Skill assessment in ocean biological data assimilation

Gregg, Watson W.; Friedrichs, Marjorie A. M.; Robinson, Allan R.; Rose, Kenneth A.; Schlitzer, Reiner; Thompson, Keith R.; Doney, Scott C.

doi:10.1016/j.jmarsys.2008.05.006

Cited by 86 publications

(83 citation statements)

References 74 publications

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“…Dowd et al (2014) provide a helpful and up-to-date overview of mainly sequential DA approaches where state estimation is combined with parameter estimation. Gregg et al (2009) and Stow et al (2009) discuss how the success of DA results of marine ecosystem models has been evaluated in the past and how model performance can be generally assessed. Fundamentals on DA that include aspects relevant to marine ecosystem and biogeochemical modelling are explained in Wikle and Berliner (2007) and in Rayner et al (2016).…”

Section: Inferences From Data Assimilationmentioning

confidence: 99%

“…Parameter values are often optimized for local ocean sites, but ideally, parameter estimates from one site should improve model performance at other locations as well. The generality of optimized models can be tested by cross-validating against independent data, providing a direct and effective test of predictive skill (Gregg et al, 2009). …”

Section: Cross-validation and Model Complexitymentioning

confidence: 99%

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Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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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.

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Section: Inferences From Data Assimilationmentioning

confidence: 99%

Section: Cross-validation and Model Complexitymentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…Since all models have to deal with uncertainties due to limitations in forcing and process descriptions, and real measurements are limited by a poor coverage in time and space, methods of data assimilation (Gregg et al, 2009) may provide a synthesis between models and observations. Data assimilation is frequently used in prognostic models for improving the predictive capacity of models.…”

Section: Introductionmentioning

confidence: 99%

Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation

Liu

Meier

Eilola

2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe impact of assimilating temperature, salinity, oxygen, phosphate and nitrate observations on marine ecosystem modelling is assessed. For this purpose, two 10-yr (1970Á1979) reanalyses of the Baltic Sea are carried out using the ensemble optimal interpolation (EnOI) method and a coupled physical-biogeochemical model of the Baltic Sea. To evaluate the reanalyses, climatological data and available biogeochemical and physical in situ observations at monitoring stations are compared with results from simulations with and without data assimilation. In the first reanalysis, only observed temperature and salinity profiles are assimilated, whereas biogeochemical observations are unused. Although simulated temperature and salinity improve considerably as expected, the quality of simulated biogeochemical variables does not improve and deep water nitrate concentrations even worsen. This unexpected behaviour is explained by a lowering of the halocline in the Baltic proper due to the assimilation causing increased oxygen concentrations in the deep water and consequently altered nutrient fluxes. In the second reanalysis, both physical and biogeochemical observations are assimilated and good quality in all variables is found. Hence, we conclude that if a data assimilation method like the EnOI is applied, all available observations should be used to perform reanalyses of high quality for the Baltic Sea biogeochemical state estimates.

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“…Thus, independent assessment is only possible by: (1) withholding part of the dataset for statistical quantification of errors (a trade-off between a sufficient population size to estimate a statistic while not significantly impacting the quality of the system performance being measured); or by (2) using other sources of data that have not been assimilated (Gregg et al 2009). The latter is generally employed with data not available in near-real time, which is useful for reanalysis (or hindcast) evaluation, but not for operational routine verification.…”

Section: S222mentioning

confidence: 99%

Recent progress in performance evaluations and near real-time assessment of operational ocean products

Hernández

Blockley

Brassington

et al. 2015

Journal of Operational Oceanography

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Skill assessment in ocean biological data assimilation

Cited by 86 publications

References 74 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation

Recent progress in performance evaluations and near real-time assessment of operational ocean products

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