This paper proposes the use of assimilation of phytoplankton functional types (PFTs) surface chlorophyll for operational forecasting of biogeochemistry on the North‐West European (NWE) Shelf. We explicitly compare the 5‐day forecasting skill of three runs of a physical‐biogeochemical model: (a) a free reference run, (b) a run with daily data assimilation (DA) of total surface chlorophyll (ChlTot), and (c) a run with daily PFTs DA. We show that small total chlorophyll model bias hides comparatively large biases in PFTs chlorophyll, which ChlTot DA fails to correct. This is because the ChlTot DA splits the assimilated total chlorophyll into PFTs by preserving their simulated ratios, rather than taking account of the observed PFT concentrations. Unlike ChlTot DA, PFTs DA substantially improves model representation of PFTs chlorophyll. During forecasting the DA reanalysis skill in representing PFTs chlorophyll degrades toward the free run skill; however, PFTs DA outperforms free run within the whole 5‐day forecasting period. We validated our results with in situ data, and we demonstrated that (in both DA cases) the DA substantially improves the model representation of CO2 fugacity (PFTs DA more than ChlTot DA). ChlTot DA has a positive impact on the representation of silicate, while the PFTs DA seems to have a negative impact. The impact of DA on nitrate and phosphate is not significant. The implications of using a univariate assimilation method, which preserves the phytoplankton stochiometry, and the impact of model biases on the nonassimilated variables are discussed.
Ocean ecosystems are subject to a multitude of stressors, including changes in ocean physics and biogeochemistry, and direct anthropogenic influences. Implementation of protective and adaptive measures for ocean ecosystems requires a combination of ocean observations with analysis and prediction tools. These can guide assessments of the current state of ocean ecosystems, elucidate ongoing trends and shifts, and anticipate impacts of climate change and management policies. Analysis and prediction tools are defined here as ocean circulation models that are coupled to biogeochemical or ecological models. The range of potential applications for these systems is broad, ranging from reanalyses for the assessment of past and current states, and short-term and seasonal forecasts, to scenario simulations including climate change projections. The objectives of this article are to illustrate current capabilities with regard to the three types of applications, and to discuss the challenges and opportunities. Representative examples of global and regional systems are described with particular emphasis on those in operational or pre-operational use. With regard to the benefits and challenges, similar considerations apply to biogeochemical and ecological prediction systems as do to physical systems. However, at present there are at least two major differences: (1) biogeochemical observation streams are much sparser than physical streams presenting a significant hinderance, and (2) biogeochemical and ecological models are largely unconstrained because of insufficient observations. Expansion of biogeochemical and ecological observation systems will allow for significant advances in the development and application of analysis and prediction tools for ocean biogeochemistry and ecosystems, with multiple societal benefits.
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.
International audienceBuilding the capacity for monitoring and forecasting marine biogeochemistry and ecosystem dynamics is a scientific challenge of strategic importance in the context of rapid environmental change and growing public awareness of its potential impacts on marine ecosystems and resources. National Operational Oceanography centres have started to take up this challenge by integrating biogeochemistry in operational systems. Ongoing activities are illustrated in this paper by presenting examples of (pre-)operational biogeochemical systems active in Europe and North America for global to regional applications. First-order principles underlying biogeochemical modelling are briefly introduced along with the description of biogeochemical components implemented in these systems. Applications are illustrated with examples from the fields of hindcasting and monitoring ocean primary production, the assessment of the ocean carbon cycle and the management of living resources. Despite significant progress over the past 5 years in integrating biogeochemistry into (pre-)operational data-assimilation systems, a sustained research effort is still needed to assess these systems and their products with respect to their usefulness to the management of marine systems
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.