Reconstruire des champs géophysiques à partir d'observations bruitées et partielles est un problème classique bien étudié dans la littérature. L'assimilation de données est une méthode populaire pour aborder ce problème, et se fait par l'utilisation de techniques classiques, comme le filtrage de Kalman d’ensemble ou des filtres particulaires qui procèdent à une évaluation online du modèle physique afin de fournir une prévision de l'état. La performance de l'assimilation de données dépend alors fortement de du modèle physique. En revanche, la quantité de données d'observation et de simulation a augmenté rapidement au cours des dernières années. Cette thèse traite l'assimilation de données d'une manière data-driven et ce, sans avoir accès aux équations explicites du modèle. Nous avons développé et évalué l'assimilation des données par analogues (AnDA), qui combine la méthode des analogues et des méthodes de filtrage stochastiques (filtres Kalman, filtres à particules, chaînes de Markov cachées). Des applications aux modèles chaotiques simplifiés et à des études de cas de télédétection réelle (température de surface de lamer, anomalies du niveau de la mer), nous démontrons la pertinence d'AnDA pour l'interpolation de données manquantes des systèmes dynamiques non linéaires et à haute dimension à partir d'observations irrégulières et bruyantes.Motivé par l'essor du machine learning récemment, la dernière partie de cette thèse est consacrée à l'élaboration de modèles deep learning pour la détection et de tourbillons océaniques à partir de données de sources multiples et/ou multi temporelles (ex: SST-SSH), l'objectif général étant de surpasser les approches dites expertes. / Reconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches.

Porites lobata is an important reef building coral in the tropical eastern Pacific and the dominant Porites species in the Galápagos archipelago. Following the 1982-83 El Niño-Southern Oscillation the Galápagos Islands experienced 97-99% coral mortality, leaving many areas throughout the archipelago denuded of corals. Because very few long term assessments have been conducted on the growth and resilience of P. lobata to natural disturbances in the Galápagos Islands (Glynn et al., 2001; Glynn et al., 2009), benthic surveys were performed on a uniquely dense aggregation of P. lobata colonies at Devil’s Crown, Floreana Island between 1993 and 2011. Annual changes in live tissue area were calculated for the majority of the population (n=17) using Coral Point Count with Excel extensions (CPCe 3.6) software to determine growth and recovery trends for this aggregation. Total live tissue area (n=10) increased from 1993 to 2011, however due to high interannual variability this increase was not significant. Within this overall pattern, a general trend of decline was observed in live tissue cover from 1993 to 2000, with increases in tissue area observed from 2000 to 2011. Severe bleaching (85-100%) was observed during the 1998 survey, followed by 42% tissue loss (n=10), coinciding with sea water warming associated with the very strong 1997-1998 El Niño-Southern Oscillation event. Subsequent regrowth of coral tissue was observed during the 2001 survey with continued recovery through 2009. Multiple comparison testing revealed a significant difference between the impacted state (1999) and the recovered state (2009), (p = 0.002, Dunn’s method, n=17), suggesting this aggregation required a period of ten years to recover from this disturbance. During this recovery period the moderately strong 2007-2008 La Niña, with accompanying stressful low temperatures, occurred but did not interrupt tissue regrowth. Warmer than average sea surface temperatures occurred during the warm months from 2008 to 2011, during which time a cool period occurred from 2010 to 2011. While the magnitude and duration of temperature anomalies during warming were not as great as those observed during the 1997-98 ENSO, low temperatures observed during the cool period were similar to those experienced throughout the 2007-08 La Niña. During this time total live tissue cover was reduced by 19% (n=10); however it is unknown whether this was due to warming or the following cool period. Based on results from the 1997-98 El Niño and 2007-08 La Niña, this reduction in live tissue was most likely caused by elevated sea surface temperatures. Data on the growth and resilience of P. lobata populations at Devil’s Crown will be used for conservation and management of this important resource.

Attempts to quantify the effects of ocean acidification and warming (OAW) on scleractinian corals provide a growing body of response measurements. However, placing empirical results into an ecological context is challenging, owing to variations that reflect both natural heterogeneity and scientific bias. This study addresses the heterogeneity of climate change induced changes in coral recruitment and calcification. To discern scientific bias and identify drivers of the remaining heterogeneity, 100 publications were analyzed using a combination of weighted mixed effects meta-regression and factorial effect size meta‑analysis. A linear model was applied to quantify the variation caused by differing stress levels across studies. The least squares predictions were then used to standardize individual study outcomes and effect size meta-analysis was performed on original and standardized outcomes separately. On average, increased temperature significantly reduces larval survival, while ocean acidification impedes settlement and calcification. Coral resistance to OAW is likely governed by biological traits (genera and life cycle stage), environmental factors (abiotic variability) and experimental design (feeding regime, stressor magnitude, and exposure duration). Linear models suggest that calcification rates are driven by carbonate and bicarbonate concentrations, which act additively with warming. Standardizing outcomes to linear model predictions proved useful in discerning strong sources of scientific bias. The approach used in this study can improve modelling projections and inform policy and management on changes in coral community structure associated with the expected future intensification of OAW.

Reproductive success in loggerhead (Caretta caretta) sea turtles is strongly dependent on the effective placement and internal conditions of their nests. Embryos rely on optimal incubation conditions for proper development and growth, which determines how many hatchlings will emerge from the nest. The internal microclimate of each nest is delicately balanced and can be easily influenced by external environmental conditions. This study was designed to examine several environmental variables and determine their effects on sea turtle nesting numbers, hatching success, and incubation conditions in Broward County Florida. Over a span of 25 years (1991-2015), the Broward County Sea Turtle Conservation Program has collected data on each sea turtle nest laid in Broward County. This data was analyzed and plotted to visualize nesting and hatching trends, and regressions were fitted to make comparisons to historic air temperature, sea surface temperature, precipitation, and lunar illumination data. These regressions were tested for significance, and each environmental variable was found to have varying levels of impact on sea turtle nesting and hatching behavior. Of the environmental variables considered in this study, analyses suggest that sea turtles are most responsive to temperature, with sea surface temperature serving as the best proxy for predicting nesting behaviors. Air temperature over the incubation period was found to be the best indicator for hatch success percentage. Air temperature, sea surface temperature, and precipitation averages all significantly affected the length of the incubation period. The regression models created in this study could be used to examine the interactions between climatic variables, and to indicate what impacts can be expected by these various environmental factors. This information could be used to estimate the future effects of climate change on sea turtle reproduction, and to predict general reproductive success and future population trends.

No description available.

In this thesis we examine intra-seasonal oscillations (ISO) in the aqua-planet setup of the Community Atmospheric Model (CAM) version 5.1, mainly based on July and January climatological sea surface temperature (SST). We investigate mainly two questions -what should be the SST distribution for the existence of (a) northward moving ISO in summer, and (b) eastward moving MJO-like modes in winter. In the first part of the thesis we discuss the northward propagation. A series of experiments were performed with zonally symmetric and asymmetric SST distributions. The basic lower boundary condition is specified from zonally averaged observed July and January SST. The zonally symmetric July SST experiment produced an inter tropical convergence zone (ITCZ) on both sides of the equator. Poleward movement is not clear, and it is confined to the region between the double ITCZ. In July, the Bay of Bengal (BOB) and West Pacific SST is high compared to the rest of the northern tropics. When we impose a zonally asymmetric SST structure with warm SST spanning about 80 of longitude, the model shows a monsoon-like circulation, and some northward propagating convective events. Analysis of these events shows that two adjacent cells with cyclonic and anticyclonic vorticity are created over the warm SST anomaly and to the west. The propagation occurs due to the convective region drawn north in the convergence zone between these vortices. Zonally propagating Madden-Julian oscillations (MJO) are discussed in the second part of the thesis. All the experiments in this part are based on the zonally symmetric SST. The zonally symmetric January SST configuration gives an MJO-like mode, with zonal wave number 1 and a period of 40-90 days. The SST structure has a nearly meridionally symmetric structure, with local SST maxima on either side of the equator, and a small dip in the equatorial region. If we replace this dip with an SST maximum, the time-scale of MJO becomes significantly smaller (20-40 days). The implication is that an SST maximum in the equatorial region reduces the strength of MJO, and a flat SST profile in the equatorial region is required for more energetic of MJO. This result was tested and found to be valid in a series of further experiments.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI)

Les instruments, satellites ou systèmes aéroportés, actuellement utilisés pour la détection et la caractérisation d'hydrocarbure sur la mer sont basés sur des moyens optiques ou radars. Ces moyens présentent une performance dégradée due à une fréquence encore trop importante de fausses alarmes ou à un temps de traitement des données trop conséquent. Les méthodes de détection, d'identification et de quantification des fuites d'hydrocarbures offshores peuvent donc être améliorées en associant robustesse et réactivité. Cette amélioration suppose une compréhension approfondie des phénomènes océanographiques et électromagnétiques à l'œuvre dans cette scène particulière. La thèse s'appuie sur des données regroupant des images optiques et SAR aéroportées ou satellites ainsi que des mesures réalisées en laboratoire. Ce jeu de données permet de vérifier la cohérence des résultats obtenus par modélisation. L'objectif de la thèse est de distinguer une surface de mer polluée d'une surface de mer propre à l'aide de la signature électromagnétique de la surface totale puis de détailler le type et la quantité d'hydrocarbure présent. La thèse se divise en deux domaines, à savoir modélisation océanographique et modélisation électromagnétique. La modélisation océanographique intègre la simulation de la surface rugueuse imitant une surface de mer propre, et polluée. Cette surface de mer doit être générée sur une superficie importante et doit conserver une résolution restituant les petites vagues avec un temps de génération minimal. La partie électromagnétique est centrée sur les modèles asymptotiques de diffusion des ondes électromagnétiques par une interface rugueuse. Ces modèles sont adaptés au contexte de la thèse, complexité de la scène et rapidité du traitement, mais nécessitent plusieurs hypothèses pour être appliqués. / Satellites or airborne systems currently used for the detection and characterization of oil slicks on sea surface are based on optical or radar means. These means have a lack of performance due to a too high frequency of false alarms or to an excessively long data processing time. The methods for detecting, identifying and quantifying offshore pollutant can therefore be improved by combining robustness and reactivity. This improvement implies an in-depth understanding of the oceanographic and electromagnetic phenomena at work in this particular scene. The thesis is based on data gathering aerial and satellite images and SAR as well as measurements carried out in laboratory. This dataset makes it possible to check the consistency of the results obtained by modeling. The objective of the thesis is to distinguish a polluted sea surface from a clean sea surface using the electromagnetic signature of the total surface and then to detail the type and quantity of pollutant. The thesis is divided into two domains, namely oceanographic modeling and electromagnetic modeling. Oceanographic modeling integrates the simulation of the rough surface imitating a clean or polluted sea surface. This sea surface must be generated over a large area with a thin resolution. The electromagnetic part is centered on the asymptotic models for the electromagnetic waves diffraction by a rough interface. These models are adapted to the context of the thesis, the complexity of the scene and the speed of processing, but require several hypotheses to be applied.

No description available.