Analyses de données à haute fréquence dans lenvironnement marin côtier: méthodologies Atelier RNSM Haute fréquences Wimereux octobre François Schmitt DR CNRS Laboratoire dOcéanologie et de Géosciences Wimereux

Outline - Methodology: general considerations - MAREL data: spectra/PDF - Valvometry - shot noise - PAR high frequency

Different components (phyto, zoo, nutriments, viruses, … ) Many interactions/coupling (predator-prey, consommation, … ) Hydrodynamic transport (turbulence: large range of scales, mixing, lost of predictability, stochastic aspect, … ) Complexity of aquatic ecosystems -> complex systems, nonlinear, large number of degrees of freedom -> need specific methods for the analysis

Consequences of nonlinearity reality DeterministicModels Mean{Interactions at microscales of quantities} Large scale interactions of Mean{quantities} -> The deterministic models (transport, biogeocemistry, …) have an intrinsic wekness -> to avoid this, try to understand microscale dynamics -> better understand dynamics before averaging

(1) Better understand processes (2) Try to extract some laws (3) Search for universality in patterns, coupling Objectives of high frequency measurements

In maths, two classical ways to deal with a space-time system (1) Eulerian approach (2) Lagrangian approach Fixed position sampling: Eulerian approach. Very useful information about the evolution of the process in time About fixed sampling

MAREL = French acronym for Network of Automatic Measurements in the Littoral Environment Multiparameters (T, S, pH, O2, etc.), fixed point 10 or 20 minutes resolution Objectives: measurement on a scale range including long term in the context of water quality monitoring, estimation of a « normal state » Marine data: Marel system

routine maintenance occasional failures many gaps of variable duration Objectives: find adequate analysis methods adapted for a wide range of scales and large data bases that can work for data possessing many gaps Marine data: Marel system

Approachs borrowed from the field of turbulence: Power spectral density Structure functions (statistical moments for increments) and multifractal modelling PDFs for increments Marel system - methods

Example data from MAREL Honfleur Estuarine buoy Data from 1997 to 2007 From 30,000 to 70,000 data for each series Ex: T, S, DO, pH MAREL Honfleur

T MAREL Honfleur data

Coastal « buoy » Data since 2004 Between and datapoints for each series (at 20 min resolution) Here consider only temperature data MAREL Carnot Boulogne-sur-mer (France) Marine data: Marel Carnot

The data Hourly temperature data from 2004 to About 30,000 data points recorded in Boulogne-sur-mer (North of France) Coastal marine temperature data recorded through the MAREL autonomous monitoring system (IFREMER) at 20 min. resolution, and averaged at 1 hour resolution Atmospheric temperature data recorded hourly by METEO-FRANCE

T The whole series showing 3 annual cycles The data

T A one-year zoom showing the parallel evolution of atmospheric and marine temperature time series

T The data The flux Q=Ta-To showing intermittent large positive and negative values

T The data Dissymetry Some statistics for the flux Q=Ta-To: Q>0: 28% of the values Q<0: 71% of the values Mean of flux for negative values: -2.6 °C Mean of flux for positive values: 2.3 °C Oceanic waters are usually warmer than the atmosphere

T Pdf of the temperature data: shows that atmospheric data have a wider range of values PDF analysis

T Spectral analysis scaling ranges and pikes associated to periodic forcing (daily cycle and tide) Ta and To spectra are similar for time scales larger than 10 days. For smaller time scales, there is mainly a difference in slopes

T modeling Experimental oceanic data (black) compared to modeled ones (red). Quite good superposition

Variabilité des micro-fermetures des huîtres Collaboration avec JC Massabuau, G Durrieu (UMR EPOC) Stage M2 M. de Rosa (2008) Micro-electrodes Données à haute fréquence (1.6 s) de valvométrie. Analyse statistique des micro-fermetures à laide de la théorie des shot noise (bruit de grenaille). Utilisation pour différencier les situations « normales » et perturbées par une micro-algue.

Variabilité des micro-fermetures des huîtres Données Données de valvométrie recueillies en 2007 à une résolution de 1.6s. Environ 900,000 points enregistrés en baie dArcachon Page web: acquisition en continu.

Variabilité des micro-fermetures des huîtres Données Deux exemples de séries temporelles montrant de nombreuses micro- fermetures à haute fréquence. On conste aussi une influence de la marée.

Variabilité des micro-fermetures des huîtres Zoom sur une période de 30 minutes : on constate des micro-fermetures très rapides, avec: - des temps inter-événements aléatoires; - des amplitudes de micro-fermetures aléatoires.

Variabilité des micro-fermetures des huîtres Fermeture à haute fréquence avec des inter-événements et amplitudes aléatoires, suggère lutilisation de la théorie des shot noise nonlinéaires. Aproche développée à lorigine en électronique. Généralisé ensuite en physique nonlinéaire: un système bombardé aléatoirement et de façon discrète par des chocs damplitude aléatoire (Eliazar and Klafter 2006, 2007). Dans ce cadre la première étape de la modélisation est de considérer: - la densité de probabilité des temps entre micro-fermetures; - la densité de probabilité de lamplitude des micro-fermetures. Modélisation à laide de la théorie des « shot noise » - « bruits de grenaille » amplitudesInter-temps

Variabilité des micro-fermetures des huîtres Influence dune pollution micro-algale (pseudo- nitzschia) En situation de pollution: -> beaucoup moins de chocs (trois fois moins) -> influence sur les pdf de temps et amplitude: plus de gros chocs, plus dintertemps longs Inter-temps amplitudes

Variabilité des micro-fermetures des huîtres Spectres dénergie en Fourier invariants déchelle Spectres de la forme E( ) = C avec =1.5 à 1.6, proche de 5/3 (turbulence) Influence probable de la turbulence sur la dynamique de micro-fermeture.

measuring device Alec Electronics

data Too low resolution Seems to have smooth fluctuations

data 21 days chosen, from 17 March 2006 to 12 May second sampling rate About 50,000 to 60,000 datapoints for each series Roof top of Wimereux marine station (north of France, Eatern English channel, latitude 50°43) Variable meteorological conditions (often cloudy…)

data Huge intermittent fluctuations For the clear sky fit, I 0 is unknown: cannot be determined from the data

data

data analysis: detrended data

data analysis: power spectra

Scaling power spectra, slope = No characteristic scale, except change of slope at about frequency h -1 -> time scale 3-12 s Next step, structure function analysis

Publications Published. Dur, G., F. G. Schmitt, S. Souissi: Analysis of high frequency temperature time series in the Seine estuary from the Marel autonomous monitoring buoy, Hydrobiologia, 588, 59-68, 2007 Schmitt FG, G. Dur, S. Souissi, S.B. Zongo, Statistical properties of turbidity, oxygen and pH fluctuations in the Seine river estuary (France), Physica A, 387, , 2008 Zongo, S.B., F.G. Schmitt, A. Lefebvre, M. Repecaud, Observations biogéochimiques des eaux côtières à Boulogne-sur-mer à haute fréquence: les mesures automatiques de la bouée MAREL, in Du naturalisme à lécologie, presses UOF, sous presse. Submitted. Zongo, S.B., F.G. Schmitt, A. Lefebvre, Multiscale dynamics of biogeochemical parameters in the English Channel waters, submitted to Progress in Oceanography Zongo, S.B., F.G. Schmitt, Scaling pH fluctuations in coastal waters of the English Channel: relations with temperature fluctuations, submitted to Journal of Marine Systems. More in preparation…