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The EuroSITES project was funded within the EU framework 7 to aid the convergence of the efforts of the 11 European-wide deep ocean observatories which formed its core. A major output of the project was envisaged as the synergies of the 13 partners working together to create a network providing sets of data in near-real-time, and in delayed-mode, which have been collected and processed in such a way as to be accessible to the wider scientific community, and comparable across time and site. The common data policy agreed by all the partners at the start of the project, and the evolution of a EuroSITES quality control manual which includes all the observatories practices, have formed the framework within which each data management group has delivered datasets which now carry the EuroSITES 'brand' as well as all the originators details. As a focal point for the, currently, 12 sites the EuroSITES data managers have worked as the European partner within the international OceanSITES project, developing the OceanSITES distribution standard, and studying the options for attaching additional metadata to data sets. The influence of EuroSITES as the European champion for the distribution of data in OceanSITES format will be an enduring legacy. The EuroSITES website (www.eurosites.info) provides a highly visible, and user accessible platform for the distribution of near-real time data from the observatories, as it becomes available. The data from the diverse sites are displayed in a consistent manner and the web pages form a coherent 'shop window' where potential data users can source data sets useful to their research, as well as a visual means of monitoring the health of the observatories. The sister site (outreach.eurosites.info) has been developed in parallel to be accessible to non-scientists. Working together EuroSITES has been able to develop a single conversion software package taking MEDATLAS and ODV ascii files and outputting the OceanSITES format. The sharing of knowledge in this and other developments ensures that costly duplication of effort is minimised. During the 3 years of the project the data management groups have interacted to share best practice, and although each is still independent and serves its own National Data Centre, the EuroSITES data inventory now carries data from all 13 sites, distributed in the OceanSITES v1.2 format. This format is NetCDF based, and carries within the file metadata. This describes the data in a consistent manner which achieves the first step towards interoperablility. The future of the funding of EuroSITES after April 2011 is not yet clear, but the project has achieved a remarkable degree of harmonisation already, and the relationships between the data management groups will undoubtedly continue as the cost benefits of resource and knowledge sharing become ever more important in the scientific arena.

Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° 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 SeaWiFS-MODIS 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 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, 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.

Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° 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 SeaWiFS-MODIS 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 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, 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.

Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° 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 SeaWiFS-MODIS 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 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, 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.