Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems can seriously compromise our ability to predict coarse-scale behaviour. Spatial variability which is deterministically unpredictable may distort parameter estimates when the ecosystem model is fitted to (or assimilates) ocean data, may compromise model validation, and may produce mean-field ecosystem behaviour discrepant with that predicted by the model. New statistical methods are investigated to mitigate these effects and thus improve understanding and prediction of coarse-scale behaviour e.g. in response to climate change. First, the standard model fitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags, as has been observed to approximate mesoscale plankton variability in the open ocean. The resulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificial ecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’ box model to suitably-averaged data from Georges Bank (as an example), allowing liberal biological parameter adjustments to account for mean effects of unresolved variability. A novel skill assessment technique is used to show that the extrapolative skill of the box model fails to improve on a strictly empirical model. Third, plankton models where horizontal variability is resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicit resolution. A simple simulation study suggests that the mean effects of fine-scale variability on coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ and similar statistical modelling techniques may be profitably applied to account for them.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:561454 |
| Date | January 2008 |
| Creators | Wallhead, Philip John |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/63762/ |
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