In the North Pacific Subtropical Gyre, a nearly-annual phytoplankton bloom forms near the subtropical front at ~30° N. Mixed communities of nitrogen-fixing diatom symbioses (diatom-diazotroph associations) increase 10²-10³ fold in these blooms. In July 2008 (31.46˚N 140.49˚W) and August 2009 (25.18 °N 154 °W), two blooms were sampled to determine diatom-diazotroph association species composition, physical, and chemical characteristics of the water column. In both 2008 and 2009, the dominant diatom-diazotroph association was the Hemiaulus hauckii-Richelia intracellularis symbiosis. The 2009 subtropical front bloom was missed; however, another bloom closer to Hawaii was sampled where diatom-diazotroph association abundance was 10-fold lower (10² cells Lˉ¹) than 2008 despite surface chlorophyll a values that were 3 times greater. Both blooms showed substantial changes in phytoplankton size structure with the >10 μm size chlorophyll a fraction increasing from 10 to 40 % in 2008. In the 2009 bloom, the non-symbiotic pennate diatom Mastogloia woodiana numerically dominated (>150,000 cells Lˉ¹) and formed aggregates that resulted in substantially higher % of netplankton chlorophyll a fractions. Summer open ocean blooms from the two years share a common trend of Hemiaulus dominance of the diatom-diazotroph association population and size structure changes. However, non-symbiotic species can dominate the overall bloom, and diatom-diazotroph association species may not be responsible for the chlorophyll a increase. These two years may represent different types of blooms or temporal changes within summer diatom blooms. The increased biomass in the larger-size fraction suggests these blooms are potential sites for carbon export from the surface layer. / text

The radiance distribution provides complete information regarding the geometrical structure of the ambient light field within the ocean. A quantitative study of the radiance field in the dynamic ocean water is presented in this thesis work. The study starts with the development of a novel radiance camera for the measurement of the full spherical radiance distribution at the ocean surface and depth. Nonlinear response functions are designed and advanced radiometric calibrations are developed. The resulting camera measures the radiance distribution in absolute units over an extremely high dynamic range at fast rates. With the newly obtained radiance data, I have examined the fine structure of both the downwelling and upwelling radiance distribution and its variation with depth in optically diverse water types. The fully specified radiance distribution data are used to derive all apparent optical properties and some inherent optical properties including the absorption coefficient. With the camera fixed at shallow depths, I have observed and determined the sea surface wave disturbance of the radiance distribution. It is found that the radiance fluctuates anisotropically with regard to its amplitude and periodicity. Typical spatial structures of the dynamic radiance field are identified and shown relevant to the surface waves and the solar zenith angles. The variability in the radiance field also propagates to the irradiance field; the variability is pronounced in measured irradiance depth profiles in the upper layers of the ocean. The statistics of the irradiance fluctuations along the water depth, including the dominant frequency and coefficient of variation, are derived using wavelet techniques and fitted to novel analytic models. The results from the irradiance depth-profile decomposition are in agreement with theoretical models and other independent measurements. This thesis work represents the first attempt to quantify the full light field and its variability in dynamic ocean waters and is of significant relevance to many other optics-related applications.

Thesis (Ph. D.)--Duke University, 2007.

Thesis (M.S.)--Naval Postgraduate School, 1984. / "March 1984." "N0001484 WR24001"--P. 1. Includes bibliographical references (leaves 144-148).

La télédétection de la couleur de l'océan représente un outil adapté à l'observation du phytoplancton avec des résolutions spatio-temporelles élevées et pouvant être adaptées à chaque cas d'étude. Plusieurs méthodes ont été développées ces dernières années afin de permettre la distinction de différents groupes de phytoplancton en utilisant les données des capteurs de la couleur de l'océan. Dans le cadre de cette thèse, on présente une nouvelle approche, appelée PHYSAT-SOM, qui se base sur l'application d'un algorithme de classification automatique non supervisée (SOM ou Self-Organizing Maps) à l'extraction de différentes formes et amplitudes de spectres d'anomalies de luminances (Ra ou Radiance Anomaly). Cette anomalie spectrale a été définie par Alvain et al. (2005), lors du développement de la méthode PHYSAT et il est actuellement admis que sa variabilité est reliée à celle de la composition des communautés phytoplanctoniques. L'utilisation des SOM vise à améliorer la caractérisation de la variabilité des Ra en termes de forme et amplitude ainsi que l'expansion du potentiel de leur utilisation à de grandes bases de données in situ de pigments. En considérant un même jeu de données de spectres de Ra, une comparaison entre la précédente version de PHYSAT et la nouvelle approche, basée sur SOM a montré qu'il est maintenant possible de couvrir toute la variabilité spectrale des Ra. Ceci n'était pas le cas avec l'ancienne approche du fait de l'utilisation de seuils, définis dans le but d'éviter les chevauchements entre les signatures spectrales des différents groupes de phytoplancton. La méthode basée sur SOM est pertinente pour caractériser une grande variété de spectres de Ra, de par sa capacité à gérer de grandes quantités de données et de sa fiabilité statistique. La première approche aurait pu, de ce fait, introduire des biais potentiels et donc, les possibilités de son extension à de plus grandes bases de données in situ étaient relativement restreintes. Par la suite, SOM a été utilisé pour classer les spectres de Ra fréquemment observés à l'échelle globale. Ces spectres ont ensuite été empiriquement reliés à différents groupes de phytoplancton, identifiés à partir de données in situ de pigments. Cette classification a été appliquée aux archives satellite du capteur SeaWiFS, permettant l'étude de la distribution globale de chaque groupe. Grâce à sa capacité à caractériser un large éventail de spectres de Ra et de gérer une plus grande base de données in situ, l'outil SOM permet de classer un nombre plus élevé de pixels (2x plus) que la précédente approche de PHYSAT. En outre, différentes signatures spectrales de Ra ont été associées aux diatomées. Ces signatures sont situées dans divers environnements où les propriétés optiques inhérentes affectant les spectres de Ra sont susceptibles d'être significativement différentes. Par ailleurs, les floraisons de diatomées dans certaines conditions sont plus clairement visibles avec la nouvelle méthode. La méthode PHYSAT-SOM offre ainsi plusieurs perspectives afin d'aller plus loin dans l'utilisation des données de la couleur de l'océan pour la détection des groupes de phytoplancton. On peut citer l'exemple d'une application future dans les eaux du Cas 2, moyennant une approche de normalisation adéquate du signal de luminances. Une étude préliminaire en Manche et Mer du Nord est présentée dans le dernier chapitre, montrant qu'il sera possible d'utiliser PHYSAT-SOM dans cet environnement optiquement complexe. / Remote sensing of ocean color is a powerful tool for monitoring phytoplankton in the ocean with a high spatial and temporal resolution. Several methods were developed in the past years for detecting phytoplankton functional types from satellite observations. In this thesis, we present an automatic classification method, based on a neural network clustering algorithm, in order to classify the anomalies of water leaving radiances spectra (Ra), introduced in the PHYSAT method by Alvain et al. (2005) and analyze their variability at the global scale. The use of an unsupervised classification aims at improving the characterization of the spectral variability of Ra in terms of shape and amplitude as well as the expansion of its potential use to larger in situ datasets for global phytoplankton remote sensing. The Self-Organizing Map Algorithm (SOM) aggregates similar spectra into a reduced set of pertinent groups, allowing the characterization of the Ra variability, which is known to be linked with the phytoplankton community composition. Based on the same sample of Ra spectra, a comparison between the previous version of PHYSAT and the new one using SOM shows that is now possible to take into consideration all the types of spectra. This was not possible with the previous approach, based on thresholds, defined in order to avoid overlaps between the spectral signatures of each phytoplankton group. The SOM-based method is relevant for characterizing a wide variety of Ra spectra through its ability to handle large amounts of data, in addition to its statistical reliability compared to the previous PHYSAT. The former approach might have introduced potential biases and thus, its extension to larger databases was very restricted. In a second step, some new Ra spectra have been related to phytoplankton groups using collocated field pigments inventories from a large in situ database. Phytoplankton groups were identified based on biomarker pigments ratios thresholds taken from the literature. SOM was then applied to the global daily SeaWiFS imagery archive between 1997 and 2010. Global distributions of major phytoplankton groups were analyzed and validated against in situ data. Thanks to its ability to capture a wide range of spectra and to manage a larger in situ pigment dataset, the neural network tool allows to classify a much higher number of pixels (2 times more) than the previous PHYSAT method for the five phytoplankton groups taken into account in this study (Synechococcus-Like-Cyanobacteria, diatoms, Prochloroccus, Nanoeucaryots and Phaeocystis-like). In addition, different Ra spectral signatures have been associated to diatoms. These signatures are located in various environments where the inherent optical properties affecting the Ra spectra are likely to be significantly different. Local phenomena such as diatoms blooms in the upwelling regions or during climatic events(i.e. La Nina) are more clearly visible with the new method. The PHYSAT-SOM method provides several perspectives concerning the use of the ocean color remote sensing data for phytoplankton group identification, such as, the potential application of the method in Case 2 waters, using an appropriate nLw signal normalization approach. A preliminary case study in the English Channel and North Sea waters is presented in the last chapter of the thesis, showing the possibility of a future use of PHYSAT-SOM in these optically complex waters.

This study analyses the spatial and temporal variability of chlorophyll-a in Chesapeake Bay; assesses the performance of Ocean Color 3M (OC3M) algorithm; and develops a novel algorithm to estimate chlorophyll-a for coastal shallow water. The OC3M algorithm yields an accurate estimate of chlorophyll-a concentration for deep ocean water (RMSE=0.016), but it failed to perform well in the coastal water system (RMSE=23.17) of Chesapeake Bay. A novel algorithm was developed which utilizes green and red bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The novel algorithm derived the chlorophyll-a concentration more accurately in Chesapeake Bay (RMSE=4.20) than the OC3M algorithm. The study indicated that the algorithm that uses red bands could improve the satellite estimation of chlorophyll-a in the coastal water system by reducing the noise associated with bottom reflectance and colored dissolved organic matter (CDOM)

Using a combination of hydrographic data and the trajectories and profiles of isobaric floats, this dissertation evaluates the connections between remote regions in the North Atlantic. First, I establish that the production and advection of the North Atlantic Subtropical Mode Water (STMW) introduces spatial and temporal variability in the subsurface nutrient reservoir of the subtropical gyre. As the mode water is formed, its nutrients are depleted by biological utilization. When the depleted water mass is exported to the gyre, it injects a wedge of low-nutrient water into the upper layers of the ocean. Contrary to intuition, cold winters that promote deep convective mixing and vigorous mode water formation may diminish downstream primary productivity by altering the subsurface delivery of nutrients. Next, the source of elevated nutrient concentrations in the Gulf Stream is assessed. The historical hydrographic data suggest that imported water advected into the Gulf Stream via the tropics supplies an important source of nutrients to the Gulf Stream. Because the high nutrients are likely imported from the tropics, diapycnal mixing need not be invoked to explain the Gulf Stream's high nutrient concentrations, as had been previously hypothesized. Furthermore, nutrients do not increase along the length of the Stream, as would be expected with strong diapycnal mixing.Finally, profiling float data are used to investigate how the Labrador Sea Water enters the Deep Western Boundary Current, one of the primary pathways by which it exits the subpolar gyre. With the trajectories and profiles of an extensive array of P-ALACE floats I evaluate three processes for their role in the entry of Labrador Sea Water in the Deep Western Boundary Current (DWBC): 1) LSW is formed directly in the DWBC, 2) Eddies flux LSW laterally from the interior Labrador Sea to the DWBC, and 3) A horizontally divergent mean flow advects LSW from the interior to the DWBC. Each of the three processes has the potential to remove heat from the boundary current, and both the formation of LSW directly in the boundary current and the eddy heat flux are possible sources of interannual variability in the exported LSW product. / Dissertation

Harmful Algal Blooms (HABs) in the Gulf of Mexico (GOM) are natural phenomena that can have negative impacts on marine ecosystems on which human health and the economy of some Gulf States depends. Many of the HABs in the GOM are dominated by the toxic dinoflagellate Karenia brevis. Non-toxic phytoplankton taxa such as Scrippsiella sp. also form intense blooms off the Mexican coast that result in massive fish mortality and economic losses, particularly as they may lead to anoxia. The main objectives of this dissertation were to (1) evaluate and improve the techniques developed for detection of Karenia spp. blooms on the West Florida Shelf (WFS) using satellite remote sensing methods, (2) test the use of these methods for waters in the GOM, and (3) use the output of these techniques to better understand the dynamics and evolution of Karenia spp. blooms in the WFS and off Mexico. The first chapter of this dissertation examines the performance of several Karenia HABs detection techniques using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images and historical ground truth observations collected on the WFS from August 2002 to December 2011. A total of 2323 in situ samples collected by the Florida Fish and Wildlife Research Institute to test for Karenia spp. matched pixels with valid ocean color satellite observations over this period. This dataset was used to systematically optimize variables and coefficients used in five published HAB detection methods. Each technique was tested using a set of metrics that included the F-Measure (FM). Before optimization, the average FM for all techniques was 0.47. After optimization, the average FM increased to 0.59, and false positives decreased ~50%. The addition of a Fluorescence Line Height (FLH) criterion improved the performance of every method. A new practical method was developed using a combination of FLH and Remote Sensing Reflectance at 555 nm (Rrs555-FLH). The new method resulted in an FM of 0.62 and 3% false negatives, similar to those from more complex techniques. The first chapter concludes with a series of recommendations on how to improve the detection techniques and how to take these results a step further into a Gulf wide observing systems for HABs. In chapter two, ocean color techniques were used to examine the extension, evolution and displacement of four Karenia spp. events that occurred in the WFS between 2004 and 2011. Blooms were identified in the imagery using the new Rrs-FLH method and validated using in situ phytoplankton cell counts. The spatial extension of each event was followed in time by delineating the blooms. In 2004 and 2005, the WFS was affected by a series of hurricanes that led to high river discharge and intense sediment resuspension events. Both processes had an impact on HAB occurrence. For example, I tracked a Karenia spp. bloom found in late December 2004 approximately 40-80 km offshore Saint Petersburg, which then expanded reaching an extension of >8000 km2 in February 2005. The bloom weakened in spring 2005 and intensified again in summer reaching >42,000 km2 after the passage of hurricane Katrina in August 2005. This bloom covered the WFS from Charlotte Harbor to the Florida Panhandle. Two other cases were studied in the WFS. The results of the Hybrid Coordinate Ocean Model from the U.S. Navy aid understanding the dispersal of the blooms. During fall 2011, three field campaigns to study HABs in Mexico were conducted to do an analysis of optical properties and explore the possibility of using ocean color techniques to distinguish between the main phytoplankton blooms in that region. Three main bloom scenarios were observed in the Campeche Bank region: massive diatom blooms, blooms dominated by Scrippsiella spp., and Karenia spp. blooms. The normalized specific phytoplankton absorption spectra were found to be different for Karenia spp. and Scrippsiella sp. blooms. A new technique that combines phytoplankton absorption derived from MODIS data and the new technique developed in Chapter One showed potential for a detection technique that can distinguish between Karenia and Scrippsiella blooms. Additional work is needed to improve the new technique developed for Mexican waters, but results show potential for detection techniques that can be used Gulf-wide. This will help better understand the dynamic and possible connectivity of phytoplankton blooms in the GOM.

Les travaux présentés dans cette thèse concernent la paramétrisation de la distribution verticale de la biomasse et de la structure des communautés phytoplanctoniques dans l’océan global. Nous avons d’abord développé une méthode neuronale de calibration de la fluorescence en concentration en chlorophylle a ([Chl]) associée à la biomasse phytoplanctonique totale et à trois classes de taille de phytoplancton. Cette méthode, FLAVOR, a été entrainée et validée à l’aide une base de données de ~900 profils de fluorescence et de pigments mesurés pat HPLC. Une base de données globale de ~49000 profils de fluorescence a ensuite été assemblée et calibrée en termes de biomasse chlorophyllienne et composition du phytoplancton. Ce travail représente une première étape vers une vision tridimensionnelle de la biomasse phytoplanctonique. Nous avons ensuite développé deux réseaux de neurones (SOCA) pour estimer la distribution verticale de deux paramètres bio-optiques, [Chl] et le coefficient de rétrodiffusion. Ces réseaux de neurones requièrent comme données d’entrée des données satellites de couleur de l’eau co-localisées avec un profil hydrologique collecté par un flotteur Argo. Ils ont été entrainés et validés avec une base de données globale composée de ~5 000 profils de propriétés bio-optiques et hydrologiques acquises par des flotteurs Bio-Argo. Les bases de données utilisées pour développer les méthodes FLAVOR et SOCA proviennent de régions océaniques représentatives de l’océan global, permettant ainsi l’application de ces méthodes à la majorité des eaux océaniques. Finalement, nous avons mené une étude focalisée sur l’Atlantique Nord qui exploite les outils développés. Les champs tridimensionnels de biomasse obtenus, couplés à un modèle bio-optique de production primaire, permettent d’étudier les cycles saisonniers de la distribution verticale de la biomasse phytoplanctonique et de la production primaire dans différentes bio-régions de l’Atlantique Nord. / This PhD work focuses on the parameterization of the vertical distribution of phytoplankton biomass and community structure in the global open ocean. First we have developed a neural network-based method for the calibration of the fluorescence in chlorophyll a concentration [Chl] associated with the total phytoplankton biomass and with three phytoplankton size classes. This method, (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm), was trained and validated using a database of ~900 concomitant fluorescence and HPLC-determined pigment profiles. A global database comprising ~49 000 fluorescence profiles was assembled and calibrated with FLAVOR. The resulting database represents a first step towards a global three-dimensional view of phytoplankton biomass and community composition. Second, two neural networks (SOCA for Satellite Ocean Color and Argo data to infer vertical distribution of bio-optical properties) were developed to infer the vertical distribution of two bio-optical proxies of the phytoplankton biomass, [Chl] and the particulate backscattering coefficient, using as input satellite-derived products matched up with a hydrological Argo profile. The SOCA methods were trained and validated using a global database of ~5 000 profiles of bio-optical and hydrological properties collected from Bio-Argo floats with concomitant satellite products. The database used to develop FLAVOR and SOCA originates from various oceanic regions largely representative of the global ocean, making the methods applicable to most oceanic waters. Finally, we proposed a study dedicated to the North Atlantic where the tools developed in this thesis are used in conjunction with a bio-optical primary production model. This allows us to characterize the seasonal cycle of the vertical distribution of the phytoplankton biomass and primary production in various bio-regions of the North Atlantic.

Depuis quelques années, les observations spatiales des traceurs, comme la température de surface de l'océan (SST) ou la couleur de l'océan, ont révélé la présence de filaments à sous-mésoéchelle, qui ne peuvent être détectées par les satellites altimétriques. Ce travail de thèse explore la possibilité d'utiliser les informations dynamiques contenues dans les images traceur haute résolution pour compléter l'estimation de la dynamique océanique de surface effectuée par les satellites altimétriques. Pour ce faire, la méthode d'inversion développée est inspirée de l'assimilation de données images. A l'aide d'une fonction coût, on mesure la distance entre une image du flot dynamique et l'image des structures présentes sur le traceur. On a choisi pour cette étude d'utiliser le FSLE (Finite-Size Lyapunov Exponents) comme proxy image de la dynamique. Cette méthode est testée avec succès sur plusieurs cas test d'observations spatiales. Un modèle de processus couplé physique-biogéochimie ainsi qu'un modèle réaliste de la mer des Salomon sont utilisés pour estimer l'erreur associée à la méthode d'inversion et la pertinence de la correction effectuée. L'utilisation conjointe d'images traceurs et de données altimétriques présente un fort intérêt pour le contrôle de la circulation océanique. / High resolution sensors of tracers such as the Sea Surface Temperature or the Ocean Color reveal small structures at the submesoscale, which are not seen by altimetry. Therefore, this thesis explores the feasibility of using tracer information at the submesoscales to complement the control of ocean dynamic fields that emerge from altimeter data analysis at larger scales. To do so, an image data assimilation strategy (i.e. inversion of images) is developed in which a cost-function is built that minimizes the misfits between image of submesoscale flow structure and tracer images. In the present work, we have chosen as an image of submesoscale flow structure the Finite-Size Lyapunov Exponents (FSLE). This method has been successfully tested on several areas using tracer and altimetric observations from space A high resolution physico-biogeochemical coupled model of process and a high resolution realistic model of the Solomon sea have been used to assess the error associated with the inversion and the efficiency of the correction on the oceanic circulation. These results show the benefits of the joint use of tracer image and altimetric data for the control of ocean circulations.