Seasonal variability of the Black Sea chlorophyll-a concentration

Chu, Peter C.; Ivanov, Leonid M.; Margolina, Tetyana

doi:10.1016/j.jmarsys.2005.01.001

Cited by 32 publications

(14 citation statements)

References 29 publications

(54 reference statements)

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“…This metal removal is consistent with the low oxygen saturation levels measured in the Black (average: 28 ± 4%) and Marmara Seas (average: 34 ± 2%), in contrast to the 74 ± 2% calculated for the rest of the sampling locations along the Mediterranean. The increased nutrients and metals inputs to the Black Sea have induced a basin-scale eutrophication (Chu et al, 2005) which is reflected in the high chlorophyll-a concentration measured in those waters (i.e., 1.7 ± 0.2 mg m −3 ). Although DSSW metal concentrations were less variable in the other regions of the Mediterranean Sea, higher levels (p < 0.005) of Cd (97 ± 13 pM), Co (169 ± 61 pM), and Cu (2.7 ± 0.9 nM) were measured in the Aegean Sea, in comparison to the concentrations detected in the Eastern (Cd: 79 ± 6 pM, Co: 115 ± 20 pM, and Cu: 1.6 ± 0.2 nM) and Western (Cd: 70 ± 9 pM, Co: 95 ± 30 pM, and Cu: 1.6 ± 0.3 nM) Mediterranean.…”

Section: Surface Water Concentrations and Longitudinal Distributions mentioning

confidence: 99%

Spatial gradients in trace metal concentrations in the surface microlayer of the Mediterranean Sea

et al. 2014

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The relationship between dust deposition and surface water metal concentrations is poorly understood. Dissolution, solubility, and partitioning reactions of trace metals from dust particles are governed by complex chemical, biological, and physical processes occurring in the surface ocean. Despite that, the role of the sea surface microlayer (SML), a thin, but fundamental component modulating the air-sea exchange of materials has not been properly evaluated. Our study revealed that the SML of the Mediterranean Sea is enriched with bioactive trace metals (i.e., Cd, Co, Cu, and Fe), ranging from 8 (for Cd) to 1000 (for Fe) times higher than the dissolved metal pool in the underlying water column. The highest enrichments were spatially correlated with the atmospheric deposition of mineral particles. Our mass balance results suggest that the SML in the Mediterranean Sea contains about 2 tons of Fe. However, we did not detect any trends between the concentrations of metals in SML with the subsurface water concentrations and biomass distributions. These findings suggest that future studies are needed to quantify the rate of metal exchange between the SML and the bioavailable pool and that the SML should be considered to better understand the effect of atmospheric inputs on the biogeochemistry of trace metals in the ocean.

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Section: Surface Water Concentrations and Longitudinal Distributions mentioning

confidence: 99%

Spatial gradients in trace metal concentrations in the surface microlayer of the Mediterranean Sea

et al. 2014

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“…3). The Romanian coastal waters generally show a winter/early spring diatoms bloom (February-March), closely related to intense mixing processes during the cold season (Bodeanu, 1987(Bodeanu, -1988Yunev et al, 2002;Chu et al, 2005;Demidov, 2008). Besides, these processes which provide an internal supply of nutrients in the surface layer, the large fluctuations of the Danube's discharges have a strong influence on the year-to-year variability of the winter/early spring bloom (magnitude, timing, and duration) mainly through the external input of nutrients, and also through the freshwater phytoplankton input (Bodeanu, 1987(Bodeanu, -1988.…”

Section: Discussionmentioning

confidence: 99%

“…l -1 in April; Mihnea, unpublished data). Generally, in the Black Sea shelf ecosystems, the winter-spring Chl a maximum is followed by lower surface Chl a in April-June, mainly as a result of nutrient depletion, as well as the increased grazing pressure (Chu et al, 2005;Demidov, 2008). Spatial-temporal extension of low Chl a levels in the Romanian coastal waters is strongly influenced by the Danube's discharges and the wind regime.…”

Section: Discussionmentioning

confidence: 99%

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Temporal variation of surface chlorophyll a in the Romanian near-shore waters

Vasiliu¹,

Boicenco²,

Gomoiu³

et al. 2012

Medit. Mar. Sci.

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Chlorophyll a (Chl a) dynamics in the near-shore waters of the NW Black Sea was investigated between 2002 and 2010 in the Mamaia Bay (north of Constanta, Romania) in relation to some physical-chemical parameters. Chl a ranged from values below detection limit (0.17 µg ) to 76.13 µg . l -1 , and showed large temporal variability (CV = 142.3%), strongly related to the Danube's discharges, meteorological conditions, and anthropogenic pressures.Seasonally, Chl a showed a winter/early spring maximum, sometimes followed by a stronger one in April/early May, closely linked to the Danube's higher discharges in spring. After significantly lower concentrations in late spring/early summer, Chl a exhibited its strongest maximum in summer (July-August), followed by another one in autumn (late September-October).Interannual variation of Chl a seems to be controlled by the hydrometeorological conditions in summer. Thus, the highest annual Chl a means were observed in 2006 (8.56 ± 8.35 µg . l -1 ) and 2010 (9.20 ± 11.72 µg . l -1 ), when, also, the summer Chl a concentrations were maximal due to the large riverine discharges. The lowest annual Chl a mean was observed in 2004 (4.57 ± 9.81 µg . l -1 ), closely linked to minimal summer Chl a resulted from a strong P limitation during summertime.

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“…Chu et al (2003a, b) named the spectral method as the optimal spectral decomposition (OSD). With it, several new ocean phenomena have been identified from observational data such as a bi-modal structure of chlorophyll-a with winter/spring (FebruaryMarch) and fall (September-October) blooms in the Black Sea (Chu et al 2005a), fall-winter recurrence of current reversal from westward to eastward on the Texas-Louisiana continental shelf from the current-meter, near-surface drifting buoy (Chu et al 2005b), propagation of long Rossby waves at mid-depths (around 1000 m) in the tropical North Atlantic from the Argo float data (Chu et al 2007), and temporal and spatial variability of the global upper ocean heat content (Chu 2011) from the data of the Global Temperature and Salinity Profile Program (GTSPP, Sun et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Ocean spectral data assimilation without background error covariance matrix

2016

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Predetermination of background error covariance matrix B is challenging in existing ocean data assimilation schemes such as the optimal interpolation (OI). An optimal spectral decomposition (OSD) has been developed to overcome such difficulty without using the B matrix. The basis functions are eigenvectors of the horizontal Laplacian operator, pre-calculated on the base of ocean topography, and independent on any observational data and background fields. Minimization of analysis error variance is achieved by optimal selection of the spectral coefficients. Optimal mode truncation is dependent on the observational data and observational error variance and determined using the steep-descending method. Analytical 2D fields of large and small mesoscale eddies with white Gaussian noises inside a domain with four rigid and curved boundaries are used to demonstrate the capability of the OSD method. The overall error reduction using the OSD is evident in comparison to the OI scheme. Synoptic monthly gridded world ocean temperature, salinity, and absolute geostrophic velocity datasets produced with the OSD method and quality controlled by the NOAA National Centers for Environmental Information (NCEI) are also presented.

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Seasonal variability of the Black Sea chlorophyll-a concentration

Cited by 32 publications

References 29 publications

Spatial gradients in trace metal concentrations in the surface microlayer of the Mediterranean Sea

Spatial gradients in trace metal concentrations in the surface microlayer of the Mediterranean Sea

Temporal variation of surface chlorophyll a in the Romanian near-shore waters

Ocean spectral data assimilation without background error covariance matrix

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