeer-review

Abstract

<p> <p id="x-x-sp0080"> Various approaches and models have been proposed to remotely estimate surface <em> p </em> CO <sub> 2 </sub> in the ocean, with variable performance as they were designed for different environments. Among these, a recently developed mechanistic semi-analytical approach (MeSAA) has shown its advantage for its explicit inclusion of physical and biological forcing in the model, yet its general applicability is unknown. Here, with extensive <a title="Learn more about In Situ Measurement from ScienceDirect's AI-generated Topic Pages"> in situ measurements </a> of surface <em> p </em> CO <sub> 2 </sub> , the MeSAA, originally developed for the summertime East China Sea, was tested in the northern <a title="Learn more about Gulf of Mexico from ScienceDirect's AI-generated Topic Pages"> Gulf of Mexico </a> (GOM) where <a title="Learn more about River Plume from ScienceDirect's AI-generated Topic Pages"> river plumes </a> dominate water's biogeochemical properties during summer. Specifically, the MeSAA-predicted surface <em> p </em> CO <sub> 2 </sub> was estimated by combining the dominating effects of thermodynamics, river-ocean mixing and biological activities on surface <em> p </em> CO <sub> 2 </sub> . Firstly, effects of thermodynamics and river-ocean mixing ( <em> p </em> CO <sub> 2@Hmixing </sub> ) were estimated with a two-endmember mixing model, assuming conservative mixing. Secondly, <em> p </em> CO <sub> 2 </sub> variations caused by biological activities (&Delta; <em> p </em> CO <sub> 2@bio </sub> ) was determined through an empirical relationship between <a title="Learn more about Sea Surface Temperature from ScienceDirect's AI-generated Topic Pages"> sea surface temperature </a> (SST)-normalized <em> p </em> CO <sub> 2 </sub> and <a title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages"> MODIS </a> (Moderate Resolution Imaging Spectroradiometer) 8-day composite chlorophyll concentration (CHL). The MeSAA-modeled <em> p </em> CO <sub> 2 </sub> (sum of <em> p </em> CO <sub> 2@Hmixing </sub> and &Delta; <em> p </em> CO <sub> 2@bio </sub> ) was compared with the field-measured <em> p </em> CO <sub> 2 </sub> . The <a title="Learn more about Root-Mean-Square Error from ScienceDirect's AI-generated Topic Pages"> Root Mean Square Error </a> (RMSE) was 22.94 &micro;atm (5.91%), with coefficient of determination (R <sup> 2 </sup> ) of 0.25, mean bias (MB) of &minus; 0.23 &micro;atm and mean ratio (MR) of 1.001, for <em> p </em> CO <sub> 2 </sub> ranging between 316 and 452 &micro;atm. To improve the model performance, a locally tuned MeSAA was developed through the use of a locally tuned &Delta; <em> p </em> CO <sub> 2@bio </sub> term. A multi-variate empirical regression model was also developed using the same dataset. Both the locally tuned MeSAA and the regression models showed improved performance comparing to the original MeSAA, with R <sup> 2 </sup> of 0.78 and 0.84, RMSE of 12.36 &micro;atm (3.14%) and 10.66 &micro;atm (2.68%), MB of 0.00 &micro;atm and &minus; 0.10 &micro;atm, MR of 1.001 and 1.000, respectively. A sensitivity analysis was conducted to study the uncertainties in the predicted <em> p </em> CO <sub> 2 </sub> as a result of the uncertainties in the input variables of each model. Although the MeSAA was more sensitive to variations in <a title="Learn more about Sea Surface Temperature from ScienceDirect's AI-generated Topic Pages"> SST </a> and CHL than in <a title="Learn more about Sea Surface Salinity from ScienceDirect's AI-generated Topic Pages"> sea surface salinity </a> (SSS), and the locally tuned MeSAA and the empirical regression models were more sensitive to changes in SST and SSS than in CHL, generally for these three models the bias induced by the uncertainties in the empirically derived parameters (river endmember total alkalinity (TA) and <a title="Learn more about Dissolved Inorganic Carbon from ScienceDirect's AI-generated Topic Pages"> dissolved inorganic carbon </a> (DIC), biological coefficient of the MeSAA and locally tuned MeSAA models) and environmental variables (SST, SSS, CHL) was within or close to the uncertainty of each model. While all these three models showed that surface <em> p </em> CO <sub> 2 </sub> was positively correlated to SST, the MeSAA showed negative correlation between surface <em> p </em> CO <sub> 2 </sub> and SSS and CHL but the locally tuned MeSAA and the empirical regression showed the opposite. These results suggest that the locally tuned MeSAA worked better in the river-dominated northern GOM than the original MeSAA, with slightly worse statistics but more meaningful physical and biogeochemical interpretations than the empirical regression model. Because data from abnormal <a title="Learn more about Upwelling Water from ScienceDirect's AI-generated Topic Pages"> upwelling </a> were not used to train the models, they are not applicable for waters with strong upwelling, yet the empirical regression approach showed ability to be further tuned to adapt to such cases. </p></p>
Original languageAmerican English
JournalContinental Shelf Research
Volume151
DOIs
StatePublished - Jan 1 2017

Keywords

  • Surface pCO2
  • TA
  • DIC
  • Northern GOM
  • MODIS
  • Remote sensing

Disciplines

  • Life Sciences

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