Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

Ciavatta, Stefano; Torres, Ricardo; Saux-Picart, S.; Allen, J. Icarus

doi:10.1029/2011jc007219

Cited by 57 publications

(103 citation statements)

References 56 publications

(145 reference statements)

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“…Ocean colour data assimilation is being increasingly utilized by the reanalysis and forecasting community (Gehlen et al, 2015), and has already been successfully demonstrated for ERSEM (Ciavatta et al, 2011(Ciavatta et al, , 2014. A suitable ocean colour assimilation scheme for operational purposes is being developed as a collaboration between the Met Office and PML, to be implemented in the SSB ERSEM version and run operationally as part of CMEMS.…”

Section: Summary and Discussionmentioning

confidence: 99%

Observing and modelling phytoplankton community structure in the North Sea

et al. 2017

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Abstract. Phytoplankton form the base of the marine food chain, and knowledge of phytoplankton community structure is fundamental when assessing marine biodiversity. Policy makers and other users require information on marine biodiversity and other aspects of the marine environment for the North Sea, a highly productive European shelf sea. This information must come from a combination of observations and models, but currently the coastal ocean is greatly under-sampled for phytoplankton data, and outputs of phytoplankton community structure from models are therefore not yet frequently validated. This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea using accessory pigment analysis. The observations allow a good understanding of the patterns of surface phytoplankton biomass and community structure in the North Sea for the observed months of August 2010 and 2011. Two physical-biogeochemical ocean models, the biogeochemical components of which are different variants of the widely used European Regional Seas Ecosystem Model (ERSEM), were then validated against these and other observations. Both models were a good match for sea surface temperature observations, and a reasonable match for remotely sensed ocean colour observations. However, the two models displayed very different phytoplankton community structures, with one better matching the in situ observations than the other. Nonetheless, both models shared some similarities with the observations in terms of spatial features and interannual variability. An initial comparison of the formulations and parameterizations of the two models suggests that diversity between the parameter settings of model phytoplankton functional types, along with formulations which promote a greater sensitivity to changes in light and nutrients, is key to capturing the observed phytoplankton community structure. These findings will help inform future model development, which should be coupled with detailed validation studies, in order to help facilitate the wider application of marine biogeochemical modelling to user and policy needs.

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Section: Summary and Discussionmentioning

confidence: 99%

Observing and modelling phytoplankton community structure in the North Sea

et al. 2017

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“…The non-operational POLCOMS-ERSEM system in development at PML and the National Oceanography Centre in Liverpool (NOCL) uses satellite inherent optical properties within ERSEM which has been shown to have a positive impact on the model chlorophyll fields . In the long-term, work is being done to improve the ecosystem model further by including data assimilation of the chlorophyll field from satellite data (Ciavatta et al, 2011) and to include the satellite optical information as used in the POLCOMS-ERSEM system in either NRT or as climatological input for the AMM7-NE system.…”

Section: Discussionmentioning

confidence: 99%

Validation of the NEMO-ERSEM operational ecosystem model for the North West European Continental Shelf

2012

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Abstract. This paper details updates to the Met Office's operational coupled hydrodynamic-ecosystem model from the 7 km Medium-Resolution Continental Shelf -POLCOMS-ERSEM (MRCS-PE) system to the 7 km Atlantic Margin Model NEMO-ERSEM (AMM7-NE) system. We also provide a validation of the ecosystem component of the new operational system. Comparisons have been made between the model variables and available in situ, satellite and climatological data. The AMM7-NE system has also been benchmarked against the MRCS-PE system. The transition to the new AMM7-NE system was successful and it has been running operationally since March 2012 and has been providing products through MyOcean (http://www.myocean.eu.org) since that time. The results presented herein show the AMM7-NE system performs better than the MRCS-PE system with the most improvement in the model nutrient fields. The problem of nutrient accumulation in the MRCS-PE system appears to be solved in the new AMM7-NE system with nutrient fields improved throughout the domain as discussed in Sect. 4. Improvements in model chlorophyll are also seen but are more modest.

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“…Ourmières et al (2009) assimilated nitrate climatology values into a model of the North Atlantic, which improved the model's representation of nitrate compared to the climatology, and led to an improved representation of chlorophyll compared to satellite data. Ciavatta et al (2011) assimilated chlorophyll data into a model of the western English Channel. This resulted in improvements compared to the assimilated data, and also an improved representation of other biogeochemical variables, including a number of plankton functional types and nutrients, compared to independent in situ observations.…”

Section: A Ford Et Al: Assimilating Globcolour Ocean Colour Datamentioning

confidence: 99%

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

et al. 2012

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Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physicalbiogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO 2 , but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

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Can ocean color assimilation improve biogeochemical hindcasts in shelf seas?

Cited by 57 publications

References 56 publications

Observing and modelling phytoplankton community structure in the North Sea

Observing and modelling phytoplankton community structure in the North Sea

Validation of the NEMO-ERSEM operational ecosystem model for the North West European Continental Shelf

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

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