Assimilation of seasonal chlorophyll and nutrient data into an adjoint three‐dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization

Tjiputra, Jerry; Polzin, Dierk; Winguth, Arne

doi:10.1029/2006gb002745

Cited by 78 publications

(87 citation statements)

References 47 publications

(62 reference statements)

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“…Initially, the results are discussed in view of the model formulation and main assumptions, providing explanations of the major biases and recommendations for model-data comparisons. Several studies have pointed out the existence of biogeographical provinces and that physically distinct oceanic regions have different biogeochemical characteristics (Longhurst, 2007), and some specific regional parameterizations might be useful to capture, for instance, the satellite-derived chlorophyll variability (Tjiputra et al, 2007). However, the extrapolation at larger spatial and temporal scales of limited observations describing carbon cycle rates may lead to misrepresentation of the microbial processes over the annual scale (e.g.…”

Section: Introductionmentioning

confidence: 99%

Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

Vichi

Masina

2009

Biogeosciences

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Abstract. Global Ocean Biogeochemistry General Circulation Models are useful tools to study biogeochemical processes at global and large scales under current climate and future scenario conditions. The credibility of future estimates is however dependent on the model skill in capturing the observed multi-annual variability of firstly the mean bulk biogeochemical properties, and secondly the rates at which organic matter is processed within the food web. For this double purpose, the results of a multi-annual simulation of the global ocean biogeochemical model PELAGOS have been objectively compared with multi-variate observations from the last 20 years of the 20th century, both considering bulk variables and carbon production/consumption rates. Simulated net primary production (NPP) is comparable with satellite-derived estimates at the global scale and when compared with an independent data-set of in situ observations in the equatorial Pacific. The usage of objective skill indicators allowed us to demonstrate the importance of comparing like with like when considering carbon transformation processes. NPP scores improve substantially when in situ data are compared with modeled NPP which takes into account the excretion of freshly-produced dissolved organic carbon (DOC). It is thus recommended that DOC measurements be performed during in situ NPP measurements to quantify the actual production of organic carbon in the surface ocean. The chlorophyll bias in the Southern Ocean that affects this model as well as several others is linked to the inadequate representation of the mixed layer seasonal cycle in the region. A sensitivity experiment confirms that the artificial increase of mixed layer depths towards the observed values substantially reduces the bias. Our assessment resultsCorrespondence to: M. Vichi (vichi@bo.ingv.it) qualify the model for studies of carbon transformation in the surface ocean and metabolic balances. Within the limits of the model assumption and known biases, PELAGOS indicates a net heterotrophic balance especially in the more oligotrophic regions of the Atlantic during the boreal winter period. However, at the annual time scale and over the global ocean, the model suggests that the surface ocean is close to a weakly positive autotrophic balance in accordance with recent experimental findings and geochemical considerations.

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Section: Introductionmentioning

confidence: 99%

Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

Vichi

Masina

2009

Biogeosciences

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“…In spite of recent advances in marine ecosystem modeling that now allow for the incorporation of multiple plankton functional types and/or size classes (e.g., Follows et al, 2007;Kishi et al, 2007;Salihoglu and Hofmann, 2007), it remains ambiguous as to whether models with additional plankton compartments necessarily perform better than models characterized by relatively simple structures. Although the use of a single plankton compartment may fail to resolve key processes in a given ecosystem (e.g., Ward et al, 2013), the inclusion of additional complexity in plankton structure comes with a substantial cost: significant uncertainties will inevitably be associated with the additional state variables and required parameters (Anderson, 2005;Flynn, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Although the use of a single plankton compartment may fail to resolve key processes in a given ecosystem (e.g., Ward et al, 2013), the inclusion of additional complexity in plankton structure comes with a substantial cost: significant uncertainties will inevitably be associated with the additional state variables and required parameters (Anderson, 2005;Flynn, 2005). Hence these trade-offs in model structure selection pose a challenging question: how does one determine how many phytoplankton and zooplankton compartments need to be included in a given application of a lower trophic model?…”

Section: Introductionmentioning

confidence: 99%

Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web

Xiao

Friedrichs

2014

Biogeosciences

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Abstract. Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20 % random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellitederived data does not pose a significant problem to the assimilation of satellite data into these models. However, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of over-tuning inherent in such complex models.

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“…Physical DA for improved ecosystem dynamics continues to be investigated [e.g., Berline et al, 2007] as well as DA with complete biogeochemical models but with simplified 1D physics [Magri et al, 2005]. Since biological rates and parameters are not well known in the ocean, parameter estimation techniques are often applied for marine biological studies [e.g., Solidoro et al, 2003;Faugeras et al, 2004;Losa et al, 2003Losa et al, , 2004Tjiputra et al, 2007].…”

Section: Overview Of Recent Biogeochemical-ecosystem Ocean Data Assimmentioning

confidence: 99%

Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation

Cossarini¹,

Lermusiaux²,

Solidoro³

2009

J. Geophys. Res.

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[1] An ensemble data assimilation scheme, Error Subspace Statistical Estimation (ESSE), is utilized to investigate the seasonal ecosystem dynamics of the Lagoon of Venice and provide guidance on the monitoring and management of the Lagoon, combining a rich data set with a physical-biogeochemical numerical estuary-coastal model. Novel stochastic ecosystem modeling components are developed to represent prior uncertainties in the Lagoon dynamics model, measurement model, and boundary forcing by rivers, open-sea inlets, and industrial discharges. The formulation and parameters of these additive and multiplicative stochastic error models are optimized based on data-model forecast misfits. The sensitivity to initial and boundary conditions is quantified and analyzed. Half-decay characteristic times are estimated for key ecosystem variables, and their spatial and temporal variability are studied. General results of our uncertainty analyses are that boundary forcing and internal mixing have a significant control on the Lagoon dynamics and that data assimilation is needed to reduce prior uncertainties. The error models are used in the ESSE scheme for ensemble uncertainty predictions and data assimilation, and an optimal ensemble dimension is estimated. Overall, higher prior uncertainties are predicted in the central and northern regions of the Lagoon. On the basis of the dominant singular vectors of the ESSE ensemble, the two major northern rivers are the biggest sources of dissolved inorganic nitrogen (DIN) uncertainty in the Lagoon. Other boundary sources such as the southern rivers and industrial discharges can dominate uncertainty modes on certain months. For dissolved inorganic phosphorus (DIP) and phytoplankton, dominant modes are also linked to external boundaries, but internal dynamics effects are more significant than those for DIN. Our posterior estimates of the seasonal biogeochemical fields and of their uncertainties in 2001 cover the whole Lagoon. They provide the means to describe the ecosystem and guide local environmental policies. Specifically, our findings and results based on these fields include the temporal and spatial variability of nutrient and plankton gradients in the Lagoon; dynamical connections among ecosystem fields and their variability; strengths, gradients and mechanisms of the plankton blooms in late spring, summer, and fall; reductions of uncertainties by data assimilation and thus a quantification of data impacts and data needs; and, finally, an assessment of the water quality in the Lagoon in light of the local environmental legislation.

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Assimilation of seasonal chlorophyll and nutrient data into an adjoint three‐dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization

Cited by 78 publications

References 47 publications

Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web

Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation

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