Search

In 2009, following approval of the European Marine Strategy Framework Directive (MSFD, 2008/56/EC), the European Commission (EC) created task groups to develop guidance for eleven quality descriptors that form the basis for evaluating ecosystem function. The objective was to provide European countries with practical guidelines for implementing the MSFD, and to produce a Commission Decision that encapsulated key points of the work in a legal framework. This paper presents a review of work carried out by the eutrophication task group, and reports our main findings to the scientific community. On the basis of an operational, management-oriented definition, we discuss the main methodologies that could be used for coastal and marine eutrophication assessment. Emphasis is placed on integrated approaches that account for physico–chemical and biological components, and combine both pelagic and benthic symptoms of eutrophication, in keeping with the holistic nature of the MSFD. We highlight general features that any marine eutrophication model should possess, rather than making specific recommendations. European seas range from highly eutrophic systems such as the Baltic to nutrient-poor environments such as the Aegean Sea. From a physical perspective, marine waters range from high energy environments of the north east Atlantic to the permanent vertical stratification of the Black Sea. This review aimed to encapsulate that variability, recognizing that meaningful guidance should be flexible enough to accommodate the widely differing characteristics of European seas, and that this information is potentially relevant in marine ecosystems worldwide. Given the spatial extent of the MSFD, innovative approaches are required to allow meaningful monitoring and assessment. Consequently, substantial logistic and financial challenges will drive research in areas such as remote sensing of harmful algal blooms, in situ sensor development, and mathematical models. Our review takes into ac, SCOPUS: ar.j, info:eu-repo/semantics/published

Nearly half of the earth’s photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT)over nearly two decades. Specifically, skill was assessed based on the models’ ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90%of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-a was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-a time series were available.

Data assembly and processing centers are essential elements of the operational oceanography infrastructure. They provide data and products needed by modeling and data assimilation systems; they also provide products directly useable for applications. The paper will discuss the role and functions of the data centers for operational oceanography and describe some of the main data assembly centers developed during GODAE: Argo and Coriolis data centers for in-situ data, SSALTO/DUACS and NAVOCEAN for altimetry and GHRSST for SST. An overview of other data centers (wind and fluxes, ocean colour, sea ice) will also be given. Major progresses have been achieved over the past 10 years to validate, intercalibrate and merge altimeter data from multiple satellites. Accuracy and timeliness of products have been improved and new products have been developed. The same is true for sea surface temperature data through the GHRSST Pilot Project. A breakthrough in processing, quality control and assembly for in-situ data has also been achieved through the development of the real time and delayed mode Argo data system. In-situ and remote sensing data are now systematically and jointly used for calibration, validation and long term monitoring of the quality and consistency of the global ocean observing system (e.g. altimetry and tide gauges, Argo and altimetry, in-situ and satellite sea surface temperature data). Main results will be illustrated. We will also review the development and use of products merging in-situ and remote sensing data (e.g. surface currents, 3D temperature and salinity fields derived from altimetry, joint use of sea surface temperature and ocean colour and altimeter data). Future issues and main prospects will be discussed., Presented at the Conference on Global Ocean Data Assimilation Experiment (GODAE), 2008. Published in the proceedings of the conference, Session 4, p236-261, 2008. Prepared in collaboration with Ifremer, Centre de Brest, Plouzane, France; CLS Space Oceanography Division, Toulouse, France; Scripps Institution of Oceanography, San Diego, CA; and others.

Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color-based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983–1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few lowPP (b0.2 g Cm-2 d-1)values; more than half of the total root-mean-squared model–data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s,which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison 6 years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.

Spectral models of solar irradiance in the atmosphere and underwater are combined with satellite ocean colour data to determine the water column integrated daily rate of primary production in an upwelling centre off the coast of Mauritania. The local algorithm of primary production is based on first principles of photosynthetic physiology in marine algae, and accounts for some optical characteristics of the atmosphere over various marine environments. The results (integrated daily rate of primary production) are shown for different computational schemes, including the segmentation of the studied area into several provinces, each of them having a proper set of bio-optical and meteorological parameters. Some examples are given to illustrate the validation of the model results with other in situ and theoretical studies in the same area.