Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0)

Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to  ≈ 100) parameters. The values of many of these parameters are empirically difficult to const...

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Vydáno v:Geoscientific Model Development Ročník 10; číslo 1; s. 127 - 154
Hlavní autoři: Kriest, Iris, Sauerland, Volkmar, Khatiwala, Samar, Srivastav, Anand, Oschlies, Andreas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 09.01.2017
Copernicus Publications
Témata:
Analysis
Biogeochemical cycles
Biogeochemistry
Biology
Climatology
Computer simulation
Covariance matrix
Distribution
Environmental changes
Evolutionary algorithms
Feasibility studies
Fluxes
Frameworks
Marine zooplankton
Mopping
Nitrogen
Nutrients
Ocean circulation
Ocean models
Ocean temperature
Oceans
Optimization
Oxygen
Parameter estimation
Parameters
Population
Probability distribution
Simulation
Three dimensional models
Tracers
Tracers (Chemistry)
Zooplankton
Germany
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
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Abstract Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to  ≈ 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
AbstractList Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to  ≈ 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to  ≈ 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( [approximate] 10 to [approximate] 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many (≈10 to≈100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes.We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.
Audience Academic
Author Khatiwala, Samar
Sauerland, Volkmar
Kriest, Iris
Srivastav, Anand
Oschlies, Andreas
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  surname: Oschlies
  fullname: Oschlies, Andreas
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Snippet Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical...
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StartPage 127
SubjectTerms Analysis
Biogeochemical cycles
Biogeochemistry
Biology
Climatology
Computer simulation
Covariance matrix
Distribution
Environmental changes
Evolutionary algorithms
Feasibility studies
Fluxes
Frameworks
Marine zooplankton
Mopping
Nitrogen
Nutrients
Ocean circulation
Ocean models
Ocean temperature
Oceans
Optimization
Oxygen
Parameter estimation
Parameters
Population
Probability distribution
Simulation
Three dimensional models
Tracers
Tracers (Chemistry)
Zooplankton
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Title Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0)
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