Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model

Doron, Maéva; Brasseur, Pierre; Brankart, Jean-Michel; Losa, Svetlana N.; Melet, Angélique

doi:10.1016/j.jmarsys.2013.02.007

Cited by 43 publications

(50 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the fact that these studies used different models, it is possible to compare some optimized parameters that are equivalent or closely related between all studies. However, little obvious consistency is seen in the spatial patterns between their estimates, although Doron et al (2013) suggested some similarity between their estimates of phytoplankton maximum growth rate and zooplankton maximum grazing rate with those of Losa et al (2004).…”

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 97%

“…Large-scale studies for the North Atlantic have shown comparably strong regional differences between parameter estimates (Hemmings et al, 2003;Losa et al, 2004;Doron et al, 2013;Kuhn et al, 2015). A set of sites representing distinct latitude bands was considered for a 1-year calibration of an NPZ model in Hemmings et al (2003).…”

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 99%

“…A set of sites representing distinct latitude bands was considered for a 1-year calibration of an NPZ model in Hemmings et al (2003). The annual cycle at locations on a 5 • grid was simulated with variable parameter estimates of a NPZD model in Losa et al (2004), and individual parameter estimates for 13 provinces in the North Atlantic, predefined according to Longhurst (1995), were derived for a six-compartment 3-D biogeochemical model in Doron et al (2013). Kuhn et al (2015) estimated NPZD model parameters for six 5 × 10 • regions of the central North Atlantic.…”

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 99%

“…In some cases, parameters are allowed to vary in space and in time (Tjiputra et al, 2007;Fan and Lv, 2009;Doron et al, 2013;Li et al, 2013). Cross-validation tests comparing the merits of varying and non-varying parameter solutions are mostly lacking, which prevents inferences being drawn about the superiority of these parameter variations for improving predictive skill.…”

Section: Time-varying Parametersmentioning

confidence: 99%

See 3 more Smart Citations

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

Abstract. 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 spacedependent 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.

show abstract

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 97%

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 99%

Section: Regional Differences Between Parameter Estimatesmentioning

confidence: 99%

Section: Time-varying Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The capability of generating biogeochemical reanalyses could be used to differentiate natural variability and long-term trends. Assimilation of biological data proved to be efficient in reducing model biases not only of the assimilated variables, such as chlorophyll (Fontana et al 2013), but also of the unobserved simulated variables or biological process parameterizations (Doron et al 2013). The use of multivariate assimilation schemes, integrating physical and biological observations, remains a high priority for GOV, and the expansion of these techniques to s266…”

Section: Marine Ecosystem Assessment and Predictionmentioning

confidence: 99%

Synthesis of new scientific challenges for GODAE OceanView

Schiller

Bell

Brassington

et al. 2015

Journal of Operational Oceanography

Self Cite

View full text Add to dashboard Cite

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Mattern

Fennel

Dowd

2014

Geophysical Research Letters

View full text Add to dashboard Cite

Using two emulator‐based procedures, we estimate time‐dependent values for two key plankton parameters in a three‐dimensional biogeochemical (BGC) ocean model. The estimation is based on a 4 year time series of daily surface satellite chlorophyll observations. The estimated parameters display an annual periodicity that can be explained by the succession of plankton groups in the study region. Model simulations using these parameters show improved fit to observations and better forecasting abilities compared to simulations with constant optimal parameters; the newly introduced sequential parameter estimation procedure creates the strongest improvement. The inclusion of time‐dependent parameters represents a simple way to improve the predictive skill of BGC models and their representation of plankton dynamics.

show abstract

Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model

Cited by 43 publications

References 41 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Synthesis of new scientific challenges for GODAE OceanView

Periodic time‐dependent parameters improving forecasting abilities of biological ocean models

Contact Info

Product

Resources

About