Combining Autonomous observations and Models for Predicting and Understanding Shelf seas
Completed projectProject start: April 2018 | Project end: March 2022
Principal Investigator: Dr Stefano Ciavatta
Other participants from PML: Dr James Fishwick, Dr Jorn Bruggeman, Dr Jozef Skakala, Dr Stefano Ciavatta, Dr Tim Smyth, Dr Yuri Artioli, Jerry Blackford
CAMPUS is a three-year project (2018–2021), funded by the Natural Environment Research Council, combining state-of-the-art computer modelling with innovative observational systems utilising the latest technologies.
Seven partner organisations from across the United Kingdom, led by Plymouth Marine Laboratory, are working together to fulfil two strategic outcomes:
- Deliver an improved evidence-base for ecosystem based marine management,
- Identify a cost-effective optimised observing network.
Shelf seas are of major societal importance as they provide a diverse range of goods (e.g. fisheries, renewable energy, transport) and services (e.g. carbon and nutrient cycling and biodiversity). A key governmental objective in the United Kingdom is managing seas to maintain clean, healthy, safe, productive and biologically diverse oceans and seas, as evidenced by the obligations to obtain Good Environmental Status (GES) under the UK Marine Strategy Framework, the Convention on Biological Diversity and ratification of the Oslo-Paris Convention (OSPAR). The delivery of these obligations requires comprehensive information about the state of our seas which in turn requires a combination of numerical models and observational programmes.
Computer modelling of marine ecosystems allows us to explore the recent past and predict future states of physical, chemical and biological properties of the sea, and how they vary in 3D space and time. The quality of these forecasts is improved by using data assimilation; the process of predicting the most accurate ocean state using observations to nudge model simulations, producing a combined observation and model product.
Ultimately CAMPUS will demonstrate an improvement in the understanding, uptake and use of integrated model observation products and observational strategies in support of decision-making. Beyond its core focus on bloom induced episodic events, associated biogeochemical cycling and the characterisation of variability in NW European Shelf Seas, the aim is to develop methodologies that are transferable and scalable to other applications (e.g. pollution, using marine litter as an example) and regions (e.g. applicable to Official Development Assistance countries).