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Article

Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

1
Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
2
Instituto Español de Oceanografía (IEO), Centro oceanográfico da Coruña, Paseo Marítimo Alcalde Francisco Vázquez, nº10, 15001 A Coruña, Spain
3
Exeter, University, Penryn Campus, Geography department, Treliever Road, Penryn, Cornwall TR10 9FE, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 2038; https://doi.org/10.3390/rs12122038
Received: 31 May 2020 / Accepted: 22 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)
This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections. View Full-Text
Keywords: data assimilation; 1D ecosystem model; CO2; air–sea gas exchange data assimilation; 1D ecosystem model; CO2; air–sea gas exchange
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MDPI and ACS Style

Torres, R.; Artioli, Y.; Kitidis, V.; Ciavatta, S.; Ruiz-Villarreal, M.; Shutler, J.; Polimene, L.; Martinez, V.; Widdicombe, C.; Woodward, E.M.S.; Smyth, T.; Fishwick, J.; Tilstone, G.H. Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation. Remote Sens. 2020, 12, 2038. https://doi.org/10.3390/rs12122038

AMA Style

Torres R, Artioli Y, Kitidis V, Ciavatta S, Ruiz-Villarreal M, Shutler J, Polimene L, Martinez V, Widdicombe C, Woodward EMS, Smyth T, Fishwick J, Tilstone GH. Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation. Remote Sensing. 2020; 12(12):2038. https://doi.org/10.3390/rs12122038

Chicago/Turabian Style

Torres, Ricardo, Yuri Artioli, Vassilis Kitidis, Stefano Ciavatta, Manuel Ruiz-Villarreal, Jamie Shutler, Luca Polimene, Victor Martinez, Claire Widdicombe, E. Malcolm S. Woodward, Timothy Smyth, James Fishwick, and Gavin H. Tilstone. 2020. "Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation" Remote Sensing 12, no. 12: 2038. https://doi.org/10.3390/rs12122038

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