Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network

[EN] Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a latitude by longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFSMODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6?atm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

Decadal changes in the structure of Cymodocea nodosa seagrass meadows: natural vs. human influences

[EN] Seagrass meadows are deteriorating worldwide. However, numerous declines are still unreported, which avoid accurate evaluations of seagrass global trends. This is particularly relevant for the western African coast and nearby oceanic archipelagos in the eastern Atlantic. The seagrass Cymodocea nodosa is an ecological engineer on shallow soft bottoms of the Canary Islands. A comparative decadal study was conducted in 21 C. nodosa seagrass meadows at Gran Canaria Island to compare the structure (shoot density, leaf length and cover) between 2003 and 2012. Overall, 11 meadows exhibited a severe regression, while 10 remained relatively stable. During this period, natural influences (sea surface temperature, Chlorophyll-a concentration and PAR light, as well as the number of storm episodes detaching seagrasses) had a low predictive power on temporal patterns in seagrass structure. In contrast, proximity from a range of human-mediated influences (e.g. the number of outfalls and ports) seem to be related to the loss of seagrass; the rate of seagrass erosion between 2003 and 2012 was significantly predicted by the number of human-mediated impacts around each meadow. This result highlights promoting management actions to conserve meadows of C. nodosa at the study region through efficient management of local impacts