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SCENARIO: Complex network approach to improve marine ecosystem modelling and data assimilation - CONECDA

Department of Meteorology

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Prof Alberto Carrassi , Dr R Bannister , Dr Jozef Skakala , Dr S Ciavatta No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

One of the most important questions in marine ecology is ecosystem resilience in the face of human pressures on the natural environment, especially anthropogenic climate change (e.g. Ives and Carpenter, 2007). From UK policy-making perspective it is crucial to focus on marine ecosystems in the North-West European Shelf (NWES), a region of high importance for the carbon cycle and the European economy (e.g. Legge et al, 2020). Addressing questions about ecosystem sensitivity, or tipping points, using present ecosystem models is computationally costly, as one typically needs to run a large ensemble of simulations with varying model parameters and/or external forcing (e.g. Zhang and Pu, 2010). It is therefore highly desirable to bypass the computational cost of the full-complexity models by using creative ideas originating from diverse branches of modern mathematics, artificial intelligence and statistics, such as machine learning and emulators (see e.g.Hsieh, 2009; Tokmakian et al., 2012; Brajard et al. 2020), or explore contexts in which one can reduce model complexity (e.g. Skakala and Lazzari, 2020).

Complex networks (e.g. Boccaletti et al., 2006) are a highly efficient mathematical approach to represent connectivity between the degrees of freedom within a diverse range of complex phenomena: from trophic relationships in ecology to representing climate through a network of teleconnected local oscillators, all the way to the financial markets, social networks and the web, neuroscience, epidemic modelling, Ising model, transportation, security and further (for an overview see Costa et al., 2011). We propose here to use complex
networks to understand the pathways through which the antrophogenic signal propagates on the NWES, both between different ecosystem variables and between different geographical regions. This will enable us to identify the key variables and the NWES sub-regions having the largest impact on the NWES marine ecosystem dynamics. Such analysis will provide insight into the ecosystem vulnerability, or resilience, however it will also deliver crucial information on which model degrees of freedom are redundant, providing a guide on how to reduce the complexity of the NWES ecosystem model. On this basis we will construct a low-complexity model emulator, relying upon state-of-the-art machine learning tools. In particular the emulator will be designed to improve our current NWES data assimilation system, used to combine marine ecosystem observations with the model forecasts to produce the best possible estimate of the ecosystem state.

This is an interdisciplinary project at the interface of mathematics and environmental science, in which the fast evolving and timely mathematics of complex networks and machine learning methods are combined to deliver new profound insights into marine ecosystems in the shelf seas around the UK. The project can potentially lead to a step-change in the state-of-the-art modelling and data assimilation tools used to represent complex marine ecosystems on the NWES.

Training opportunities:

The student will be partly placed at Plymouth Marine Laboratory (PML), a centre of excellence for marine science and marine ecosystem modelling, where they will be trained in complex networks, NWES ecosystem processes and the NWES ecosystem numerical model. They will also receive training in programming, data processing and machine learning. PML is participating in a MARINT COST proposal, which, if granted, will offer opportunities to participate in machine learning/emulator building workshops, summer schools and training events. PML co-supervisors: Jozef Skakala and Stefano Ciavatta.

Through the National Centre for Earth Observation (NCEO) the student will have access to the large portfolio of the NCEO training courses and attend its annual conference and young researcher’s forum.

At the University of Reading the student will be exposed to a thriving academic environment and attend training on dynamical systems and data assimilation. There will be opportunities to attend international summer or winter schools on data assimilation and machine learning and virtual training organized by collaborators in Europe and USA. The student may attend conferences in geosciences well as machine learning.

Student profile:
Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in a quantitative discipline, ideally mathematics, physics, computer science, or engineering. Some understanding of oceanography is a bonus. The student should be confident with developing and editing computer codes (e.g. python, R, Fortran), and be able to work in a group and towards project deadlines.

To apply, please follow the instructions at

Funding Notes

This project is potentially funded by the Scenario NERC Doctoral Training Partnership, subject to a competition to identify the strongest applicants.

Co-funding from the National Centre for Earth Observation tbc.

Due to UKRI rules, the DTP can only fund a very limited number of international students. We will only consider applications from international students with an outstanding academic background placing them in the top 10% of their cohort.


Boccaletti S., et al., Complex networks: Structure and dynamics. Physics Reports, 424(4-5):175–308,
Brajard J., et al., Combining data assimilation and machine learning to emulate a dynamical model from
sparse and noisy observations: a case study with the Lorenz 96 model. Journal of Computational
Science, 44: 101171, 2020.
Butenschon, M., et al. "ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem
dynamics of the lower trophic levels." Geoscientific Model Development 9.4 (2016): 1293-1339.
Ciavatta, S., et al. "Decadal reanalysis of biogeochemical indicators and fluxes in the North West
European shelf‐sea ecosystem." Journal of Geophysical Research: Oceans 121.3 (2016): 1824-1845.
Costa L.d.F., et al., Analyzing and modeling real-world phenomena with complex networks: a survey of
applications. Advances in Physics, 60(3):329–412, 2011.
Hsieh W.W. Machine learning methods in the environmental sciences: Neural networks and kernels.
Cambridge university press, 2009.
Ives A.R. and Carpenter S.R.. Stability and diversity of ecosystems. Science, 317(5834):58–62, 2007.
Legge O., et al. Carbon on the northwest European shelf: Contemporary budget and future influences.
Frontiers in Marine Science, 7:143, 2020.
Skakala J. and Lazzari P., Keep it simple: A low-complexity model to study scale dependence of
phytoplankton dynamics in the tropical Pacific. submitted to Physical Review E, arXiv:2009.03629v1,
Tokmakian R., et al., On the use of emulators with extreme and highly nonlinear geophysical simulators.
Journal of Atmospheric and Oceanic Technology, 29(11):1704–1715, 2012.
Zhang H. and Pu Z., Beating the uncertainties: ensemble forecasting and ensemble-based data
assimilation in modern numerical weather prediction. Advances in Meteorology, 2010.
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