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Surface gravity waves alter the turbulence of the bottom atmosphere and the upper ocean. Accordingly, they can affect momentum flux, heat fluxes, gas exchange and atmospheric mixing. However, in most state-of-the-art Earth System Models (ESMs), surface wave influences are not fully considered or even included. Here, applying surface wave influences into ESMs is investigated from different aspects. Tuning parameterisations for including instantaneous wave influences has difficulties to capture wave influences. Increasing the horizontal resolution of models intensifies storm simulations for both atmosphere-wave coupled (considering the influence of instantaneous wave-induced stress) and stand-alone atmospheric models. However, coupled models are more sensitive to the horizontal resolution than stand-alone atmospheric models. Under high winds, wave states have a big impact on the sea spray generation. Introducing a wave-state-dependent sea spray generation function and Charnock coefficient into a wind stress parameterisation improves the model performance concerning wind speed (intensifies storms). Adding sea spray impact on heat fluxes improves the simulation results of air temperature. Adding sea spray impact both on the wind stress and heat fluxes results in better model performance on wind speed and air temperature while compared to adding only one wave influence. Swell impact on atmospheric turbulence closure schemes should be taken into account through three terms: the atmospheric mixing length scale, the swell-induced momentum flux at the surface, and the profile of swell-induced momentum flux. Introducing the swell impact on the three terms into turbulence closure schemes shows a better performance than introducing only one of the influences. Considering all surface wave impacts on the upper-ocean turbulence (wave breaking, Stokes drift interaction with the Coriolis force, Langmuir circulation, and stirring by non-breaking waves), rather than just one effect, significantly improves model performance. The non-breaking-wave-induced mixing and Langmuir circulation are the most important terms when considering the impact of waves on upper-ocean mixing. Accurate climate simulations from ESMs are very important references for social and biological systems to adapt the climate change. Comparing simulation results with measurements shows that adding surface wave influences improves model performance. Thus, an accurate description of all important wave impact processes should be correctly represented in ESMs, which are important tools to describe climate and weather. Reducing the uncertainties of simulation results from ESMs through introducing surface gravity wave influences is necessary.

Solar energy is incident on the earth's surface in both short-wave and long-wave parts of the spectrum. The short-wave part of the spectrum is of special interest to oceanographers since the vertical distribution of temperature in the top layer of the ocean is mostly determined by the vertical attenuation of short-wave radiation. There are numerous studies regarding the temperature evolution as a function of time (see Chapter 2 for details). The diurnal and seasonal variation of the heat content (and hence temperature) of the ocean is explored in this thesis. The basis for such heat budget simulation lies in the fact that the heat budget is the primary driver of ocean currents (maybe secondary to wind effects) and these circulation features affect the biological and chemical effects of that region. The vertical attenuation of light (classified to be in the 300-700 nm range) in the top layer of the ocean has been parameterized by several authors. Simpson and Dickey (1981) in their paper have listed the various attenuation schemes in use till then. This includes a single-exponential form, a bimodal exponential form, and a spectral decomposition into nine spectral bands, each with their specific exponential functions with depth. The effects of vertical light attenuation have been investigated by integrating the light models into a 1D and a 3D turbulence closure model. The main part of the thesis is the inclusion of a bottom effect in the shallow waters. Bottom serves two purposes, it reflects some light based on its albedo and it radiates the rest of the light as heat. 1-D simulation including bottom effects clearly indicates the effect of light on the temperature profile and also the corresponding effect on salinity profiles. An extension of the study includes a 3D simulation of the heat budget and the associated circulation and hydrodynamics. Intense heating due to the bottom leads to the formation of hyper-saline waters that percolate down to depths of 50 m in the summer. Such plumes have been simulated by using a 3D numerical ocean model and it is consistent with observations from the Bahamas banks.

La caractérisation des dynamiques de sous-mésoéchelle (<10km) à la surface de l'océan et leurs impacts sur les processus océaniques globaux sont des enjeux scientifiques majeurs. L'imagerie satellitaire est un outil essentiel dans ce contexte, qui présente toutefois des limitations liées aux instruments de télédétection. Dans le cas des images de température de surface des océans (SST), les mesures satellitaires des structures océaniques sont limitées par la résolution grossière des capteurs micro-ondes (~50km) d'une part, et par la sensibilité aux conditions climatiques (e.g., couverture nuageuse) des instruments de mesure infrarouge haute-résolution. Dans cette thèse, nous nous intéressons à l'analyse, la modélisation et la reconstruction des structures turbulentes haute-résolution capturées par imagerie satellitaire de SST, et proposons quatre contributions principales. Dans un premier temps, nous développons une méthode de filtrage conjointe Fourier-ondelettes pour le prétraitement d'artefacts géométriques dans les observations satellitaires infrarouges. Dans un deuxième temps, nous nous focalisons sur la caractérisation de la variabilité géométrique de champs de température de surface (SST) en utilisant des modèles de marches aléatoires appliqués aux lignes de niveaux. En particulier, nous considérons des processus aléatoires de type schramm Loewner (SLE). Nous nous intéressons ensuite à la modélisation stochastique des variabilités inter-échelles de champs de SST. Des modèles stochastiques de textures multivariées sont introduits. Ces modèles permettent de reproduire des propriétés statistiques et spectrales similaires à celles des données ayant servi à les calibrer. Nous développons ensuite des méthodes de super-résolution de champs de SST conditionnellement à une observation basse-résolution. Nous utilisons des modèles multivariés de textures formulés dans le domaine des ondelettes, en exploitant l'apprentissage d'à priori statistiques (i.e., covariances et covariances croisées) des différentes sous-bandes à partir d'images haute-résolution. Des contraintes supplémentaires imposées sur la phase de Fourier des différentes sous-bandes simulées permettent la reconstruction de structures géométriques marquées tels que les fronts. Nous démontrons la pertinence de la méthode proposée sur des images satellitaires de SST obtenues à partir du capteur Modis/Aqua. / The characterization of sub-mesoscale dynamics (<10 km) in the ocean surface and their impact on global ocean processes are major scientific issues. Satellite imagery is an essential tool within this framework. However, the use of remote sensing techniques still raise challenging. For instance, regarding Sea Surface Temperature (SST) images, satellite measurements of oceanic structures are limited by the coarse resolution of microwave sensors (~50km) on one hand, and by sensitivity to climatic conditions (eg., Cloud cover) of high-resolution infrared instruments on the other hand. In this thesis, we are interested in analysis, modeling and reconstruction of high-resolution turbulent structures captured by satellite SST imagery. In this context, we propose four main contributions. First, we develop a joint Fourier-Wavelet filtering method for the pre-processing of geometrical noises in satellite-based infrared observations, namely the striping noises. Secondly, we focus on the characterization of the geometric variability of sea surface temperature (SST) fields using random walk models applied to SST isolines. In particular, we consider the class of Schramm Loewner evolution curves (SLE). We then focus on the stochastic modeling of the cross-scale variabilities of SST fields. Stochastic multivariate texture-based models are introduced. These models are designed to reproduce several statistics and spectral properties that are observed on the data that are used to calibrate the model. We then develop our framework for stochastic super-resolution of SST fields conditionally to low-resolution observations. We use multivariate texture-based models formulated in the wavelet domain. These models exploit the formulation of statistical and spectral priors (i.e., covariances and cross-covariances) on wavelet subbands. These priors are directly learned from exemplar high-resolution images. Additional constraints imposed on the Fourier-phase of the different simulated subbands allow the reconstruction of coherent geometric structures such as the edge information. Our method is tested and validated using infrared high-resolution satellite SST images provided by Aqua Modis sensor.