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| Titel |
Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha) |
| VerfasserIn |
J. C. P. Hemmings, P. G. Challenor |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 5, no. 2 ; Nr. 5, no. 2 (2012-04-20), S.471-498 |
| Datensatznummer |
250002453
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| Publikation (Nr.) |
 copernicus.org/gmd-5-471-2012.pdf |
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| Zusammenfassung |
A wide variety of different plankton system models have been coupled with
ocean circulation models, with the aim of understanding and predicting
aspects of environmental change. However, an ability to make reliable
inferences about real-world processes from the model behaviour demands a
quantitative understanding of model error that remains elusive. Assessment of
coupled model output is inhibited by relatively limited observing system
coverage of biogeochemical components. Any direct assessment of the plankton
model is further inhibited by uncertainty in the physical state. Furthermore,
comparative evaluation of plankton models on the basis of their design is
inhibited by the sensitivity of their dynamics to many adjustable parameters.
Parameter uncertainty has been widely addressed by calibrating models at
data-rich ocean sites. However, relatively little attention has been given to
quantifying uncertainty in the physical fields required by the plankton
models at these sites, and tendencies in the biogeochemical properties due to
the effects of horizontal processes are often neglected.
Here we use model twin experiments, in which synthetic data are assimilated
to estimate a system's known "true" parameters, to investigate the impact of
error in a plankton model's environmental input data. The experiments are
supported by a new software tool, the Marine Model Optimization Testbed,
designed for rigorous analysis of plankton models in a multi-site 1-D
framework. Simulated errors are derived from statistical characterizations of
the mixed layer depth, the horizontal flux divergence tendencies of the
biogeochemical tracers and the initial state. Plausible patterns of
uncertainty in these data are shown to produce strong temporal and spatial
variability in the expected simulation error variance over an annual cycle,
indicating variation in the significance attributable to individual
model-data differences. An inverse scheme using ensemble-based estimates of
the simulation error variance to allow for this environment error performs
well compared with weighting schemes used in previous calibration studies,
giving improved estimates of the known parameters. The efficacy of the new
scheme in real-world applications will depend on the quality of statistical
characterizations of the input data. Practical approaches towards developing
reliable characterizations are discussed. |
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