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SATELLITE ESTIMATION OF COASTAL PCO2 AND AIR-SEA FLUX OF CARBON DIOXIDE IN THE NORTHERN GULF OF MEXICO
  • +2
  • Steven Lohrenz,
  • Wei-Jun Cai,
  • Sumit Chakraborty,
  • Ruoying He,
  • Hanqin Tian
Steven Lohrenz
University of Massachusetts Dartmouth

Corresponding Author:[email protected]

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Wei-Jun Cai
University of Delaware
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Sumit Chakraborty
Mote Marine Laboratory
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Ruoying He
North Carolina State Univ.
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Hanqin Tian
Auburn University
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Abstract

A key to better constraining estimates of the ocean sink for fossil fuel emissions of carbon dioxide is reducing uncertainties in coastal carbon fluxes. A contributing factor in uncertainties in coastal carbon fluxes stems from the under sampling of seasonality and spatial heterogeneity. Our objectives were to i) assess satellite-based approaches that would expand the spatial and temporal coverage of the surface ocean pCO2 and sea-air CO2 flux for the northern Gulf of Mexico, and ii) investigate the seasonal and interannual variations in CO2 dynamics and possible environmental drivers. Regression tree analysis was effective in directly relating surface ocean pCO2 to satellite-retrieved (MODIS Aqua) products including chlorophyll, sea surface temperature, and dissolved/detrital absorption. Satellite-based assessments of sea surface pCO2 were made spanning the period from 2006-2010 and were used in conjunction with estimates of wind fields and atmospheric pCO2 to produce regional-scale estimates of air-sea fluxes. Seasonality was evident in air-sea fluxes of CO2, with an estimated annual average CO2 flux for the study region of -4.3 + 1.1 Tg C y-1, confirming prior findings that the Gulf of Mexico was a net CO2 sink. Interannual variability in fluxes was related to Mississippi River dissolved inorganic nitrogen inputs, an indication that human- and climate-related changes in river exports will impact coastal carbon budgets. This is the first multi-year assessment of pCO2 and air-sea flux of CO2 using satellite-derived environmental data for the northern Gulf of Mexico.
Mar 2018Published in Remote Sensing of Environment volume 207 on pages 71-83. 10.1016/j.rse.2017.12.039