Results from a three‐dimensional, nested biological‐physical model of the California Current System and comparisons with statistics from satellite imagery

Powell, Thomas M.; Lewis, Craig V.W.; Curchitser, Enrique N.; Haidvogel, Dale B.; Hermann, Albert J.; Dobbins, Elizabeth L.

doi:10.1029/2004jc002506

Cited by 100 publications

(86 citation statements)

References 62 publications

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“…This approach could incorporate the comparison of modelled vs. observed Fourier spectra and cross-spectra/coherences (e.g. Powell et al, 2006). Whether the statistics e.g.…”

Section: 2)mentioning

confidence: 99%

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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“…This approach could incorporate the comparison of modelled vs. observed Fourier spectra and cross-spectra/coherences (e.g. Powell et al, 2006). Whether the statistics e.g.…”

Section: 2)mentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…ROMS includes accurate and efficient physical and numerical algorithms McWilliams 2003, 2005) and several coupled biogeochemical models (Powell et al 2006, Fennel et al 2006. A four-compartment (nitrogen, phytoplankton, zooplankton and detritus), N-based biogeochemical model (Fennel et al 2006) was run coupled on-line with the hydrodynamics model.…”

Section: Modelmentioning

confidence: 99%

The role of ocean velocity in chlorophyll variability. A modelling study in the Alboran Sea

Solé¹,

Ballabrera-Poy²,

Macías³

et al. 2016

Sci. Mar.

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Summary:In this work we focus on the Alboran Sea (western Mediterranean) to relate wind field and ocean velocity variability with chlorophyll a (Chl a) behaviour, using a 2-km resolution, coupled 3D ocean circulation-NPZD model (ROMS). The analysis is done in three steps. First, we split the seasonal and residual contribution for the fields under study. Second, we calculate the corresponding empirical orthogonal functions (EOFs) for the seasonal and residual parts. Finally, we relate each pair of variables for both seasonal and residual contribution EOFs. The results reported here allow the links between wind and Chl a to be quantified. We explain these links in terms of the ocean velocity field acting as a driver of Chl a variability. The results show that, although the seasonal part of the Chl a field is modulated by the vertical velocity, the residual component is modulated by the horizontal velocity components. Vertical velocities are responsible, through coastal upwelling, for Chl a bloom enhancement, while horizontal velocities spread coastal Chl a surface blooms off-shore.Keywords: Alboran Sea; chlorophyll; wind forcing; primary production; ocean biogeochemical model; ROMS model; EOF analysis.El papel de la velocidad oceánica en la variabilidad de la clorofila. Un estudio de modelado en el mar de Alborán Resumen: En este trabajo nos centramos en el mar de Alborán (Mediterráneo Occidental) para relacionar los campos de velocidad del viento y del océano con la variabilidad de la clorofila a (Chl a), utilizando un modelo de circulación 3D-NPZD (ROMS-Fennel) con una resolución de 2 km. El análisis se realiza en tres pasos. En primer lugar, separamos la contribución estacional y el residuo para todos los campos de estudio. En segundo lugar, calculamos las correspondientes funciones ortogonales empíricas (EOFs) para las components estacionales y residuales. Por último, relacionamos cada par de variables para ambos, EOFs estacionales y residuales. Los resultados permiten la cuantificación de las relaciones entre el viento y la clorofila. Explicamos estas relaciones en términos del campo de velocidades del océano que actúa como motor de la variabilidad de la Chl a. Los resultados muestran que, aunque la parte estacional de campo Chl a es modulada por la velocidad vertical, la componente residual es modulada por las componentes de la velocidad horizontal. Las velocidades verticales son responsables, a través de la surgencia costera, de incrementar el valor de la proliferación de Chl a, mientras que las velocidades horizontales extienden estas proliferaciones costeras superficiales a mar abierto.Palabras clave: mar de Alborán; clorofila; forzamiento del viento; producción primaria; modelo oceánico biogeoquímico; modelo ROMS; análisis EOF.

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“…Simpler models have shown reasonable distributions of overall chlorophyll levels (Gruber et al, 2006;Omta et al, 2009) and decorrelation statistics of physical and biological fields (Powell et al, 2006). As Goebel et al (2010) note, this self-assembling ecosystem model enables a study of biogeography that is not possible with a more traditional model.…”

Section: The Diversity-productivity Scattermentioning

confidence: 99%

Modeled phytoplankton diversity and productivity in the California Current System

Goebel

Edwards

Zehr

et al. 2013

Ecological Modelling

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a b s t r a c tWe explore the phytoplankton community structure and the relationship between phytoplankton diversity and productivity produced by a self-emergent ecosystem model that represents a large number of phytoplankton type and is coupled to a circulation model of the California Current System. Biomass of each modeled phytoplankton type, when averaged over the uppermost model level and for 5-years, spans 7 orders of magnitude; 13 phytoplankton types contribute to the top 99.9% of community biomass, defining modeled species richness. Instantaneously, modeled species richness ranges between 1 and 17 while the Shannon index reaches values of 2.3. Diversity versus primary productivity shows large scatter with low species richness at both high and low productivity levels and a wide range of values including the maximum at intermediate productivities. Highest productivity and low diversity is found in the nearshore upwelling region dominated by fast growing diatoms; lowest productivity and low diversity occurs in deep, light-limited regions; and intermediate productivity and high diversity characterize offshore, oligotrophic surface waters. Locally averaged diversity and productivity covary in time with the sign of correlation dependent on geographic region as representing portions of the diversity-productivity scatter.

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Results from a three‐dimensional, nested biological‐physical model of the California Current System and comparisons with statistics from satellite imagery

Cited by 100 publications

References 62 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

The role of ocean velocity in chlorophyll variability. A modelling study in the Alboran Sea

Modeled phytoplankton diversity and productivity in the California Current System

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