scholarly journals Global high-resolution monthly <i>p</i>CO<sub>2</sub> climatology for the coastal ocean derived from neural network interpolation

2017 ◽  
Vol 14 (19) ◽  
pp. 4545-4561 ◽  
Author(s):  
Goulven G. Laruelle ◽  
Peter Landschützer ◽  
Nicolas Gruber ◽  
Jean-Louis Tison ◽  
Bruno Delille ◽  
...  

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.

2017 ◽  
Author(s):  
Goulven G. Laruelle ◽  
Peter Landschützer ◽  
Nicolas Gruber ◽  
Jean-Louis Tison ◽  
Bruno Delille ◽  
...  

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 both in 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 degrees and with monthly resolution from 1998 until 2014. 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 as a predictor of pCO2. The validity of our interpolation, both in space and time, is assessed by comparing the SOM-FFN outputs with pCO2 measurements extracted from the SOCATv3.0 and LDVEO2014 datasets. 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. But significant differences in the seasonality across the ocean basins exist. The shelves of the western and northern Pacific, as well as the shelves in the temperate North Atlantic display particularly pronounced seasonal variations in pCO2, while the shelves in the southeastern Atlantic and in the South Pacific reveal a much smaller seasonality. Overall, 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. Finally, thanks to this product having been extended to cover open ocean areas 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.


2014 ◽  
Vol 31 (8) ◽  
pp. 1838-1849 ◽  
Author(s):  
J. Zeng ◽  
Y. Nojiri ◽  
P. Landschützer ◽  
M. Telszewski ◽  
S. Nakaoka

Abstract A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO2 (fCO2) on a 1° × 1° spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO2 increase rate of 1.50 μatm yr−1. The rate was used to normalize multiple years’ fCO2 observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO2 climatology. The model simulates monthly fCO2 distributions that agree well with observations and yields an anthropogenic CO2 update of −1.9 to −2.3 PgC yr−1. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont–Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO2 uptake. The method’s benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO2 as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO2 in all seasons, and (iii) the model can be extended to produce global fCO2 maps at a higher resolution in time and space as long as the required data for input variables are available.


2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.


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