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In spite of the recent strong increase in the number of\udmeasurements of the partial pressure of CO2 in the surface ocean\ud(pCO2), the air–sea CO2 balance of the continental shelf seas\udremains poorly quantified. This is a consequence of these regions remaining\udstrongly under-sampled in both time and space and of surface pCO2\udexhibiting much higher temporal and spatial variability in these regions\udcompared to the open ocean. Here, we use a modified version of a two-step\udartificial neural network method (SOM-FFN; Landschützer et al., 2013) to\udinterpolate the pCO2 data along the continental margins with a spatial\udresolution of 0.25° and with monthly resolution from 1998 to 2015. The\udmost important modifications compared to the original SOM-FFN method are\ud(i) the much higher spatial resolution and (ii) the inclusion of sea ice and\udwind speed as predictors of pCO2. The SOM-FFN is first trained with\udpCO2 measurements extracted from the SOCATv4 database. Then, the\udvalidity of our interpolation, in both space and time, is assessed by\udcomparing the generated pCO2 field with independent data extracted\udfrom the LDVEO2015 database. The new coastal pCO2 product confirms a\udpreviously suggested general meridional trend of the annual mean pCO2\udin all the continental shelves with high values in the tropics and dropping\udto values beneath those of the atmosphere at higher latitudes. The monthly\udresolution of our data product permits us to reveal significant differences\udin the seasonality of pCO2 across the ocean basins. The shelves of the\udwestern and northern Pacific, as well as the shelves in the temperate\udnorthern\udAtlantic, display particularly pronounced seasonal variations in pCO2, \udwhile the shelves in the southeastern Atlantic and in the southern Pacific\udreveal a much smaller seasonality. The calculation of temperature normalized\udpCO2 for several latitudes in different oceanic basins confirms that\udthe seasonality in shelf pCO2 cannot solely be explained by\udtemperature-induced changes in solubility but are also the result of\udseasonal changes in circulation, mixing and biological productivity. Our\udresults also reveal that the amplitudes of both thermal and nonthermal\udseasonal variations in pCO2 are significantly larger at high\udlatitudes. Finally, because this product's spatial extent includes parts of\udthe 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\udtemporal variability of surface ocean pCO2

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Global high-resolution monthly &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; climatology for the coastal ocean derived from neural network interpolation