Time series analysis of marine data: a key knowledge at the crossroads of marine sciences

In our time emerged the idea of a major environmental degradation, at both local and global scales, in the face of the recurrent human pollution. Consequently in the sake of a sustainable humanity development, of the ethic and of the ecology, the protection and the monitoring of our environment have become a major stake. Many scientific and technical tools contributed to improve the environmental knowledge, as helped remote and in situ observations, and forecast modeling. In situ environmental sensor systems have been designed to be increasingly sustainable even in a hostile environment such as the deep ocean. In a similar way, the infrastructures hosting those sensors are now thought and built to be permanent. In marine sciences these considerations gave birth to the “Observatory” concept: a long-term infrastructure dedicated to both bottom and water column in situ observations. In open-ocean those observatories are managed in the infrastructure European projects EMSO and FIXO3 (www.esonet-emso.org, http://www.fixo3.eu/). This community gathers about 55 partners from about 15 countries in Europe and is working in close association with other international observatories communities: Neptune Canada; OOI in USA, DONET in Japan, etc. All scientists of these communities are getting a similar product: longer and longer time series data. They all agree on the need to acquire deep sea time series data as reviewed by (Ruhl et al., 2011): “…such observatories will contribute to answering major ocean science questions including: How can monitoring of factors such as seismic activity, pore fluid chemistry and pressure, and gas hydrate stability improve seismic, slope failure, and tsunami warning? What aspects of physical oceanography, biogeochemical cycling, and ecosystems will be most sensitive to climatic and anthropogenic change? What are natural versus anthropogenic changes? Most fundamentally, how are marine processes that occur at differing scales related?”. Similarly in coastal oceanography, time series are also acquired and the involved scientists are got together in the JERICO (www.jerico-fp7.eu) consortium to harmonize the coastal infrastructures from the sensors to the data diffusion. From the coast to the open-ocean all are facing a common challenge: the analysis of this increasing data flow. In order to facilitate the data processing from the archiving to the distribution, Neptune Canada developed a great data management system which offers at the end of the network a data portal and tool: Oceans 2.0. Nevertheless the challenge to analyse this increasing data flow in an optimal way is not yet tackled. Consequently the idea raised to help the community from coastal to open sea areas, at international level, with the organization of a conference involving the here-above cited scientific communities (not only) in order to share the experiences in time series analysis, to outline the gaps and needs for the future. This international event, titled “Time series analysis in marine sciences and applications for industry" (Logonna-Daoulas, Bretagne, 17–21 September 2012) included a 2-day training session followed by a 3-day conference. It gathered more than 100 attendees from 54 institutions amongst 24 countries with purpose i) to integrate the scientific community and research activities at the crossroads of marine sciences (physical oceanography, marine chemistry, marine biology, ecology, geology and ocean engineering); ii) to share the rapidly developing knowledge; iii) to enhance cross discipline interactions and collaborations. This conference produces several outcomes amongst which this special issue dedicated to time series analysis in marine sciences and gathering 11 papers. Considering that time-series analysis is the future for marine science to understand ocean processes and their dynamics in most of the marine research fields the conference was organized according to marine research fields. Meanwhile, as the initial objective was to exchange knowledge on time series analysis methods, this special edition is organized according to two topical analysis methods. The first section presents articles focused on the study of the variability and information contained in the observed signal (1), and the second section introduces articles dealing with the study of extreme events (2)