A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations
<p>The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivi...
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2018-11-01
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doaj-79ff18b9edd7406592d5e47f94f03f032020-11-25T02:28:30ZengCopernicus PublicationsBiogeosciences1726-41701726-41892018-11-01156685671110.5194/bg-15-6685-2018A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observationsP. Anugerahanti0S. Roy1S. Roy2K. Haines3Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading, RG6 6AB, UKDepartment of Geography and Environmental Science, University of Reading, Whiteknights, Reading, RG6 6AB, UKSchool of Agriculture, Policy, and Development, University of Reading, Whiteknights, Reading, RG6 6AR, UKDepartment of Meteorology and National Centre for Earth Observation, University of Reading, Whiteknights campus Early Gate, Reading, RG6 6BB, UK<p>The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69 mg m<sup>−3</sup> at coastal stations, 0.43 and 1.11 mg m<sup>−3</sup> on the abyssal plain, and 0.004 and 0.16 mg m<sup>−3</sup> at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.</p>https://www.biogeosciences.net/15/6685/2018/bg-15-6685-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
P. Anugerahanti S. Roy S. Roy K. Haines |
spellingShingle |
P. Anugerahanti S. Roy S. Roy K. Haines A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations Biogeosciences |
author_facet |
P. Anugerahanti S. Roy S. Roy K. Haines |
author_sort |
P. Anugerahanti |
title |
A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
title_short |
A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
title_full |
A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
title_fullStr |
A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
title_full_unstemmed |
A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
title_sort |
perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations |
publisher |
Copernicus Publications |
series |
Biogeosciences |
issn |
1726-4170 1726-4189 |
publishDate |
2018-11-01 |
description |
<p>The dynamics of biogeochemical models are determined by the mathematical
equations used to describe the main biological processes. Earlier studies
have shown that small changes in the model formulation may lead to major
changes in system dynamics, a property known as structural sensitivity. We
assessed the impact of structural sensitivity in a biogeochemical model of
intermediate complexity by modelling the chlorophyll and dissolved inorganic
nitrogen (DIN) concentrations. The model is run at five different
oceanographic stations spanning three different regimes: oligotrophic,
coastal, and the abyssal plain, over a 10-year timescale to observe the
effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient
Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with
each ensemble member having a combination of tuned function parameterizations
that describe some of the key biogeochemical processes, namely nutrient
uptake, zooplankton grazing, and plankton mortalities. The impact is
quantified using phytoplankton phenology (initiation, bloom time, peak
height, duration, and termination of phytoplankton blooms) and statistical
measures such as RMSE (root-mean-squared error), mean, and range for
chlorophyll and nutrients. The spread of the ensemble as a measure of
uncertainty is assessed against observations using the normalized RMSE ratio
(NRR). We found that even small perturbations in model structure can produce
large ensemble spreads. The range of 10-year mean surface chlorophyll
concentration in the ensemble is between 0.14 and 3.69 mg m<sup>−3</sup> at
coastal stations, 0.43 and 1.11 mg m<sup>−3</sup> on the abyssal plain, and 0.004
and 0.16 mg m<sup>−3</sup> at the oligotrophic stations. Changing both
phytoplankton and zooplankton mortalities and the grazing functions has the
largest impact on chlorophyll concentrations. The in situ measurements of
bloom timings, duration, and terminations lie mostly within the ensemble
range. The RMSEs between in situ observations and the ensemble mean and
median are mostly reduced compared to the default model output. The NRRs for
monthly variability suggest that the ensemble spread is generally narrow (NRR
1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for
surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are
obtained for the oligotrophic station ALOHA (for the surface and integrated
chlorophyll and bloom peak height), for coastal station L4 (for inter-annual
mean), and for the abyssal plain station PAP (for bloom peak height). Overall
our study provides a novel way to generate a realistic ensemble of a
biogeochemical model by perturbing the model equations and parameterizations,
which will be helpful for the probabilistic predictions.</p> |
url |
https://www.biogeosciences.net/15/6685/2018/bg-15-6685-2018.pdf |
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