Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation

Rückelt, Johannes; Sauerland, Volkmar; Slawig, Thomas; Srivastav, Anand; Ward, Ben A.; Patvardhan, C.

doi:10.1016/j.nonrwa.2010.03.006

Cited by 29 publications

(40 citation statements)

References 7 publications

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“…The considered system (1) is a spatially one-dimensional marine biogechemical model, that simulates the interaction of dissolved inorganic nitrogen N, phytoplankton P, zooplankton Z and detritus D. It was developed with the aim of simultaneously reproducing observations at three North Atlantic locations by the optimization of free parameters within credible limits, see [4]. The model uses the ocean circulation and temperature field in an off-line modus, i.e.…”

Section: Model Equationsmentioning

confidence: 99%

“…As a reference we also compare the results to those obtained by a direct optimization of the nonlinear model using constant parameters with a Sequential Quadratic Programming (SQP) method that takes into account parameter bounds. This method was used in [4]. We performed the optimization for the years 1994 to 1998, in contrast to the years 1991 to 1996 that were used in [4].…”

Section: Fit Of Model Output To Observational Datamentioning

confidence: 99%

“…This method was used in [4]. We performed the optimization for the years 1994 to 1998, in contrast to the years 1991 to 1996 that were used in [4]. The reason for this is that no zooplankton data are available at BATS for the years 1991 to 1993, which would be disadvantageous for the linearization procedure in the DLQC method.…”

Section: Fit Of Model Output To Observational Datamentioning

confidence: 99%

“…The circulation data are the turbulent mixing coefficient ν ρ = ν ρ (z, t) and the temperature Θ = Θ(z, t), which goes into the non-linear coupling terms q i , see (3). The vertical sinking velocity w i is a parameter of the biological model that is nonzero only for x 4 , i.e. w 1 = w 2 = w 3 = 0, w 4 = ws > 0.…”

Section: Introductionmentioning

confidence: 99%

“…As was reported in several publications with different optimization algorithms, the quality of the model-to-dat fit was not optimal, and in some cases it was difficult to identify the parameters uniquely, see for example [3], [5], [4]. In most cases, the parameters of the marine ecosystem models are assumed to be temporally constant.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Introducing Periodic Parameters in a Marine Ecosystem Model Using Discrete Linear Quadratic Control

Jarbi

Slawig

Oschlies

2013

IFIP Advances in Information and Communication Technology

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Abstract. This paper presents the application of the Discrete Linear Quadratic Control (DLQC) method for a parameter optimization problem in a marine ecosystem model. The ecosystem model simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column with temperature and turbulent diffusivity profiles taken from a three-dimensional ocean circulation model. We present the linearization method which is based on the available observations. The linearization is necessary to apply the DLQC method on the nonlinear system of state equations. We show the form of the linearized timevariant problems and the resulting two algebraic Riccati Equations. By using the DLQC method, we are able to introduce temporally varying periodic model parameters and to significantly improve -compared to the use of constant parameters -the fit of the model output to given observational data.

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Section: Model Equationsmentioning

confidence: 99%

Section: Fit Of Model Output To Observational Datamentioning

confidence: 99%

Section: Fit Of Model Output To Observational Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Introducing Periodic Parameters in a Marine Ecosystem Model Using Discrete Linear Quadratic Control

Jarbi

Slawig

Oschlies

2013

IFIP Advances in Information and Communication Technology

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Effects of parameter indeterminacy in pelagic biogeochemical modules of Earth System Models on projections into a warming future: The scale of the problem

Löptien

Dietze

2017

Global Biogeochemical Cycles

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Numerical Earth System Models are generic tools used to extrapolate present climate conditions into a warming future and to explore geoengineering options. Most of the current‐generation models feature a simple pelagic biogeochemical model component that is embedded into a three‐dimensional ocean general circulation model. The dynamics of these biogeochemical model components is essentially controlled by so‐called model parameters most of which are poorly known. Here we explore the feasibility to estimate these parameters in a full‐fledged three‐dimensional Earth System Model by minimizing the misfit to noisy observations. The focus is on parameter identifiability. Based on earlier studies, we illustrate problems in determining a unique estimate of those parameters that prescribe the limiting effect of nutrient‐ and light‐depleted conditions on carbon assimilation by autotrophic phytoplankton. Our results showcase that for typical models and evaluation metrics no meaningful “best” unique parameter set exists. We find very different parameter sets which are, on the one hand, equally consistent with our (synthetic) historical observations while, on the other hand, they propose strikingly differing projections into a warming climate.

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Marine Ecosystem Model Calibration through Enhanced Surrogate-Based Optimization

Prieß

Koziel

Slawig

2013

Advances in Intelligent Systems and Computing

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Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation

Cited by 29 publications

References 7 publications

Introducing Periodic Parameters in a Marine Ecosystem Model Using Discrete Linear Quadratic Control

Introducing Periodic Parameters in a Marine Ecosystem Model Using Discrete Linear Quadratic Control

Effects of parameter indeterminacy in pelagic biogeochemical modules of Earth System Models on projections into a warming future: The scale of the problem

Marine Ecosystem Model Calibration through Enhanced Surrogate-Based Optimization

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