Ocean biogeochemistry is increasingly seen as a major regulator of atmospheric CO2 concentrations. Biogeochemical cycles are driven by trophic interactions within pelagic food webs comprising an as yet unknown range of species. Categorising organisms into functional groups is a convenient way of rendering this biological diversity and hence complexity amenable to modelling. However, a mechanistic understanding of the factors regulating the wax and wane of the organisms comprising functional groups is a prerequisite to improving the predictive power of models. These factors range from physicochemical characteristics of the environment to interactions with other organisms (pathogens and grazers). Each species represents a set of adaptive traits that have been selected in the course of evolution by a given range of these factors. The relevant properties of these species can be determined by means of a variety of approaches that range from detailed, interdisciplinary field observations, in-situ perturbation experiments, mesocosm to beaker experiments, assessments of physiological and chemical properties and mapping of the genomic potential. Since relatively few genera and species appear to play key roles in trophic interactions and biogeochemical fluxes we will focus our efforts on understanding the autecology of selected species. This knowledge will help link biogeochemical cycles with ecological processes that shape the structure and functioning of pelagic systems.

Thus, we will integrate the physical, chemical, biological and geological disciplines of the ocean sciences and combine observations, experiments and models to identify changes in and links between physical, chemical and biological processes that regulate biogeochemical fluxes, develop new biological and geochemical proxies for improved interpretation of the palaeo records, explore sea-ice climate feedback mechanisms and their role in the ecosystem and provide input to improve models to hindcast and forecast climate change. The ultimate goal is to advance the predictive power of earth system models.