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. This study applies a neural network technique to produce mapsof oceanic surface pCO2 in Prydz Bay in the SouthernOcean on a weekly 0.1 ∘ longitude  ×  0.1 ∘ latitudegrid based on in situ measurements obtained during the 31st CHINARE cruisefrom February to early March 2015. This study area was divided into threeregions, namely, the “open-ocean” region, “sea-ice” region and “shelf” region. Thedistribution of oceanic pCO2 was mainly affected byphysical processes in the open-ocean region, where mixing and upwelling werethe main controls. In the sea-ice region, oceanic pCO2 changed sharply due to the strong change in seasonal ice. In the shelfregion, biological factors were the main control. The weekly oceanic pCO2 was estimated using a self-organizing map (SOM) withfour proxy parameters (sea surface temperature, chlorophyll  a concentration, mixed Layer Depth and sea surface salinity) to overcome thecomplex relationship between the biogeochemical and physical conditions inthe Prydz Bay region. The reconstructed oceanic pCO2 datacoincide well with the in situ pCO2 data fromSOCAT, with a root mean square error of 22.14  µ atm. Prydz Baywas mainly a strong CO2 sink in February 2015, with a monthlyaveraged uptake of 23.57±6.36  TgC. The oceanic CO2 sink ispronounced in the shelf region due to its low oceanic pCO2 values and peak biological production.

biogeosciences

Variations in the summer oceanic &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and carbon sink in Prydz Bay using the self-organizing map analysis approach