Modelling the Marine Environment

We are internationally renowned for our expertise in modelling the marine environment and we host one of the largest and most experienced marine ecosystem modelling groups in the world. Our models enable us to gain a greater understanding of the dynamics and potential change of marine processes and systems, and we continually refine and build new models to address the emerging challenges facing the global ocean and society that depends upon it.

There are increasing calls from policy makers and ocean users to make projections of how the ocean may change and in turn affect the resources the ocean provides. Combining ecological, physical and chemical understanding into dynamic models of the ocean provides us with tools with which we can assess the vulnerabilities and opportunities of marine systems and promote good management.

We work at a range of scales encapsulating global earth-system models, regional seas and local systems such as estuaries and bays. Our research covers issues from climate change and ocean acidification, including mitigation to offshore energy, aquaculture, fisheries and good environmental status.

We collaborate with colleagues internally, nationally and across the globe applying models to a wide range of pressing questions, developing new methods to safeguard the sustainability of the ocean and increasing our understanding of how it works.

Making a difference

Our models are used to understand the implications and risks of global change and human activity and produce decision support tools that can be used by policy makers and regulators to inform choices over the optimum use of natural resources and the marine environment.

Further information

For further information contact Jerry Blackford (jcb@pml.ac.uk) or see our dedicated sub-site about the Marine System Modelling Group at PML.

Projects

Shelf Seas Biogeochemistry research programme: modelling
Completed

Shelf Seas Biogeochemistry research programme: modelling

Contact: Professor Icarus Allen

The shelf seas are of major importance to society, providing a diverse range of goods, such as fisheries, renewable energy, transport and services...

NOWMAPS 2
Completed

NOWMAPS 2

Contact: Jerry Blackford

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical state...

ERSEM (European Regional Seas Ecosystem Model)

ERSEM (European Regional Seas Ecosystem Model)

Contact: Professor Icarus Allen

An ecosystem model is an abstract, usually mathematical, representation of an ecological system, which is studied to gain understanding of the real...

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You may be interested in...

News

New modelling tool to enhance global understanding

Open access model allows scientists to predict climate and other anthropogenically influenced environmental changes.

News

Making modelling count

A new study with PML authors supported through the Marine Ecosystems Research Programme was published in Marine Policy this week, highlighting the value of increasing the contribution of shelf-seas community and ecosystem models to policy development and management. 

News

Progress in UK Environmental Prediction research

​A recent workshop has highlighted the way forward for environmental predictions.

Selected key publications

Blackford, J; Artioli, Y; Clark, J; de Mora, L. 2017. Monitoring of offshore geological carbon storage integrity: implications of natural variability in the marine system and the assessment of anomaly detection criteria. International Journal of Greenhouse Gas Control. 64, 99-112. doi: 10.1016/j.iggc.2017.06.020

Butenschon, M; Clark, JR; Aldridge, JN; Allen, JI; Artioli, Y; Blackford, JC; Bruggeman, J; Cazenave, P; Ciavatta, S; Kay, S; Lessin, G; van Leeuwen, S; Van der Molen, J; de Mora, L; Polimene, L; Sailley, SF; Stephens, N; Torres, R. 2016. ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels. Geoscientific Model Development, 9 (4). 1293-1339. doi: 10.5194/gmd-9-1293-2016

Cazenave, PW; Torres, R;, Allen, JI. 2016. Unstructured grid modelling of offshore wind farm impacts on seasonally stratified shelf seas. Progress in Oceanography, 145: 25-41 doi: 10.1016/j.pocean.2016.04.004.

Ciavatta, S; Kay, S; Saux-Picart, S; Butenschon, M; Allen, JI. 2016. Decadal reanalysis of biogeochemical indicator and fluxes in the North West European shelf-sea ecosystem, Journal of Geophysical Research - Oceans. doi: 10.1002/2015JC011496

Clark, JR; Cole, M; Lindeque, PK; Fileman, E; Blackford, JC; Lewis, C; Lenton, TM; Galloway, TS. 2016. Marine microplastic debris: a targeted plan for understanding and quantifying interactions with marine life Frontiers in Ecology and the Environment, 14, 317-324. doi: 10.1002/fee.1297

de Mora, L; Butenschön, M; Allen, JI. 2016. The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM, Geoscientific Model Development 9, 59-76. doi:10.5194/gmd-9-59-2016.

Kay, S; Butenschon, M. 2016. Projections of change in key ecosystem indicators for planning and management of marine protected areas: An example study for European seas. Estuarine, Coastal and Shelf Science. doi: 10.1016/j.ecss.2016.03.003 (In Press)

Lessin, G; Artioli. Y; Queiros, AM; Widdicombe, S; Blackford, JC. 2016. Modelling impacts and recovery in benthic communities exposed to localised high CO2. Marine Pollution Bulletin, 109(1), 267-280. 10.1016/j.marpolbul.2016.05.071

Skákala, J; Cazenave, PW; Smyth, TJ; Torres, R. 2016. Using multifractals to evaluate oceanographic model skill. Journal of Geophysical Research: Oceans, 121, 5487-5500. DOI 10.1002/2016JC011741

Related recent publications

  1. Sankar, S; Polimene, L; Marin, L; Menon, NN; Samuelsen, A; Pastres, R; Ciavatta, S. 2018 Sensitivity of the simulated Oxygen Minimum Zone to biogeochemical processes at an oligotrophic site in the Arabian Sea. Ecological Modelling, 372. 12-23. 10.1016/j.ecolmodel.2018.01.016
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  2. Beecham, J; Bruggeman, J; Aldridge, J; Mackinson, S. 2016 Couplerlib: a metadata-driven library for the integration of multiple models of higher and lower trophic level marine systems with inexact functional group matching. Geoscientific Model Development, 9 (3). 947-964. 10.5194/gmd-9-947-2016
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  3. Aldridge, JN; Lessin, G; Amoudry, LO; Hicks, N; Hull, T; Klar, JK; Kitidis, V ; McNeill, CL; Ingels, J; Parker, ER; Silburn, B; Silva, T; Sivyer, DB; Smith, HEK; Widdicombe, S; Woodward, EMS ; Van der Molen, J; Garcia, L; Kröger, S. 2017 Comparing benthic biogeochemistry at a sandy and a muddy site in the Celtic Sea using a model and observations [in special issue: Biogeochemistry, macronutrient and carbon cycling in the shelf sea benthos] Biogeochemistry, 135 (1-2). 155-182. 10.1007/s10533-017-0367-0
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  4. Brewin, RJW; Ciavatta, S; Sathyendranath, S; Jackson, T; Tilstone, GH; Curran, K; Airs, RL; Cummings, DG; Brotas, V; Organelli, E; Dall’Olmo, G ; Raitsos, DE . 2017 Uncertainty in ocean-colour estimates of chlorophyll for phytoplankton groups. Frontiers in Marine Science, 4 (104). 10.3389/fmars.2017.00104
    View publication

  5. Cazenave, P; Torres, R; Allen, JI. 2016 Unstructured grid modelling of offshore wind farm impacts on seasonally stratified shelf seas. Progress in Oceanography. /10.1016/j.pocean.2016.04.004
    View publication

View more publications