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Sylvie Brizard Zongo and François G Schmitt

Multiscale distributions of biogeochemical parameters in the English Channel. Sylvie Brizard Zongo and François G Schmitt. Laboratory of Oceanology and Geosciences, UMR LOG 8187, Wimereux Marine Station, University of Lille1, 80 av. Foch, 62930, Wimereux, France. Introduction.

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Sylvie Brizard Zongo and François G Schmitt

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  1. Multiscale distributions of biogeochemical parameters in the English Channel Sylvie Brizard Zongo and François G Schmitt Laboratory of Oceanology and Geosciences, UMR LOG 8187, Wimereux Marine Station, University of Lille1, 80 av. Foch, 62930, Wimereux, France

  2. Introduction Marine coastal area are highly variable on a wide range of time and space scales High variability of biogeochemical parameters High frequency sampling All forcings To define characteristic scales Physics-biology couplings Multi-scale fluctuations

  3. Study site Coastal waters off Boulogne sur mer Western English Channel Wimereux river off Boulogne sur mer English Channel Waters Eastern • A megatidal regime • A cold-temperature shallow sea • Strong tidal currents • The well mixed waters • Shallow water with variable depth English Channel Coastal waters off Roscoff: Escatade station Bay of Seine between the North sea and the English Channel waters Parameters Temperature, dissolved oxygen, pH, salinity, fluorescence, turbidity

  4. Study site and sampling strategy Eastern English Channel waters MAREL program Network of Automatic measurements for Littoral Environment Wimereux Based on the deployment of moored buoys Bay of Seine Buoys equipped with physico-chemical measuring devices Marel Carnot Marel Honfleur Marel carnot: 2004-present Marel Honfleur: 1996-2007 Coastal station Estuarine station

  5. Study site and sampling strategy Marel Carnot station is on the extremity of the embankment MAREL program Automatic monitoring - Records many parameters with high frequency resolution 20 minutes Wimereux Bay of Seine Frequency resolution: 10 minutes We consider here • Honfleur buoy The depth of measurements is 1.5 meters below the surface

  6. Study site and sampling strategy Wimereux river: Multi-parameter TROll 9500 Upstream/Downstream river monitoring data Wimereux Bay of Seine Temperature, Dissolved oxygen, Turbidity and Salinity sensors April-May 2009

  7. The Data temperature Marel carnot: 2004-present Marel Honfleur: 1996-2007 turbidity salinity dissolved and satured oxygen The high frequency Marel curves show high fluctuations and exhibit very intermittent behaviour

  8. The Data temperature time series Evolution of daily maximal and minimal values during 360 days salinity time series turbidity time series fluorescence time series

  9. Scale: 4.5 days from 9 May to 11 May 2009 The Data Wimereux river upstream Large fluctuations at all scales downstream The dissolved oxygen and temperature curves show a smooth periodicity cycle Scale: 6 hours from 13 hours to 19 hours (20 April 2009) 60 minutes portion of the turbidity time series, showing important and apparently irregular fluctuations filling up drain filling up drain

  10. The power spectal We also propose here a methodology, focusing on Fourier space and real space fluctuations and their scale dependences (power-spectra) This approach is borrowed from the field of turbulence • Scale dependences (periodicity frequencies characteristics) • Scaling regimes • A scaling behaviour with power law The spectrum can obey a power law form: an exponent frequency

  11. Scaling analysis 1year Marel carnot: 2004-present 24h 12h 12h The estimation of the parameters power spectra in a loglog plot shows 12h 12h Quite nice scaling regimes Scaling behaviour for scales between 1 year to 20 minutes Turbulence Passive scalar (Obhukov and Corrsin, 1941, 1959; Schmitt and Seuront, 2007) Temperature pH Fluorescence Dissolved oxygen Active scalar (Seuront et al., 1996ab; Lovejoy et al., 2001; Schmitt et al., 2008) 24h, 12h correspond to the diurnal cycle (tide cycle)

  12. Scaling analysis Marel carnot: 2004-present Good superposition indicates that fluorescence, dissolved oxygen and pH have a same temporal distribution which are close to the turbulence power law 33 days We note a discrepancy with temperature

  13. Scaling analysis Marel Honfleur: 1996-2007 The estimation of power spectra in a loglog plot Two scaling regimes 1.7 <Scaling regime (salinity)> 1.6 1.22 <Scaling regime (oxygen)> 1.7 Variability is associated to complex physical Deterministic forcing (tides) Stochastic forcing (river flow, turbulence)

  14. Scaling analysis Marel Honfleur: 1996-2007 Turbidity The strong determistic forcing The power spectra of the stochastic part of the time series Oxygen Turbidity Schmitt et al., 2008 Dissolved oxygen Satured oxygen Oxygen fluctuations are certainly strongly influenced by river flow than Turbidity

  15. Scaling analysis Marel Honfleur: 1996-2007 Pdf-Probability density function Oxygen and turbidity distribution during high (extreme +) and low (extreme -) Seine flow Turbidity

  16. Wimereux river scaling exponents Upstream River flow Huang et al., 2009 , we shown that the wimereux river is more intermittent than the Seine river River flow 3 minutes Downstream Tidal and river flow Turbulence One scaling regime Scaling exponents (downstream) very close to turbulence exponents

  17. Coastal waters: Temperature scaling Deep and shallow well mixed waters of the English channel Coastal waters off Boulogne sur mer (Marel carnot) Eastern English Channel Coastal waters off Roscoff (Escatade site) (data from Pascal Morin) Western English Channel Eastern English Channel: shallow well mixed water ( the high primary production) Western English Channel: deep well mixed water (the low primary production) Marine coastal temperature reveals a universal scaling

  18. Discussion and Conclusion This region is characterised by strong tidal currents and wind mixing: and shows the high variability of biological parameters Huge fluctuations are clearly visible, indicating the intermittency properties of the time series High frequency sampling reveals scaling regimes and periodic forcing with different values of power law for each parameters: • The parameters fluctuations are certainly strongly influenced by turbulence • We note that for these scales, oxygen, fluorescence and pH concentration are a turbulence active scalar with combination of turbulence impact and influences of chemical and biological reactions

  19. Thank you your Attention

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