Model Tools

To provide the best possible estimates of the state of marine ecosystems, we apply and develop data assimilation systems that integrate model simulations with observations such as satellite imagery. Model validation tools and a repertoire of advanced statistical methods are also used to further ensure the robustness of our models' application.
 

Model validation

Before a model can be used to inform policy decisions or for science, we need to know how good it is.

Does it reproduce patterns seen in nature?
Can we trust it?
Will it be able to make reliable predictions?
Is it a valid model of the system that we are investigating?

Model validation is the process that answers these questions by demonstrating how closely the model reproduces the natural behaviour of the ocean. Model validation is important because it tells us about not just the quality of the model, but also the limitations of the model. This allows us to judge how useful the model can be to address scientific, policy and management questions. Validation is also an important part of model development because it lets us know if changes to the model improve, degrade or have no effect on the model.

Further information

Please contact: Lee de Mora

Related projects

Integrated Global Biogeochemical Modelling Network to support UK Earth System Research (iMarNet)
UK Earth System model (UKESM)

Related publications

  • de Mora L, Butenschön M and 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, Geosci. Model Dev., 9, 59-76, doi:10.5194/gmd-9-59-2016
  • de Mora L, Butenschön M and Allen JI. 2013. How should sparse marine in situ measurements be compared to a continuous model: an example, Geosci. Model Dev., 6, 533-548, doi:10.5194/gmd-6-533-2013

In a recent work, PML scientists L. de Mora, M. Butenschon and J.I. Allen used emergent properties to demonstrate the validity of the marine ecosystem model, ERSEM. This figure, taken from that paper, shows that the ERSEM model was able to reproduce the relative abundance of three major phytoplankton classes seen in nature.

 
 

Data Assimilation

Data assimilation is the process of combining observations from a wide variety of sources with the forecast output from a numerical model. The resulting analysis is considered to be the 'best' estimate of the state of the system at a particular instant in time. PML, as part of the National Centre for Earth Observation (NCEO), develops and apply systems for assimilating biogeochemical ocean observations into ERSEM (European Regional Seas Ecosystem Model) and into the UK Earth System Model.

PML has demonstrated the effectiveness of this approach by the improvement in chlorophyll estimates after assimilation of satellite products in the NW European shelf. We will maintain and enhance this capacity by adopting the state of the art methods and expanding to larger spatial domains.

Further information

Please contact: Stefano Ciavatta

Related projects

EC Copernicus-CMEMS NowMaps Project
EC Copernicus CMEMS TOSCA project
ESA’s OceanFlux Greenhouse Gases project
National Centre for Earth Observation
Operational Ecology Project (OPEC)
Tools for Assessment and Planning of Aquaculture Sustainability 
(TAPAS)

Related publications

  • Ciavatta S, Kay S, Saux-Picart S, Butenschon M and Allen JI. 2016. Decadal reanalysis of biogeochemical indicator and fluxes in the North West European shelf-sea ecosystem, Journal of Geophysical Research - Oceans, submitted.
  • Ciavatta S, Torres R, Martinez-Vicente V, Smyth T, Dall’Olmo G, Polimene L and Allen JI. 2014. Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling. Progress in Oceanography, 127, 74-95.
  • Butenschön M and Zavatarelli M. 2012. A comparison of different versions of the SEEK Filter for assimilation of biogeochemical data in numerical models of marine ecosystem dynamics. Ocean Modelling, 54, 37-54.
  • Ciavatta S, Torres R, Saux Picart S and Allen JI. 2011. Can ocean color assimilation improve biogeochemical hindcasts in shelf seas? Journal of Geophysical Research, 116, C12043, 19 PP. doi:10.1029/2011JC007219.
  • Ciavatta S and Pastres R. 2011. Exploring the long-term and interannual variability of biogeochemical variables in coastal areas by means of a data assimilation approach, Estuarine, Coastal and Shelf Science, 91 (3),  411–422.
  • Torres R, Allen JI and Figueiras FG. 2006. Sequential data assimilation in an upwelling influenced estuary, Journal of Marinr Systems, 60(3-4), 317-329.
  • Allen JI, Eknes M and Evensen G. 2003. An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea, Annales Geophysicae, 21(1), 399-411.
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Reanalysis of the biogeochemistry of the North East Atlantic in the years 1998-2009: average value of simulated chlorophyll (top) after assimilation of data of chlorophyll from ocean colour (bottom) (Ciavatta et al., 2016).