Thesis (M.Eng.) -- Memorial University of Newfoundland. / Typescript. Bibliography: leaves 73-76. Also available online.

The coastal ocean is a marginal region of the global ocean, but is home to metabolically intense ecosystems which increase the structural complexity of the benthos. These ecosystems have the ability to alter the carbon chemistry of surrounding waters through their metabolism, mainly through processes which directly release or consume carbon dioxide. In this way, coastal habitats can engineer their environment by acting as sources or sinks of carbon dioxide and altering their environmental chemistry from the regional norm. In most coastal water masses, it is difficult to resolve the ecosystem effect on coastal carbon biogeochemistry due to the mixing of multiple offshore end members, complex geography or the influence of variable freshwater inputs. The Red Sea provides a simple environment for the study of ecosystem processes at a coastal scale as it contains only one offshore end-member and negligible freshwater inputs due to the arid climate of adjacent land. This work explores the ability of three Red Sea benthic coastal habitats (coral reefs, seagrass meadows and mangrove forests) to create characteristic ecosystem end-members, which deviate from the biogeochemistry of offshore source waters. This is done by both calculating non-conservative deviations in carbonate stocks collected over each ecosystem, and by quantifying net carbonate fluxes (in seagrass meadows and mangrove forests only) using 24 hour incubations. Results illustrate that carbonate stocks over ecosystems conform to broad ecosystem trends, which are different to the offshore end-member, and are influenced by inherited properties from surrounding ecosystems. Carbonate fluxes also show ecosystem dependent trends and further illustrate the importance of sediment processes in influencing CaCO3 fluxes in blue carbon benthic habitats, which warrants further attention. These findings show the respective advantages of studying both carbonate stocks and fluxes of coastal benthic ecosystems in order to understand the spatial, temporal and net effects of their metabolism on the coastal ocean.

Bayesian inference is commonly used to quantify and reduce modeling uncertainties in coastal ocean models by computing the posterior probability distribution function (pdf) of some uncertain quantities to be estimated conditioned on available observations. The posterior can be computed either directly, using a Markov Chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation (DA) approach. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without a significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach often due to restricted Gaussian prior and noise assumptions. This thesis aims to develop, implement and test novel efficient Bayesian inference techniques to quantify and reduce modeling and parameter uncertainties of coastal ocean models. Both state and parameter estimations will be addressed within the framework of a state of-the-art coastal ocean model, the Advanced Circulation (ADCIRC) model. The first part of the thesis proposes efficient Bayesian inference techniques for uncertainty quantification (UQ) and state-parameters estimation. Based on a realistic framework of observation system simulation experiments (OSSEs), an ensemble Kalman filter (EnKF) is first evaluated against a Polynomial Chaos (PC)-surrogate MCMC method under identical scenarios. After demonstrating the relevance of the EnKF for parameters estimation, an iterative EnKF is introduced and validated for the estimation of a spatially varying Manning’s n coefficients field. Karhunen-Lo`eve (KL) expansion is also tested for dimensionality reduction and conditioning of the parameter search space. To further enhance the performance of PC-MCMC for estimating spatially varying parameters, a coordinate transformation of a Gaussian process with parameterized prior covariance function is next incorporated into the Bayesian inference framework to account for the uncertainty in covariance model hyperparameters. The second part of the thesis focuses on the use of UQ and DA on adaptive mesh models. We developed new approaches combining EnKF and multiresolution analysis, and demonstrated significant reduction in the cost of data assimilation compared to the traditional EnKF implemented on a non-adaptive mesh.

Ocean-atmosphere fluxes on the West Florida Shelf (WFS) coastal ocean region are investigated using observations and derived surface fluxes from an array of buoys deployed between 1998 and 2003. The observed annual cycle shows that water column temperatures increase and are stratified when heat flux is positive, and they decrease and are well mixed when it is negative. Water temperature is minimum (maximum) when heat flux switches sign from negative (positive) to positive (negative) in early spring (autumn). Tropical and extra-tropical events help define the seasonal characteristics of the water temperature. Despite considerable daily and synoptic variability in relative humidity, observations on the WFS show that the monthly mean values are nearly constant at about 75%. Winter relative humidity varies from less than 50% to over 100% (supersaturation values of up to 3% are recorded and coincide with fog on shore) as extra-tropical fronts move over the WFS. Sensor distribution shows small spatial variations in relative humidity in the coastal ocean environment that depends on high frequency variability in meteorological conditions and low-frequency variability in oceanic conditions. Comparisons with observations show that standard climatologies are unable to reproduce spatial variability on the WFS, especially in relative humidity and surface heat flux components that are dependent on sea surface temperature. Model experiments show that careful attention must be paid in calculating and applying surface heat fluxes. Observations and models are employed to assess the relative importance of surface fluxes and convergence of heat flux by the ocean circulation in controlling ocean temperature. In spring and autumn, seasonal change in water temperature is mainly controlled by surface heat flux with smaller contributions by ocean convergence, but synoptic scale variability is controlled by both surface heat flux and ocean circulation. Surface fluxes are of primary importance in determining water temperature during the passage of tropical storms or extra-tropical fronts. The coastal ocean temperature balance is fully three-dimensional. Models must be supported by adequate surface heat flux boundary conditions. These require sufficient numbers of in situ measurement points for constraining atmospheric models. The number of observations will depend on the spatial scales of SST variability.

Located at the northern edge of the Northern Gulf of Alaska (NGOA), Prince William Sound(PWS) is a small, two-strait semi-enclosed sea. The general ocean circulation pattern inside PWS is significantly affected by external forcings, for instance, the large-scale circulation in NGOA, atmospheric pressure and surface winds, surface heating/cooling, runoff, and tides. Motivated by multi-year experience with a well-validated, quasi-operational ocean circulation nowcast/forecast system for PWS (viz., Extended PWS Nowcast/Forecast System (EPWS/NFS)), the present study addresses some aspects of the PWS response to various external forcings, via numerical simulations. Based on the Princeton Ocean Model (POM), four numerical implementations have been examined, viz., PWS-POM, Extended PWS-POM (EPWS-POM), Idealized PWS-POM (IPWS-POM), and a 2-D tidal model. These implementations are used to simulate physical processes with various spatial and temporal scales in PWS. A series of numerical simulations are conducted, driven by various external forcings ranging from large scale and mesoscale circulation in NGOA represented by the Global Navy Coastal Ocean Model (NCOM), to atmospheric pressure observed by National Data Buoy Center (NDBC) buoys and mesoscale winds predicted by Regional Atmospheric Modeling System (RAMS), and to tides simulated by the 2-D tidal model. These simulations, along with analysis from a Helmholtz resonance model, demonstrate and help interpret some phenomena in PWS; for instance, barotropic Helmholtz resonance in coastal sea levels, and volume transports through the two PWS straits, and a dominant cyclonic gyre in the Central Sound in August and September. The simulation results are used to study a wide range of oceanic phenomena in PWS; e.g., two-layer/three-layer baroclinic transports through the straits, a "transition band" in the coherence pattern between volume transports through the two straits, mesoscale circulation in the Central Sound, the deep water circulation, and the annual tidal energy budget.

An oceanographic buoy with 10 atmospheric and oceanographic instruments was deployed in Clayoquot Sound on the west coast of Canada in 2007. The high-resolution time series was used to monitor the fine-scale variability in the coastal ocean. Over 700 CTD profiles measuring temperature, salinity and chlorophyll fluorescence made in the region of the buoy were used to relate the buoy data to spatial patterns. Analysis showed that large-scale upwelling in combination with the localized winds and tidal currents affect water properties at time scales of hours to days. At low tide the buoy represented inland water and at high tide the buoy represented offshore water. Both the buoy data and CTD profiles measured a strong offshore/onshore gradient. For temperature the gradient depended on the direction of the wind, salinity was always higher offshore compared to onshore, and the chlorophyll fluorescence was higher onshore in the early spring and higher offshore for the rest of the time series. The fine scale temporal resolution of the buoy was able to capture the variability measured by the CTD profiles in a 40km2 area. This work shows the importance of making high-resolution temporal measurements in the coastal ocean. However, these types of moorings also require frequent maintenance. In Clayoquot Sound, the optical sensors needed to be cleaned every 4-6 days.

A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, dataset reconstructions with this method are sensitive to the characteristics of the input data used. The spatial and temporal coverage of the input imagery can heavily impact the reconstruction outcome, and thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, the DINEOF method was applied to a three-year time series of MODIS-Aqua chlorophyll-a of the Salish Sea, Canada. Spatial reconstructions were performed on an annual and multi-year basis at daily and week- composite time resolutions, and assessed relative to the original, clouded chla datasets and a set of extracted in situ chla measurements. A sensitivity test was performed to assess stability of the results with variation of cross-validation data and simulated scenarios of lower temporal data coverage. Daily input time series showed greater accuracy reconstructing chla (95.08-97.08% explained variance, RMSExval 1.49 - 1.65 mg m-3) than week-composite counterparts (68.99-76.88% explained variance, RMSExval 1.87 – 2.07 mg m-3), with longer time series of both types producing a better relationship to original chla pixel concentrations (R 0.95 over 0.94, RMSE 1.29 over 1.35 mg m-3, slope 0.88 over 0.84). Original daily chla achieved a better relationship to in situ matchups than DINEOF gap-filled chla, with annual DINEOF-processed data performing better than the multi-year. The results of this study are of interest to those who require spatially continuous satellite-derived products, particularly from short time series, and encourage processing consistency in future DINEOF studies to allow unification for global purposes such as climate change studies (Mélin et al., 2017). / Graduate

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