Temporal Variability in Ocean Mesoscale and Submesoscale Turbulence

Turbulence in the Ocean is characterized by a highly nonlinear interaction of waves, eddies and jets drawing energy from instabilities of the large-scale flow and spans a wide range of scales. Turbulent mesoscale eddies are well known as the dominant reservoir of kinetic energy in the ocean and are suspected to contribute significantly to the transport of heat, momentum, and chemical tracers, thereby playing an important role in the global climate system. The intermediate-scale flow structures (i.e. the submesoscale), often manifest as fronts, filaments, wakes and coherent vortices and pose considerable theoretical challenges due to the breakdown of balanced dynamics and the overlapping of scales with inertia-gravity waves. The full role of these submesoscale motions in transport and mixing, therefore remains unknown. This thesis is divided into three chapters focusing on different aspects of turbulence in the mesoscale and submesoscale range. In Chapter 1, we develop an analytical framework for understanding the time dependent mesoscale eddy equilibration process in the Southern Ocean using theory and idealized numerical simulations. In the Southern Ocean, conventional wisdom dictates that the equilibrium stratification is determined by a competition between westerly-wind-driven Ekman upwelling and baroclinic eddy restratification. The transient picture however, is not well established. To study the time dependent response of the stratification in the Southern Ocean to changing winds, we derive a simple theoretical framework describing the energetic pathways between wind input, available potential energy (APE), eddy kinetic energy (EKE), and dissipation. By characterizing the phase and amplitude of the APE and EKE response to oscillating wind stress, with a transfer function, we show that the transient ocean response lies between - a high frequency (Ekman) limit, characterized by the isopycnal slopes responding directly to wind stress, and a low frequency ("eddy saturation") limit, wherein a large fraction of the anomalous wind work goes into mesoscale eddies. Both the phase and amplitude responses of EKE and APE predicted by the linear theory agrees with results from numerical simulations using an eddy resolving isopycnal-coordinate model. Furthermore, this theory can be used to explain certain features, like the lagged EKE response to winds, observed in previous modeling studies and observations. In Chapter 2, we investigate the role of submesoscale flows and inertia-gravity waves (IGW) on lateral transport, and lagrangian coherence, using velocity fields and particle trajectories from a high resolution ocean general circulation model (MITgcm llc4320). We use a temporal filter to partially filter the fast timescale processes, which results in a largely rotational/geostrophic flow, with a rapid drop off in energy at scales away from the mesoscales. We calculate and compare various Lagrangian diagnostics from particle advection simulations with these filtered/unfiltered velocities.At large length/time scales, dispersion by filtered and unfiltered velocities is comparable, while at short scales, unfiltered velocities disperse particles much faster. For the temporally filtered velocity fields, we observe strong material coherence similar to previous studies with altimetry derived velocities. When temporal filtering is reduced/removed, this material coherence breaks down with the particles experiencing enhanced vertical motion, which indicates that vertical advection is mainly associated with small scale, high frequency motions embedded within the larger scale flows. This study suggests that Lagrangian diagnostics based on satellite-derived surface geostrophic velocity fields, even with improved spatial resolution, as in the upcoming SWOT mission, may overestimate the presence of coherent structures and underestimate small scale dispersion. These high-frequency unbalanced motions are likely to alias the estimation of surface currents from low temporal resolution satellite altimetry, and the high-wavenumber sea surface height (SSH) variability may represent a dynamically different ageostrophic regime, where geostrophy might not be the best route to infer velocities. In Chapter 3, we explore statistical models based on machine learning (ML) algorithms, as an alternate route to infer surface currents from satellite observable quantities like SSH, wind and temperature. Our model is simply a regression problem with sea surface height, sea surface temperature, windstress (quantities that are directly observable by satellites) as input (regressors) and the surface currents (which are typically inferred by physical models like geostrophy, Ekman etc.) as the output (regressands). To help the model learn physical principles like geostrophy (which relies on taking spatial gradients), we also provide the spatial coordinates and information in the neighboring gridpoints as additional features. Using output from an ocean general circulation model (CESM POP) simulation as data, we first train a linear rigression model on small domains and show that linear models only work up to a certain extent in small localized regions far from the equator (no large variation in the Coriolis parameter f). We then train a deep neural network on the whole globe for a relatively short period of time and use it to make predictions. Even with a short training period, the NN can make fairly accurate predictions of surface currents over most of the global ocean just as well as the physical models themselves. At its present state the NN fails to pick up on some mesoscale and submesoscale turbulent flow features. We discuss some possible ways to address these present problems in future studies

Carbon from Space: determining the biological controls on the ocean sink of CO2 from satellites, in the Atlantic and Southern Ocean

Increasing anthropogenic carbon dioxide (CO2) emissions to the atmosphere have partially been absorbed by the global oceans. The role which the plankton community contributes to this net CO2 sink, and how it may change under climate change has been identified as a key issue to address within the United Nations decade of ocean science (2021-2030) Integrated Ocean Carbon Research (IOC-R) programme. This thesis sets out to explore how the net community production (NCP; the balance between photosynthesis and respiration) of the plankton community contributes to the variability in air-sea CO2 flux in the South Atlantic Ocean. In Chapter 2, NCP is shown to be accurately and precisely estimated from satellite measurements with respect to in situ observations. For this, weighted statistics are used to account for satellite, in situ and model uncertainties. The accuracy of satellite NCP could be improved by up to 40% by reducing uncertainties in net primary production (NPP). In Chapter 3, these satellite NCP observations were then used within a feed forward neural network scheme (SA-FNN) to extrapolate partial pressure of CO2 in seawater (pCO2 (sw)) over space and time, which is a key component to estimating the CO2 flux. NCP improved the accuracy and precision of pCO2 (sw) fields compared to using chlorophyll a (Chl a); the primary pigment in phytoplankton which is often used as a proxy for the biological CO2 drawdown. Compared to in situ observations, the seasonal variability in pCO2 (sw) was improved using the SA-FNN in key areas such as the Amazon River plume and Benguela upwelling, which make large regional contributions to the air-sea CO2 flux in the South Atlantic Ocean. In Chapter 4, these complete pCO2 (sw) fields were used with a timeseries decomposition method to determine the drivers of air-sea CO2 flux over seasonal, interannual and multi-year timescales. NCP was shown to correlate with the variability in CO2 flux on a seasonal basis. At interannual and mutli-year timescales, NCP became a more important contributor to variability in CO2 flux. This has not been previously analysed for this region. Mesoscale eddies in the global ocean can modify the biological, physical, and chemical properties and therefore may modify the CO2 flux. In Chapter 5, the cumulative CO2 flux of 67 long lived eddies (lifetimes > 1 year) was estimated using Lagrangian tracking with satellite observations. The eddies could enhance the CO2 flux into the South Atlantic Ocean by up to 0.08 %, through eddy modification of biological and physical properties. Collectively this research has shown that the plankton community plays a more significant role in modulating the air-sea CO2 flux in the South Atlantic Ocean, which has significant implications for the global ocean

MEC: A Mesoscale events classifier for oceanographic imagery

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.info:eu-repo/semantics/publishedVersio

Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence.

Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the global ocean circulation remains difficult as it requires simultaneous observations of upper-ocean velocity fields and interior temperature and density properties. Here we demonstrate that for quasigeostrophic baroclinic turbulence the eddy patterns in SSH snapshots alone contain sufficient information to estimate the eddy heat fluxes. We use simulations of baroclinic turbulence for the supervised learning of a deep Convolutional Neural Network (CNN) to predict up to 64% of eddy heat flux variance. CNNs also significantly outperform other conventional data-driven techniques. Our results suggest that deep CNNs could provide an effective pathway towards an operational monitoring of eddy heat fluxes using satellite altimetry and other remote sensing products

Progress in operational modeling in support of oil spill response

Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies

Multiscale modeling of a closure problem using the scattering transform

Els models climàtics estimen la dinàmica dels fluids a la Terra resolent equacions contínues en quadrícules finites. Els processos físics per sota de la resolució dels models climàtics tenen efectes estadístics a les escales resoltes. Per tenir-los en compte, els models incorporen parametritzacions. El desenvolupament de parametritzacions efectives és crucial per produir prediccions climàtiques precises a llarg termini. En aquest treball, abordem aquest problema utilitzant l'scattering transform per estudiar i modelar el problema de clausura en el domini d'scattering, i desenvolupem un model generatiu i un mètode de superresolució per a camps de turbulència.Los modelos climáticos estiman la dinámica de los fluidos en la Tierra resolviendo ecuaciones en cuadrículas finitas. Los procesos físicos por debajo de la resolución de los modelos climáticos tienen efectos estadísticos en las escalas resueltas. Para tenerlos en cuenta, los modelos incorporan parametrizaciones. El desarrollo de parametrizaciones efectivas es crucial para producir predicciones climáticas precisas a largo plazo. En este trabajo, abordamos este problema utilizando la scattering transform para estudiar y modelar el problema de clausura en el dominio de scattering, y desarrollamos un modelo generativo y un método de superresolución para campos de turbulencia.Climate models estimate the dynamics of the fluids on Earth by solving equations on finite grids. The physical processes below the resolution of climate models have statistical effects on the resolved scales. To account for them, models incorporate subgrid parametrizations. Devising effective parametrizations is crucial when producing accurate, long-term climate predictions. In this work, we address this challenge by using the scattering transform to study and model the closure problem in the scattering domain and we develop a generative model and a super-resolution method for turbulence fields.Outgoin

Fast ocean data assimilation and forecasting using a neural-network reduced-space regional ocean model of the north Brazil current

Data assimilation is computationally demanding, typically many times slower than model forecasts. Fast and reliable ocean assimilation methods are attractive for multiple applications such as emergency situations, search and rescue, and oil spills. A novel framework which performs fast data assimilation with sufficient accuracy is proposed for the first time for the open ocean. Speed improvement is achieved by performing the data assimilation on a reduced-space rather than on a full-space. A surface 10km resolution hindcast of the North Brazil current from the Regional Ocean Modelling System (ROMS) serves as the full-space state. The target variables are sea surface height, sea surface temperature, and surface currents. A dimension reduction of the full-state is made by an Empirical Orthogonal Function analysis while retaining most of the explained variance. The dynamics are replicated by a state-of-the-art neural network trained on the truncated principal components of the full-state. An Ensemble Kalman filter assimilates the data in the reduced-space, where the trained neural network produces short-range forecasts from perturbed ensembles. The Ensemble Kalman filter of the reduced-space is successful in reducing the root mean squared error by ∼ 45% and increases the correlations between state variables and data. The performance is similar to other full-space data assimilation studies. However, the computations are three to four orders of magnitude faster than for other full-space data assimilation schemes. The forecast of ocean variables is a computationally demanding task in terms of speed and accuracy. This framework manages to create fast forecasts in ∼ 30 seconds, once data have been assimilated. The forecasts are obtained using the trained neural network. We performed additional experiments using data and forecasts from July 2015 and January 2016. The analysis and forecasts in our framework yield a higher skill score and high spatial correlation when compared to the operational dataset Global Ocean Physics Analysis and Forecast by the UK MetOffice. Forcing the neural network with 10 m surface winds in order to improve the total surface currents forecast was considered. There is no additional skill in the forecasts using wind forcing because of the low Ekman component compared to the dominant geostrophic currents. The reduced model approach could be a useful tool when full physics regional models are not available to make a forecast.Open Acces

Elements of Ocean Mesoscale Eddy-Atmosphere Interactions in Extratropics

As resolution of observations and climate models continues to improve, it has become increasingly evident that mesoscale eddies – a ubiquitous feature of the world ocean – can interact with the overlying atmosphere, potentially affecting large-scale atmospheric and oceanic circulation and climate. Improving our understanding of this ocean mesoscale eddy – atmosphere (OME-A) interaction has important implications for improving climate simulations and predictions. This dissertation contributes to this understanding by focusing on two elements of OME-A interaction. The first element deals with the influence of ocean mesoscale eddies on rainfall. By comparing three different satellite-derived rainfall datasets, we examined the robustness of the rainfall response to ocean eddy induced mesoscale sea-surface temperature anomalies (SSTAs). The three datasets are the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), NOAA Climate Prediction Center (CPC) Morphing Technique (CMORPH) global precipitation and newly available Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) that is based on the latest remote sensing technology with finer spatial and temporal resolution. The results show that 1) all datasets exhibit a similar rainfall response to ocean eddies, but the amplitude of the rainfall response varies among datasets with IMERG producing the strongest and most coherent rainfall response, despite the weakest time-mean rainfall, 2) eddy-induced precipitation response is significantly stronger in winter than in summer and over warm eddies than cold eddies, and these asymmetries in rainfall response is more robust in IMERG than in the other two datasets. Documenting and analyzing these asymmetric rainfall responses are important for understanding the potential role of ocean eddies in forcing the large-scale atmospheric circulation and climate. The second element examines the effect of OME-A interaction on ocean eddy wind power that plays a vital role in dissipating eddy kinetic energy (EKE). By using a scaling analysis and analyzing eddy-resolving coupled climate model simulations, we not only quantify the impact of OME-A interaction on eddy wind power, but also provide a mechanistic understanding of the underlying process. Results show that the impact of OME-A feedback on eddy wind power, albeit smaller than that due to ocean current feedback, is significant and amounts to about 30-40% reduction of the value without OME-A interaction. Therefore, in the absence of OME-A interaction, eddy wind power is significantly overestimated, thus providing a too-strong sink for EKE