Objectives

CAMPUS is working to deliver two strategic outcomes:

Deliver an improved evidence-base for ecosystem based marine management

This will require a step change in interaction between observing and model systems, including optimising the use of data for assimilation, and the evaluation and development of model systems. One of the main outcomes will be the improved prediction of episodic events, in particular the occurrence of phytoplankton blooms and oxygen depletion events, as well as inter-annual variability.

Identify a cost-effective optimised observing network

CAMPUS will recommend a comprehensive observational strategy, and evaluate it with a pilot study, resulting in greater efficiency and utility of UK observing systems. This will require significantly improved understanding of the key spatial and temporal scales of variability and their controls.
 

In order to deliver these strategic outcomes our research is structured around the following scientific hypotheses: 
 

1. Shelf- and seasonal-scale plankton growth and associated biogeochemical cycles (the mean-state) and its inter-annual variability are controlled by cumulative episodic events and fine-scale processes, in combination with background large scale forcing from ocean, atmosphere and rivers.
2. Fine scale and episodic plankton growth and associated biogeochemical cycles have distinct physical and biogeochemical signatures that provide predictable precursors to their evolution. This implies that adaptive autonomous (smart) monitoring strategies informed by ocean-forecasts can substantially enhance our ability to observe fine-scale, episodic events.
3. The addition of smart autonomy can deliver more efficient and effective monitoring and observations than the existing ad hoc and largely static or fixed route systems alone.

The specific objectives of CAMPUS are therefore:
 

1. To improve the predictive skill of key episodic events and hence ascertain the relative importance of episodic/fine scale processes and large scale forcing in setting the mean state of plankton production and associated biogeochemical cycles.
2. To provide model tools with enhanced process representation and assimilative capability for integration into existing operational oceanographic simulation and data delivery systems.
3. To develop and test smart autonomy which minimises the use of pilots and learns where to sample adaptively depending on the environment, thereby improving the observational representation of episodic events.
4. To develop understanding of the scales and variability of the shelf sea system, providing context for Good Environmental Status (GES) and advice on cost-effective observation network design for monitoring
5. To establish the utility, and facilitate the uptake, of models and autonomous observing systems in providing policy relevant evidence for marine assessment.