UMD Theses and Dissertations
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Item OCEAN HEAT CONTENT CALCULATION IMPROVEMENTS FOR EARTH’S ENERGY IMBALANCE QUANTIFICATION(2024) Boyer, Tim; Carton, James; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Earth’s Energy Imbalance, the difference between incoming and outgoing radiation at the top of the atmosphere, is stored in the atmosphere, land surface, cryosphere, and ocean, but is stored overwhelmingly (~90%) in the ocean on interannual and longer time scales. This imbalance, which is reflected in ocean heat uptake, is a primary indicator of the magnitude of change in energy the Earth’s system as well as an essential variable for understanding short-term variations and their effects on long-term regional and global climate change. The primary methods for calculating ocean heat content all depend on situ measurements of ocean subsurface temperature. The ocean subsurface temperature observing system as it is currently configured, with a substantial but not exclusive contribution from autonomous Argo profiling floats, is shown here to allow estimation of annual global ocean heat uptake with an uncertainty well below that possible with earlier ocean observing systems. It is also shown that maintenance and improvement of a global best quality ocean temperature profile database will lower uncertainty, both historically and for the current observing system and compensate to some extent for areas of sparse data in both direct calculation from observation and in data assimilation models. It is also shown that improvements to the methods used for mapping the inhomogeneous and anisotropic observations onto a regular grid spanning the global ocean will reduce uncertainty historically, currently, and into the future. On shorter monthly timescales regional changes in the Earth’s Energy Imbalance requires tracking the storage within the atmosphere, land, and cryosphere, and the heat transport within the ocean especially to depths where the energy is stored on longer time scales, in addition to ocean heat uptake. Monthly heat uptake estimates discussed here can be utilized with additional terms from atmosphere/land and ocean/sea ice reanalyses to provide Earth's Energy Imbalance estimates on these shorter time-scales in the future.Item PREDICTING THE SALINITY HISTORY OF OYSTERS IN DELAWARE BAY USING OBSERVING SYSTEMS DATA AND NONLINEAR REGRESSION(2022) HOWLADER, ARCHI; NORTH, ELIZABETH; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet methods for predicting the salinity history at specific sampling stations are not widely available. The specific aim of this research was to predict the history of salinity experienced by juvenile and adult oysters (Crassostrea virginica) collected at sampling stations in Delaware Bay as part of the Selection along Estuarine Gradients in Oysters (SEGO) project. To do so, empirical relationships were created to predict salinity at five oyster bed stations using observing systems data and then applied to construct indices of salinity exposure over an oyster’s lifetime. The desired accuracy was +/- 2 psu. Three independent sources of salinity data were used in conjunction with observing systems data to construct and validate the predictive relationships. Observing systems data from the USGS station at Reedy Island Jetty and continuous near-bottom measurements taken by the U.S. Army Corps of Engineers (ACOE) from 2012-2015 and 2018 were employed to fit nonlinear empirical models at each station. Haskin Shellfish Research Laboratory (Haskin) data were used to evaluate model fit, then ACOE data from 2018 (withheld from model fitting in the validation analysis) and SEGO data from 2021 were used to validate models. The best-fitting models for predicting salinity at the oyster bed stations given the salinity at Reedy Island Jetty were logarithmic in form. The root mean square error (RMSE) of the models ranged from 1.3 to 1.6 psu when model predictions were compared with Haskin data, 0.5 to 1.5 when compared with ACOE data, and 0.6 to 0.8 when compared with SEGO data. All of these models were within the desired accuracy range. Results demonstrate that observing systems data can be used for predicting salinity within +/- 2 psu at oyster bed stations within 39 km in upper Delaware Bay. When these models were applied to estimate low salinity exposure of 2-year-old oysters via the metric of consecutive days below 5 psu, the indices suggested that there could be as much as a 42-day difference in low salinity exposure for oysters at stations 31 km apart. This study helps further our understanding of the salt distribution in Delaware Bay as well as the effect of low-salinity stress on the life cycle and genetic differentiation of oysters. In addition, the approach of using observing systems data to predict salinity could be applied to advance understanding of salt distribution and the effect of low salinity exposure on living resources in other estuaries.Item RETRIEVALS OF ANTARCTIC SEA ICE PHYSICAL PROPERTIES FROM SATELLITE RADAR ALTIMETRY(2021) Fons, Steven William; Carton, James; Kurtz, Nathan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Satellite observations have been used in sea ice research throughout the last 40+ years and have brought to light substantial changes in the global sea ice coverage. More recently, satellite altimetry has become a valuable tool to estimate the thickness of sea ice - a parameter that plays an important role in the Earth System by moderating heat and moisture fluxes between the polar ocean and atmosphere. While radar altimetry has been effective in providing estimates of Arctic sea ice thickness, the complex snow stratigraphy and uncertain snow depth on Antarctic sea ice have precluded sea ice thickness retrievals in the Southern Ocean, leading to a decade-long gap in the thickness record spanning the lifetime of ESA’s CryoSat-2 satellite. This dissertation will address the need for Antarctic sea ice thickness estimates from CryoSat-2 through the development and assessment of new retrievals of sea ice physical properties that enable the estimation of sea ice thickness.The first part of this dissertation is aimed at developing a CryoSat-2 retrieval algorithm that is less dependent on uncertain returns from the snow-ice interface of Antarctic sea ice. This method exploits observed scattering of Ku-band radar pulses from the snow surface and snow volume atop sea ice and uses a physical waveform model and optimization approach to retrieve the air-snow interface elevation and snow freeboard. Building off the initial development, the second part of this work offers improvements to – and assessments of – the retrieval process though comparisons with coincident snow freeboard measurements from NASA’s ICESat-2 laser altimeter. The final part of this dissertation uses the retrieval process to estimate snow depth and ice freeboard, enabling first estimates of Antarctic sea ice thickness that span the CryoSat-2 mission. Potential applications for use of this method over Arctic sea ice are also explored. The studies within this dissertation represent new possibilities for CryoSat-2 data and lay a foundation for the development of a combined laser-radar altimetric record of Antarctic sea ice thickness.Item A numerical investigation of variability in particulate organic matter transport and fate, phytoplankton and primary production, and denitrification in a partially mixed estuary(2020) Wang, Hao; Hood, Raleigh; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In Chesapeake Bay substantial quantities of organic matter are produced during the spring bloom, which contributes to severe chronic bottom oxygen depletion during the summertime. However, the details of this transport in the estuarine system under realistic forcing is still unclear. In this Research, a three-dimensional model was used to investigate the production, transport, and fate of organic matter in Chesapeake Bay. Analysis of a control volume in the deep channel revealed that the sinking flux of fast-sinking particulate organic nitrogen (PON) into the deep channel is comparable to the horizontal advective transport. The model analysis also revealed a pronounced east to west transport of PON during the springtime and a tendency to export mass from the eastern shore to the deep channel and from the deep channel to the western shore of the Chesapeake Bay, and also a convergence of mass transport on the western shore. This transport is consistent with the lateral estuarine circulation in Chesapeake Bay that arises due to the asymmetry of the flood-neap tidal cycle. In addition, the model revealed that seasonal variations in wind alter the magnitude and distribution of organic matter flux in the along channel and cross channel direction, with northerly winds during the springtime favoring more northward organic matter transport and more organic matter accumulation in the deep channel, however, the lateral net flux direction remains the same. In Chesapeake Bay, phytoplankton biomass typically peaks in spring whereas primary production peaks in summer. For this to happen, phytoplankton growth rates must be low in spring and high in summer and very likely there must be low grazing losses in spring and high grazing losses in summer as well. In this research, a three dimensional coupled physical-biological model is used to explore how these seasonal patterns in phytoplankton and primary production arise during the year from 2000 to 2005. It is shown that with the seasonal variation of maximum carbon to chlorophyll ratio, temperature control on phytoplankton growth, and temperature-dependent zooplankton grazing effects, my model can capture the spring peak in phytoplankton biomass and the summer peak in the primary production, agreeing well with the observations. The model simulates high phytoplankton growth rates in the summer, with the maximum growth rates occurring in late summer. The model also reveals that nutrient supply shifts from river-derived nitrate in the springtime to organic matter- derived ammonium during summer. The simulation results also reveal that a substantial fraction of the ammonium that supports the high summer production is derived from allochthonous transport rather than autochthonous ammonium production. The transport process provides as large as 50% ammonium needed for uptake during summertime in the mesohaline Chesapeake Bay. My research also confirms the importance of nutrient recycling in supporting high summer production in Chesapeake Bay. Denitrification is an essential process in the marine nitrogen cycle because it removes bioavailable nitrogen from the aquatic system. Current understanding of denitrification variability in Chesapeake Bay is severely constrained by the sparse observations that provide insufficient coverage in both space and time. In this research, denitrification variability is examined in the Chesapeake Bay using a three dimensional coupled physical-biogeochemical model based on the Regional Ocean Modelling System (ROMS). Model simulations indicate that denitrification occurs not only in the sediment but also in the water column at significant, though somewhat lower rates. Model results indicated that the water column accounts for around 7.5% of the total denitrification amount that occurred in the system during the 2001 and 2002 period of this study. This conflicts with the historical assumption that water column denitrification in Chesapeake Bay is negligible. The model also reveals the spatial patterns in denitrification with more denitrification occurring in the upper to middle bay due to higher availability of organic matter in these areas compared to the lower bay. In terms of temporal variability, denitrification peaks in the sediment in spring while in the water column it peaks in the summer. The reason for this difference in the timing is related to the availability of oxygen: In the spring oxygen levels in the water column are too high to allow denitrification so it happens only in the sediment where low oxygen levels persist all year around. In summer low oxygen and depletion of nitrate below the pycnocline completely shuts down denitrification in the sediment in the mesohaline and polyhaline region of the by. However, water column denitrification continues at the interface between oxygenated waters near the surface and oxygen-depleted waters below where coupled nitrification-denitrification happens. The model also reveals that denitrification removes significant quantities of biologically available nitrogen, meaning that without this process, more summertime primary production would occur in the form of more surface chlorophyll, increasing as much as 10ug/L in the middle bay region, which would, in turn, lead to more oxygen depletion.Item EVALUATING OCEANOGRAPHIC HYPOTHESES: THREE METHODS FOR TESTING IDEAS(2020) Johnson, Benjamin K; Kalnay, Eugenia E; Wenegrat, Jacob O; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The disciplines of meteorology and oceanography are both vital to understanding the earth system. Throughout most of the last half century, meteorology has largely been a prognostic discipline. Forecasts made by meteorologists have been widely used and scrutinized, allowing for countless opportunities to test and improve ideas about atmospheric circulation and physics. Since weather forecasts involve integrating numerical models and updating the model state via data assimilation, forecasting demands frequent use of the principles of Bayesian inference. This requirement essentially confronts the physics contained within numerical models at recurring intervals and can reveal systematic model bias. In contrast, prognostic applications have been less prevalent in oceanography. Oceanographic forecasts are much rarer than atmospheric forecasts and, perhaps as a consequence of this disparity, many ideas concerning oceanic circulation have not been tested to the same degree as ideas concerning atmospheric circulation. This dissertation presents three methods for testing oceanographic ideas: applying common methodologies to analogous regions of different ocean basins; creating synthetic time series to mimic the properties of oceanographic time series in order to construct null distributions for hypothesis testing; and using water mass census information to interpret the results of water mass transformation analysis.Item The Dynamics of Plunging Breakers and the Generation of Spray Droplets(2020) Erinin, Martin Aleksandrov; Duncan, James H.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, drop generation by plunging breaking waves is studied in laboratory-scale experiments. The breaking waves are generated by a programmable wavemaker that is set to produce a dispersively focused wave packet. Breaker profile and drop measurements are obtained for three breaking waves of increasing intensity, called herein the, weak, moderate, and strong plunging breakers, respectively. Drops, with radius >= 50 microns, are measured using a cinematic in-line holographic system positioned at 28 streamwise measurement locations, arranged in a horizontal plane, called herein the measurement plane, positioned 1 cm above the maximum wave crest height, during many repeated breaking events. From the holograms, the radius, three-dimensional location, and velocity of the drops is determined. The evolution of the breaker profile is measured at the center plane of the tank using a cinematic laser-induced fluorescence (LIF) technique. Drop and breaker profile movies are taken simultaneously and recorded at 650 frames per second. The breaker profile and drop measurements from the weak breaker are used to identify and quantify spray generation processes in plunging breakers. Two spatially and temporally separated regions of drop production are found. The first region (I) of drop production is associated with the active phase of wave breaking and begins with jet impact. In this region, drops are produced by jet impact, large bubble bursting events, and splashing. The second region (II) of drop production occurs after the active phase of wave breaking, approximately one wave period after jet impact. In this region, drops are generated by small air bubbles, initially entrained by the breaker, that rise to the free-surface and burst. Various features of the breaker profiles and drop production are compared and contrasted between the three waves. The temporal evolution of the breaker profile is measured in 10 unique realizations for each of the three breakers. At every instant in time, the phase averaged mean breaker profile and the distribution of standard deviation (SD) in breaker height along the streamwise direction of the mean profile is computed. Using the mean profiles, the three breakers are physically characterized based on their wave crest and jet impact speed. When aligned to the plunging jet impact location in space and time, the breaker profiles are found to be highly repeatable throughout the non-linear wave breaking process. Profile regions with high SD in height correspond to regions of high drop production. The number of drops generated per breaking event is found to scale exponentially with jet impact speed. The drop probability distribution from each of the three waves follows a power law scaling where large and small drop regions obey power laws with different coefficients.Item MODELING IMPACTS OF SUBMERSED AQUATIC VEGETATION ON SEDIMENT DYNAMICS UNDER STORM CONDITIONS IN UPPER CHESAPEAKE BAY(2019) Biddle, Mathew Michael; Sanford, Lawrence P; Palinkas, Cindy; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Submersed aquatic vegetation is an important modulator of sediment delivery from the Susquehanna River through the Susquehanna Flats into the Chesapeake Bay. However, the impact of vegetation coupled with the physical drivers of sediment transport through the region are not well understood. This study used a new vegetation component in a coupled flow-wave-sediment transport modeling system (COAWST) to simulate summer through fall 2011, when the region experienced a sequence of events including Hurricane Irene and Tropical Storm Lee. Fine sediment was exported under normal flows and high wind forcing but accumulated under high flows. The relative effect of vegetation under normal and high wind forcing depended on previous sediment dynamics. Vegetation doubled the accumulation of fine sediments under high flows. While further refinement of the bed model may be needed to capture some nuances, the COAWST modeling system provides new insights into detailed sediment dynamics in complex vegetated deltaic systems.Item Strongly Coupled Ocean-Atmosphere Data Assimilation with the Local Ensemble Transform Kalman Filter(2018) Sluka, Travis Cole; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current state-of-the-art coupled data assimilation systems handle the ocean and atmosphere separately when generating an analysis, even though ocean atmosphere models are subsequently run as a coupled system for forecasting. Previous research using simple 1-dimensional coupled models has shown that strongly coupled data assimilation (SCDA), whereby a coupled system is treated as a single entity when creating the analysis, reduces errors for both domains when using an ensemble Kalman filter. A prototype method for SCDA is developed with the local ensemble transform Kalman filter (LETKF). This system is able to use the cross-domain background error covariance from the coupled model ensemble to enable assimilation of atmospheric observations directly into the ocean. This system is tested first with the intermediate complexity SPEEDYNEMO model in an observing system simulation experiment (OSSE), and then with real observations and an operational coupled model, the Climate Forecasting System v2 (CFSv2). Finally, the development of a major upgrade to ocean data assimilation used at NCEP (the Hybrid-GODAS) is presented, and shown how this new system could help present a path forward to operational strongly coupled DA.Item Physical-Biological Interactions Driving the Distribution of the Pelagic Macroalgae Sargassum(2019) Brooks, Maureen Therese; Coles, Victoria J.; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The holopelagic macroalgae of the genus Sargassum are the ecosystem engineers of a unique open-ocean rafting ecosystem in the subtropical North Atlantic and tropical Atlantic. Over the last decade, increases in biomass in the tropics and Caribbean Sea have been observed. The underlying causes of this regime shift have been difficult to discern without a baseline understanding of the drivers of Sargassum distribution. The objective of this dissertation is to fill this knowledge gap using remote and in situ observations, and coupled ocean circulation, biogeochemical, Lagrangian particle, and Sargassum physiology models. A satellite-derived Sargassum abundance climatology shows the center-of-mass of Sargassum shifting between the tropics, Caribbean, Gulf of Mexico, and Sargasso Sea throughout the year. Model experiments demonstrate that advection alone can explain up to 60% of the observed distribution at time scales shorter than two months. At longer time scales, the growth and reproductive strategy of the macroalgae interact with physical processes to drive the overall observed pattern. Sargassum populations in the Western Tropical Atlantic and Gulf of Mexico appear to exert disproportionate influence over the basin-wide distribution. One key physical process influencing both transport and growth is inertia. A novel inverse method, developed from remote sensing to determine the effective radius of Sargassum rafts, facilitates modeling inertial effects. The effective radius is on the order of 0.95 m, much closer to the size of an individual plant than that of aggregations which can span kilometers. The inclusion of inertia alters modeled distributions of Sargassum by increasing retention in the Gulf of Mexico and the Caribbean, while increasing export from the Sargasso Sea by up to 20%. Inertia acting on buoyant Sargassum rafts also leads to their increased entrainment in cyclonic eddies. These eddies propagate toward the north-west in the northern hemisphere providing transport for Sargassum from the tropics through the Caribbean to the Gulf of Mexico and leading to increased biomass due to transport into regions with better growing conditions. Sargassum biology and its interaction with ocean circulation and mesoscale features is central to improving understanding of the changes in its distribution and for prediction of costly beaching events.Item Response of the coastal ocean and estuaries to tropical cyclones(2018) Zhang, Fan; Li, Ming; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Landfalling tropical cyclones (TC) pose great threats to public safety. The recent decades have witnessed major advances of knowledge in TC dynamics and improvement in TC forecast models, however, occasionally inaccurate TC intensity and storm surge predictions remain a vital concern. Different representations of subgrid-scale physics by various atmospheric model parameterization schemes lead to uncertainty in predictions of TC’s intensity and associated surges. In a case study for Hurricane Arthur (2014), local closure scheme for planetary boundary layer turbulence produces lower equivalent potential temperature than non-local closure schemes, leading to under-predicted TC intensity and surge heights. On the other hand, higher-class cloud microphysics schemes over-predict TC intensity and surge heights. Without cumulus parameterization for coarse-resolution grids, both TC intensity and surge heights are grossly under-predicted due to large precipitation decreases in the storm center. To avoid widespread predictions, the ensemble mean approach is shown to be effective. Another source of TC forecast error is inaccurate sea surface temperature (SST) prediction, and accurate SST prediction necessitates a better understanding of mixing processes in the coastal ocean. Previously, the importance of TC-induced near-inertial currents (NICs) to mixing in the coastal ocean was overlooked. With high-frequency radar and autonomous glider, long-lasting NICs with amplitudes of ~0.4 m s-1 were observed on the shelf during Arthur. With an atmosphere-ocean model, we find the NICs were dominated by mode-1 vertical structure and were a major contributor to the shear spectrum. Therefore, NICs may be important in producing turbulent mixing and surface cooling during Arthur’s passage. In the future, with warmer SST, sea level rise, and possible hard shorelines in estuaries, increased storm surge hazard is expected. Using Isabel (2003) as a case study, we find storm intensification under 2100 SST raises surge heights in Chesapeake Bay by 0.1-0.4 m given increased energy input. While sea level rise in 2100 reduces surge heights by 0-0.15 m through non-linear processes, it increases total water level by 0.4-1 m. Moreover, hard shoreline further increases surge heights by up to 0.5 m in the middle and upper Chesapeake Bay by prohibiting energy flux towards wetlands.