Over the past decade marine sciences experience a surge in the volume of data from in-situ collection, satellite missions and earth system model simulations. Scientists are increasingly faced with the emerging challenge of analysing and synthesising collected data, which are often too big and complex to detect temporal (diurnal to decadal) and spatial (local to global) variability or secular trends using traditional statistical approaches. Greater than ever proportion of the community has turned to advanced statistical and machine learning methods to integrate, analyse and synthetize these multi-platform data. The utility of these methods has been demonstrated in many areas of Earth Science, and is now also becoming apparent in ocean physics, air-sea interactions, ocean biogeochemical cycles, and marine ecology. This has resulted in the proliferation of data products that straddle oceanography and data science.
We invite scientists in this exciting intersection to submit abstracts for the session titled: "Novel data-analysis techniques for big data applications in marine science" (OS034). The session is open for submissions in the field of ocean physics, chemistry and/or biology but also those connecting the spheres, such as air-sea and air-land interactions.
Rebecca Latto (NASA Goddard Institute for Space Studies, New York, NY, USA)
Maciej Telszewski (International Ocean Carbon Coordination Project, Sopot, Poland)
Peter Landschuetzer (Max Planck Institute for Meteorology, Hamburg, Germany)
Luke Gregor (Council for Scientific and Industrial Research, Cape Town, South Africa)
Anastasia Romanou (NASA Goddard Institute for Space Studies, New York, NY, USA)
Below please also find a subjective selection of other sessions potentially of interest to the marine biogeochemistry community.
B088: Towards a Global Assessment of Regional Carbon Budgets
B077: The Global Methane Cycle
B034: Global and Regional Nitrous Oxide Budget: Data, Models, and Uncertainty
B036: Identifying and Reducing Biogeochemical and Physical Uncertainty in Ocean Models
GC075: Quantifying Nutrient Budgets for sustainable nutrient management
For more information see: https://fallmeeting.agu.org/2018/abstract-submissions/