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When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites

When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites
When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites
The degree of structural complexity that should be incorporated in marine biogeochemical models is unclear. We know that the marine ecosystem is complex, and that its observed behaviour is attributable to the interaction of a large number of separate processes, but observations are scarce and often insufficient to constrain more than a small number of model parameters. This issue is addressed using a novel algorithm that systematically removes model processes that are not constrained by observations. The algorithm is applied to a one-dimensional, eight component ecosystem-biogeochemistry model at two North Atlantic time-series sites. Between 11 and 14 of the 30 model parameters can be removed at each site with no significant reduction in the model’s ability to fit upper ocean (0–200 m) biogeochemical tracer and productivity data. The statistically optimal model structures and parameters provide estimates of the most likely state variables and fluxes at each site. Differences in these estimates between the two sites indicate that the optimal models are specialised to both the physical environment and the assimilated observations. At each site the heavily reduced models may thus be suitable for diagnostic purposes but may not be sufficiently complex for more general applications, such as in global ocean general circulation models, or for predicting the response of marine systems to environmental change.
Nested, Ecosystem, Complexity, Likelihood-ratio, Akaike, Bayesian
0079-6611
49-65
Ward, Ben A.
9063af30-e344-4626-9470-8db7c1543d05
Schartau, Markus
ae10d82f-2a65-453c-abe9-dc8e12bc2443
Oschlies, Andreas
75e18f55-3134-44a2-82ba-71334397727f
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d
Follows, Michael J.
12c723bc-f2f8-43f4-a309-bff6885b9c7c
Anderson, Thomas R.
dfed062f-e747-48d3-b59e-2f5e57a8571d
Ward, Ben A.
9063af30-e344-4626-9470-8db7c1543d05
Schartau, Markus
ae10d82f-2a65-453c-abe9-dc8e12bc2443
Oschlies, Andreas
75e18f55-3134-44a2-82ba-71334397727f
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d
Follows, Michael J.
12c723bc-f2f8-43f4-a309-bff6885b9c7c
Anderson, Thomas R.
dfed062f-e747-48d3-b59e-2f5e57a8571d

Ward, Ben A., Schartau, Markus, Oschlies, Andreas, Martin, Adrian P., Follows, Michael J. and Anderson, Thomas R. (2013) When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites. Progress in Oceanography, 116, 49-65. (doi:10.1016/j.pocean.2013.06.002).

Record type: Article

Abstract

The degree of structural complexity that should be incorporated in marine biogeochemical models is unclear. We know that the marine ecosystem is complex, and that its observed behaviour is attributable to the interaction of a large number of separate processes, but observations are scarce and often insufficient to constrain more than a small number of model parameters. This issue is addressed using a novel algorithm that systematically removes model processes that are not constrained by observations. The algorithm is applied to a one-dimensional, eight component ecosystem-biogeochemistry model at two North Atlantic time-series sites. Between 11 and 14 of the 30 model parameters can be removed at each site with no significant reduction in the model’s ability to fit upper ocean (0–200 m) biogeochemical tracer and productivity data. The statistically optimal model structures and parameters provide estimates of the most likely state variables and fluxes at each site. Differences in these estimates between the two sites indicate that the optimal models are specialised to both the physical environment and the assimilated observations. At each site the heavily reduced models may thus be suitable for diagnostic purposes but may not be sufficiently complex for more general applications, such as in global ocean general circulation models, or for predicting the response of marine systems to environmental change.

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More information

Published date: September 2013
Keywords: Nested, Ecosystem, Complexity, Likelihood-ratio, Akaike, Bayesian
Organisations: Marine Systems Modelling, Marine Biogeochemistry

Identifiers

Local EPrints ID: 356914
URI: http://eprints.soton.ac.uk/id/eprint/356914
ISSN: 0079-6611
PURE UUID: 6dbaf685-1231-4f92-9bef-2211ed21ce5a

Catalogue record

Date deposited: 17 Sep 2013 09:07
Last modified: 08 Jan 2022 06:05

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Contributors

Author: Ben A. Ward
Author: Markus Schartau
Author: Andreas Oschlies
Author: Adrian P. Martin
Author: Michael J. Follows
Author: Thomas R. Anderson

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