Impact of assimilating physical oceanographic data on modeled ecosystem dynamics in the California Current System

Raghukumar, Kaustubha; Edwards, Christopher A.; Goebel, Nicole L.; Broquet, Grégoire; Veneziani, Milena; Moore, Andrew M.; Zehr, Jon P.

doi:10.1016/j.pocean.2015.01.004

Cited by 36 publications

(25 citation statements)

References 43 publications

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“…Inconsistent physical-biogeochemical data assimilation might generate incompatible density and nutrient profiles that may generate an extra amount of vertical flux of nutrients in this highly dynamical area, thus enhancing its productivity. Increase in nutrient availability along isocline surfaces has been observed by Raghukumar et al (2015) suggesting this as a possible cause of an increase in productivity in oligotrophic areas. The upgrade of MedBFM boundary conditions (at the Strait of Gibraltar, Med-PHY is coupled with the CMEMS global product while Med-BIO uses climatological biogeochemical value) with high-frequency values, and the extension of the Atlantic buffer zone, could improve the model performance in this area.…”

Section: Discussionmentioning

confidence: 91%

“…The Mediterranean Sea Monitoring and Forecasting Centre (Med-MFC; Tonani et al, 2013) is one of the regional systems and is composed of the physical system "Med-PHY" (Tonani et al, 2008;Oddo et al, 2009), which drives the biogeochemical system "Med-BIO" (Lazzari et al, 2010(Lazzari et al, , 2012 and the wave system "Med-WAV" (Ravdas et al, 2018). In recent years, following the CMEMS requirements, the Med-MFC has been consistently upgraded in the physical (Oddo et al, 2014;Clementi et al, 2017;Pistoia et al, 2018), wave , and biogeochemical components (Cossarini et al, 2015;Lazzari et al, 2016), including also data assimilation (Teruzzi et al, 2014;Storto et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Salon¹,

Cossarini²,

Bolzon³

et al. 2019

Ocean Sci.

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Abstract. The quality of the upgraded version of the Copernicus Marine Environment Monitoring Service (CMEMS) biogeochemical operational system of the Mediterranean Sea (MedBFM) is assessed in terms of consistency and forecast skill, following a mixed validation protocol that exploits different reference data from satellite, oceanographic databases, Biogeochemical Argo floats, and literature. We show that the quality of the MedBFM system has been improved in the previous 10 years. We demonstrate that a set of metrics based on the GODAE (Global Ocean Data Assimilation Experiment) paradigm can be efficiently applied to validate an operational model system for biogeochemical and ecosystem forecasts. The accuracy of the CMEMS biogeochemical products for the Mediterranean Sea can be achieved from basin-wide and seasonal scales to mesoscale and weekly scales, and its level depends on the specific variable and the availability of reference data, the latter being an important prerequisite to build robust statistics. In particular, the use of the Biogeochemical Argo floats data proved to significantly enhance the validation framework of operational biogeochemical models. New skill metrics, aimed to assess key biogeochemical processes and dynamics (e.g. deep chlorophyll maximum depth, nitracline depth), can be easily implemented to routinely monitor the quality of the products and highlight possible anomalies through the comparison of near-real-time (NRT) forecasts skill with pre-operationally defined seasonal benchmarks. Feedbacks to the observing autonomous systems in terms of quality control and deployment strategy are also discussed.

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Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Salon¹,

Cossarini²,

Bolzon³

et al. 2019

Ocean Sci.

View full text Add to dashboard Cite

show abstract

“…This was found to be due to issues with the model and data assimilation system, rather than any lack of consistency between the observations being assimilated. Much of this was down to known issues with physical data assimilation causing spurious vertical mixing, which is not unique to the system used in this study (While et al, 2010;Raghukumar et al, 2015;Park et al, 2018). But it also revealed complex interactions between the model and assimilation, with the assimilation of individual variables improving some non-assimilated variables while degrading others, and correcting some compensating errors while introducing others.…”

Section: Carbon Cycle Validation (Fig 6)mentioning

confidence: 91%

“…It is not yet routine though to combine the assimilation of physics and biogeochemistry in a single ocean reanalysis. A major reason for this is that, especially in global models, assimilating physical observations has been widely found to degrade biogeochemical fields (While et al, 2010;Raghukumar et al, 2015;Park et al, 2018) through the creation of spurious vertical mixing. This is an outstanding issue for the ocean data assimilation community, and has not been solved in this study.…”

mentioning

confidence: 99%

Assessing the role and consistency of satellite observation products in global physical-biogeochemical ocean reanalysis

Ford¹

2019

Preprint

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Abstract. As part of the European Space Agency's Climate Change Initiative, new sets of satellite observation products have been produced for Essential Climate Variables including ocean colour, sea surface temperature, sea level and sea ice. These new products have been assimilated into a global physical-biogeochemical ocean model, to create a set of 13-year reanalyses at 1° resolution and 3-year reanalyses at 1/4° resolution. In a series of experiments, the variables were assimilated individually and in combination, in order to assess their consistency from a data assimilation perspective. The satellite products, and the reanalyses assimilating them, were found to be consistent in their representation of spatial features such as fronts, sea ice extent and bloom activity. Assimilating multiple variables together often resulted in larger mean increments for a variable than assimilating it individually, revealing ways in which the model and assimilation scheme could be improved. Sea surface fugacity of carbon dioxide had lower errors against independent observations in the higher resolution simulations, and was improved by assimilating ocean colour or sea ice concentration, but degraded by assimilating sea surface temperature or sea level anomaly. Phytoplankton biomass correlated more strongly with net air-sea heat fluxes in the reanalyses than chlorophyll concentration did, and the correlation was weakened by assimilating ocean colour data, suggesting that studies of phytoplankton bloom initiation based solely on chlorophyll data may not provide a full understanding of the underlying processes.

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“…In ocean DA it has received much less attention, although many centers apply ad-hoc techniques [such as Incremental Analysis Updates (IAU)] to mitigate the problem. However, the ensuing ocean wave activity resulting from initialization shocks can be especially pernicious for some applications, such as biogeochemical modeling (e.g., Raghukumar et al, 2015;Waters et al, 2017).…”

Section: Observation Streams and Observation Errorsmentioning

confidence: 99%

Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean

et al. 2019

Self Cite

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Ocean data assimilation is increasingly recognized as crucial for the accuracy of realtime ocean prediction systems and historical re-analyses. The current status of ocean data assimilation in support of the operational demands of analysis, forecasting and reanalysis is reviewed, focusing on methods currently adopted in operational and realtime prediction systems. Significant challenges associated with the most commonly employed approaches are identified and discussed. Overarching issues faced by ocean data assimilation are also addressed, and important future directions in response to scientific advances, evolving and forthcoming ocean observing systems and the needs of stakeholders and downstream applications are discussed.

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Impact of assimilating physical oceanographic data on modeled ecosystem dynamics in the California Current System

Cited by 36 publications

References 43 publications

Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts

Assessing the role and consistency of satellite observation products in global physical-biogeochemical ocean reanalysis

Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean

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