Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 14, issue 19
Biogeosciences, 14, 4545-4561, 2017
https://doi.org/10.5194/bg-14-4545-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 14, 4545-4561, 2017
https://doi.org/10.5194/bg-14-4545-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Oct 2017

Research article | 13 Oct 2017

Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation

Goulven G. Laruelle1, Peter Landschützer2, Nicolas Gruber3, Jean-Louis Tison1, Bruno Delille4, and Pierre Regnier1 Goulven G. Laruelle et al.
  • 1Department Geoscience, Environment & Society (DGES), Université Libre de Bruxelles, Bruxelles, Belgium
  • 2Max Planck Institute for Meteorology, Bundesstr. 53, Hamburg, Germany
  • 3Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland
  • 4Unité d'Oceanographie Chimique, Astrophysics, Geophysics and Oceanography department, University of Liège, Liège, Belgium

Abstract. In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO2), the air–sea CO2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in both time and space and of surface pCO2 exhibiting much higher temporal and spatial variability in these regions compared to the open ocean. Here, we use a modified version of a two-step artificial neural network method (SOM-FFN; Landschützer et al., 2013) to interpolate the pCO2 data along the continental margins with a spatial resolution of 0.25° and with monthly resolution from 1998 to 2015. The most important modifications compared to the original SOM-FFN method are (i) the much higher spatial resolution and (ii) the inclusion of sea ice and wind speed as predictors of pCO2. The SOM-FFN is first trained with pCO2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO2 field with independent data extracted from the LDVEO2015 database. The new coastal pCO2 product confirms a previously suggested general meridional trend of the annual mean pCO2 in all the continental shelves with high values in the tropics and dropping to values beneath those of the atmosphere at higher latitudes. The monthly resolution of our data product permits us to reveal significant differences in the seasonality of pCO2 across the ocean basins. The shelves of the western and northern Pacific, as well as the shelves in the temperate northern Atlantic, display particularly pronounced seasonal variations in pCO2,  while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO2 for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO2 cannot solely be explained by temperature-induced changes in solubility but are also the result of seasonal changes in circulation, mixing and biological productivity. Our results also reveal that the amplitudes of both thermal and nonthermal seasonal variations in pCO2 are significantly larger at high latitudes. Finally, because this product's spatial extent includes parts of the open ocean as well, it can be readily merged with existing global open-ocean products to produce a true global perspective of the spatial and temporal variability of surface ocean pCO2.

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