ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2017-03-29
    Description: To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time- and space-dependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2017-04-06
    Description: The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2017-04-06
    Description: Mesocosm experiments on phytoplankton dynamics under high CO2 concentrations mimic the response of marine primary producers to future ocean acidification. However, potential acidification effects can be hindered by the high standard deviation typically found in the replicates of the same CO2 treatment level. In experiments with multiple unresolved factors and a sub-optimal number of replicates, post-processing statistical inference tools might fail to detect an effect that is present. We propose that in such cases, data-based model analyses might be suitable tools to unearth potential responses to the treatment and identify the uncertainties that could produce the observed variability. As test cases, we used data from two independent mesocosm experiments. Both experiments showed high standard deviations and, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Conversely, our simulations showed earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggested that uncertainties in average cell size, phytoplankton biomass losses, and initial nutrient concentration potentially outweigh acidification effects by triggering strong variability during the bloom phase. We also estimated the thresholds below which uncertainties do not escalate to high variability. This information might help in designing future mesocosm experiments and interpreting controversial results on the effect of acidification or other pressures on ecosystem functions.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2016-09-26
    Description: A series of studies were conducted during the last two decades to investigate effects of ocean acidification (OA) on phytoplankton physiology, plankton ecology, and biogeochemical dynamics of marine ecosystems. Among those studies are experiments with tanks or bags called mesocosms, with some enclosed water volume that typically comprised a natural plankton community found in the surrounding environment. The Pelagic Ecosystem CO2 – Enrichment Study PeECE-I experiment is one such study, where mesocosms were perturbed and exposed to different carbon dioxide (CO2) concentrations to determine responses in growth dynamics of the coccolithophorid Emiliania huxleyi, a marine calcifying algae. The data from replicate mesocosms of PeECE-I show some natural variability and significant differences were revealed in the accumulation of particulate inorganic carbon (PIC) between mesocosms of similar CO2 treatments. In our study we reanalyse PeECE-I data and apply an optimality-based model approach to understand most of the variability observed, with major focus on total alkalinity (TA) and calcification. We explore how much of the observed variability in data can be explained by variations of initial conditions and by the effect of CO2 perturbations. According to our model approach, changes in cellular calcite formation are resolved at the organism-level in response to variations in CO2. With a data assimilation (DA) method we obtain three distinctive ensembles of model solutions, with low, medium and high calcification rates. Optimised values of initial conditions turned out to be correlated with estimates physiological model parameters. The spread of ensemble model solutions captures most of the observed variability, corresponding to the combinations of parameter estimates. Optimised model solutions of the high CO2 treatment are shown to systematically overestimate observed PIC production. Thus, the simulated CO2 effect on calcification is likely too weak. At the same time our model results yield large differences in optimal mass flux estimates of carbon and of nitrogen even between mesocosms exposed to similar CO2 conditions.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 5
    Publication Date: 2016-06-20
    Description: To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterisations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data, therefore data assimilation methods are utilised to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are 1) planktonic ecosystem models are imperfect and 2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also addressing issues of parameter identifiability. Aspects of model complexity will be covered and we describe how results from cross-validation studies provide much insight in this respect. Moreover, we elucidate inferences made in studies that allowed for variations in space and time of parameter values. The usage of dynamical and statistical emulator approaches will be briefly explained, discussing their advantage for parameter optimisations of large-scale biogeochemical models. Our survey extends to studies that approached parameter identification in global biogeochemical modelling. Parameter estimation results will exemplify some of the advantages and remaining problems in optimising global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 6
    Publication Date: 2016-04-08
    Description: Mesocosm experiments on phytoplankton dynamics under high CO2 concentrations mimic the response of marine primary producers to future ocean acidification. However, potential acidification effects can be hindered by the high standard deviation typically found in the distribution of the replicates exposed to the same treatment. In experiments with multiple unresolved factors and a suboptimal number of replicates, post-processing statistical inference tools may fail to detect an effect. In such cases, model-based data analyses are suitable tools to unearth potential responses to the treatment and to identify which uncertainties may give rise to the observed divergences. As test cases, we use data showing high variability from two independent mesocosm experiments, where, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Our simulations, in stead, show earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggest that uncertainties in average cell size, phytoplankton biomass losses and initial nutrient concentration potentially outweigh acidification effects by triggering strong variability during the bloom phase. We also estimate the thresholds below which uncertainties do not escalate into high variability. This information may help to interpret controversial results about acidification and to design future mesocosm experiments.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...