Atmospheric & Oceanic Science Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2747

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End date: 2019

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    THE DIURNAL AND SEASONAL RADIATIVE EFFECTS OF CIRRUS CLOUDS UTILIZING LARGE AIRBORNE AND SPACE-BORNE LIDAR DATASETS
    (2019) Ozog, Scott; Dickerson, Russell R; Yorks, John E; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Cirrus clouds are globally the most common cloud type, however, their radiative impact on the Earth remains a large source of uncertainty in global climate models. Cirrus are unique in that they are absorptive to terrestrial outgoing longwave radiation, while also relatively transmissive to incoming solar radiation. The interactions of this greenhouse and albedo effect determine the sign and magnitude of cirrus radiative effects. Cirrus are microphysically complex, and can exhibit a variety of different ice crystal shapes and sizes depending on the thermodynamic environment in which they form, and their dynamic formation mechanism. Our ability to reliably model cirrus radiative effects is dependent upon accurate observations and parameterizations incorporated into radiative transfer simulations. Laser lidar instruments provide valuable measurements of cirrus clouds unavailable by other radar systems, passive remote sensors, or in-situ instruments alone. In this dissertation I developed and tested an improved calibration technique for the ACATS lidar instrument, and its impact on the direct retrieval of cirrus HSRL optical properties. HSRL retrievals theoretically have reduced uncertainty over those from a standard backscatter lidar. ACATS flew on two field campaigns in 2012 and 2015 where it was unable to consistently calibrate its etalon. It has been operating from the lab in NASA GSFC collecting zenith pointing data of cirrus layers where the improved calibration has resulted in consistent and reliable separation of the particulate and Rayleigh signal components. The diurnal trend of cirrus influence on the global scale has primarily been limited to data provided by satellites in sun-synchronous orbit, which provide only a snapshot of conditions at two times a day. Utilizing data from the CATS lidar aboard the ISS I investigated cirrus at four periods throughout the day in morning, afternoon, evening, and night across all seasons. Cirrus radiative effects were found to have a large latitudinal dependence, and have a greater potential to cool than many studies suggest with their primary warming contributions skewed towards the nighttime hours. Constrained lidar retrievals reduce the assumptions made in retrieving cirrus optical properties. Utilizing the expansive airborne CPL dataset from six flight campaigns I model the radiative effects of over twenty thousand constrained cirrus observations. Mid-latitude cirrus were found to have a mean positive daytime forcing equivalent to that of the CO2 greenhouse effect. However, synoptic cirrus were found to have a greater warming effect than convective cirrus, which were more likely to have a cooling effect.
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    SEA-SURFACE TEMPERATURE BASED STATISTICAL PREDICTION OF THE SOUTH ASIAN SUMMER MONSOON RAINFALL DISTRIBUTION
    (2019) Sengupta, Agniv; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The South Asian summer monsoon brings copious amounts of rainfall accounting for over 70% of the annual rainfall over India. Summer monsoon predictions have drawn considerable public/policy attention lately as South Asia has become a resource-stressed and densely populated region. This environmental backdrop and the livelihood concerns of a billion-plus people generate the demand for more accurate monsoon predictions. The prediction skill, however, has remained marginal and stagnant for several decades despite advances in the representation of physical processes, numerical model resolution, and data assimilation techniques, leading to the following key question: what is the potential predictability of summer monsoon rainfall at lead times of one month to a season? This dissertation examines the role of influential climate system components with large thermal inertia and reliable long-term observational records, like sea-surface temperature (SST) in forecasting the seasonal distribution of South Asian monsoon rainfall. First, an evolution-centric SST analysis is conducted in the global oceans using the extended-Empirical Orthogonal Function technique to uncover the recurrent modes of spatiotemporal variability and their potential inter-basin linkages. A statistical forecast model is next developed using these extracted modes of SST variability as predictors. Assessment of the forecasting system’s long-term performance from reconstruction and hindcasting over an independent verification period demonstrates high forecast skill over core monsoon regions – the Indo-Gangetic Plain and southern peninsular India, indicating prospects for improved seasonal predictions. The influence of SSTs on the northeast winter monsoon is subsequently investigated, especially, its evolution, interannual variability and the El Niño–Southern Oscillation (ENSO) influence. Key findings from this study include evidence of increased rainfall over southeastern peninsular India and Sri Lanka (generated by an off-equatorial anticyclonic circulation centered over the Bay of Bengal) during El Niño winters. This dissertation provides the first quantitative assessment of the potential predictability of summer monsoon rainfall anomalies – the maximum predictable summer rainfall signal (amount, distribution) over South Asia from prior SST information – at various seasonal leads, and notably, at SST-mode resolution. The improved skill of the SST-based statistical forecast establishes the bar – an evaluative benchmark – for the dynamical prediction of summer monsoon rainfall.
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    Improving our Understanding of Tropical Cyclone Unusual Motion and Rapid Intensification
    (2019) Miller, William James Schouler; Zhang, Da-Lin; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Despite steady improvement in their tropical cyclone (TC) track and intensity forecasts over recent decades, operational numerical weather prediction (NWP) models still struggle at times in predicting two TC phenomena: climatologically unusual motion and rapid intensification (RI). Atlantic TCs typically move clockwise along curved tracks skirting the southern, western, and northwestern periphery of the Western Atlantic Ridge. Hurricane Joaquin (2015) followed a particularly unusual hairpin loop-shaped track that was poorly predicted by most operational NWP models, including the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Over recent years, considerable interest has also developed in understanding the cause-and-effect relationship between RI, defined here as a maximum surface wind (VMAX) intensification rate exceeding 15 m s-1 (24 h-1), and outbreaks of inner core deep convection, known as convective bursts (CBs), that have been observed to precede or coincide with RI in some TCs. A deeper physical understanding of the atmospheric processes governing TC unusual motion and RI, together with retrospective case study analyses of model forecast errors, will help us to identify NWP model components – data assimilation and physical parameterizations, for example – that may need further improvement. This research project seeks to (i) identify the atmospheric features that steered Hurricane Joaquin (2015) along the southwestward leg of its looping track and (ii) investigate the thermodynamic and three-dimensional characteristics of CBs as a first step toward developing a more comprehensive understanding of how CBs may facilitate RI. To accomplish (i), we generate a high-resolution Weather Research and Forecasting (WRF) model Control (CTL) simulation of Hurricane Joaquin (2015) that reproduces its looping track and intensification trends. Comparing CTL forecast fields against sensitivity WRF simulations initialized from perturbed analyses and against two representative GFS forecasts, we find that a sufficiently strong mid-to-upper level ridge northwest of Joaquin and a vortex sufficiently deep to interact with northeasterly geostrophic flows surrounding the ridge are both necessary for steering Joaquin southwestward. These results suggest that more accurate track forecasts for TCs developing in vertically sheared environments may be at least partly contingent on improved vortex initialization; for these cases, assimilation of more inner-core observations such as cloudy radiances and airborne radar-derived winds could be particularly beneficial. We address (ii) by comparing parcel traces, thermodynamic variables, and vertical accelerations along trajectories run through CB updraft cores with trajectories representative of the background eyewall ascent in a Hurricane Wilma (2005) WRF simulation. We compute three-dimensional trajectories from WRF-output winds using a model developed for this study that implements an experimental advection correction algorithm designed to reduce time interpolation errors, with the latter confirmed by tests on analytical and numerically-simulated flows. Results show that Wilma’s CBs are characterized by significant thermal buoyancy, particularly in the upper troposphere; this is consistent with their lower environmental air entrainment rates and reduced midlevel hydrometeor loading relative to the background ascent, and with their updrafts being rooted in portions of the boundary layer where ocean surface heat and moisture fluxes are locally higher.
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    IMPROVING U.S. EXTREME PRECIPITATION PREDICTION AND PROCESS UNDERSTANDING USING A MESOSCALE CLIMATE MODEL MULTI-PHYSICS ENSEMBLE APPROACH
    (2019) sun, chao; Liang, Xin-zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Despite many recent improvements, climate models continue to poorly simulate extreme precipitation. I attempted to improve prediction of extreme precipitation, focusing on daily 95th percentile (P95) events, and to better understand the source of model biases in three ways: 1) determine which physics processes P95 is most sensitive to and which parameterization schemes best represent these processes; 2) understand the underlying mechanisms through which these processes impact P95; and 3) maximize advantages from the ensemble of the best performing models. First, to determine the sensitive processes affecting P95, I tested a 25-member ensemble of different physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF) for 36-yr historical U.S. simulations. Of these, P95 simulation was most sensitive to cumulus parameterization. Overall, the ensemble cumulus parameterization best represented P95 seasonal mean spatial patterns and interannual variations, while one traditional cumulus scheme generally overestimated P95 and the other three severely underestimated P95, especially over the Gulf States (GS) and the Central-Midwest States (CM) in convection-dominated seasons. Second, I built structural equation models (SEMs) to identify the underlying processes through which cumulus parameterization affects precipitation. I discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings. The relative importance of these factors varied significantly by season and region. For example, water supply is the dominant factor for P95 in CM, but its effect reversed from positive in summer to negative in winter due to changes in the prevailing precipitation system. In contrast, the predominant factors affecting P95 in GS were cloud forcing in summer, but surface forcing in winter. Since the choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, it determined CWRF’s ability to simulate extreme precipitation. Third, I improved P95 prediction by developing an optimized multi-model ensemble based on the Bayesian Model Averaging (BMA) approach. BMA is a model-selection method that weights ensemble members to create an optimal composite. However, many BMA methods rely on maximum likelihood estimation and thus may be flawed when the true solution is not among the ensemble, as is the case in extreme precipitation. To resolve this issue, I adapted three BMA variations to fit the needs of extreme precipitation problems. These methods significantly improved performance compared to both the ensemble mean and the single best model and provided a more reliable confidence interval. My work shows that to improve extreme precipitation simulation, a better understanding of physics processes, especially cumulus processes, is critical. For this, I applied the SEM framework, for the first time in the climate community, to uncover the underlying physical mechanisms essential to regional extreme precipitation predictions. Furthermore, I adapted new BMA methods into extreme precipitation ensembles to maximize the benefits from the most physically advanced models. These advances may help improve the prediction of extreme precipitation occurrences and future changes, one of the most difficult modeling challenges and one with huge socioeconomic significance.
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    IMPACTS OF AEROSOL ON CONVECTIVE STORMS AND PRECIPITATION
    (2019) Zhang, Yuwei; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Aerosol-cloud interactions (ACI) remain the largest uncertainty in projections of its future changes in climate in response to the buildup of greenhouse gases, even though they have been extensively investigated. Convective clouds have complicated dynamics and microphysics, and aerosol effects on them are the least understood of any cloud type. This study aims to further our understanding of aerosol effects on convective clouds by tackling a few outstanding problems by means of observational data analysis and model simulations under a wide-range of environmental conditions. There are three primary objectives: (1) to investigate the impact of ultra-fine aerosol particles from the Manaus metropolis on convective clouds under the pristine environment of Amazon; (2) to explore and quantify the urbanization effect on convective storms over the Houston area where the anthropogenic effects of both land surface and aerosols are exceptionally strong; (3) to examine and compare the relative significances of fire-induced surface heating and aerosol effects on exceptionally deep convective clouds, or pyroCb. Ultrafine aerosol particles smaller than 50 nanometers (UAP<50) are abundant in the troposphere but have been conventionally considered too small to be activated as cloud condensation nuclei (CCN) to affect cloud formation. Observational evidence and numerical simulations of deep convective clouds (DCCs) over the Amazon show that DCCs forming in a low-aerosol environment can develop very large water vapor supersaturation. This is because fast droplet coalescence reduces integrated droplet surface area and subsequent condensation. UAP<50 from pollution plumes that are ingested into such clouds can be activated to form additional cloud droplets on which water condenses and forms additional cloud water and latent heating, thus intensifying convective strength. This “warm-phase invigoration” is demonstrated to have much stronger effects than the “cold-phase invigoration” previously proposed and does not affect the timing of precipitation because warm rain needs to form first to remove droplets and form high in-cloud supersaturation. Urbanization has local impacts on storms through changing urban land-cover and anthropogenic aerosols. The Chemistry version of Weather Research and Forecast model (WRF‐Chem) coupled with spectral‐bin microphysics (SBM) are first employed to examine how urban land and anthropogenic aerosols impact DCCs on 19-20 June 2013 over Houston. We find that urbanization in Houston drastically enhances convective intensity and precipitation, primarily due to the urban aerosol effects. Urban land effect does not change precipitation much but initiates mixed-phase clouds 20 min earlier due to urban heating. Urban aerosols accelerate the development of convective cells into ice phase clouds, resulting from larger latent heat release. With the Morrison bulk scheme, the model does not show significant aerosol impacts on convective intensity and precipitation, due to limitations in representation of aerosol-cloud interaction processes, particularly aerosol drop condensation. Wildfires can influence severe convective storms through releasing sensible heat and aerosols into the atmosphere. We developed a computationally efficient model capability based on WRF-Chem that can account for the impact of sensible heat fluxes from wildfires on thermodynamics. The model is used to investigate how the Texas Mallard Fire on 11-12 May 2018 led to the development of pyrocumulonimbus (pyroCb) clouds that are well simulated by accounting for both the effects of heat and aerosols emitted from the wildfire. Both heat and aerosol effects increase low-level temperatures and mid-level buoyancy and enhance convective intensity. Intensified convection along with more supercooled liquid condensate at high altitudes due to stronger vertical transport, results in larger hailstones and enhanced lightning. The effects of heat flux on the extreme convection are more significant than those of aerosol emissions. This is on the contrary to the effect of urbanization in Houston for which the effect of land surface change is smaller than that of aerosols, presumably because heat from fire is much more intensive than that from the urban heat island effect.
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    Covariance Localization in Strongly Coupled Data Assimilation
    (2019) Yoshida, Takuma; Kalnay, Eugenia; Penny, Stephen G; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The recent development of accurate coupled models of the Earth system and enhanced computation power have enabled numerical prediction with the coupled models in weather, sub-seasonal, seasonal, and interannual time scales as well as climate projection. In the shorter timescales, the initial condition, or the estimate of the present state of the system, is essential for accurate prediction. Coupled data assimilation (DA) based on an ensemble of forecasts seems to be a promising approach for this state estimate due to its inherent ability to estimate flow-dependent error covariance. Strongly coupled DA tries to incorporate more observations of the other subsystems into an analysis (e.g., ocean observations into the atmospheric analysis) using the coupled error covariances; the covariance is estimated with a finite ensemble, and spurious covariance must be eliminated by localization. Because the coupling strength between subsystems of the Earth is not a simple function of a distance, we develop a better localization strategy than the distance-dependent localization. Based on the estimated benefit of each observation into each analysis variable, we first propose the correlation-cutoff method, where localization of strongly coupled DA is guided by ensemble correlations of an offline DA cycle. The method achieves improved analysis accuracy when tested with a simple coupled model of the atmosphere and ocean. As a related topic, error growth and predictability of a coupled dynamical system with multiple timescales are explored using a simple chaotic model of the atmosphere and ocean. A discontinuous response of the attractor's characteristics to the coupling strength is reported. The characteristic of global atmosphere-ocean coupled error correlation is investigated using two sets of ensemble DA systems. This knowledge is essential for effectively implementing global strongly coupled atmosphere-ocean DA. We report and discuss common and uncommon features, and the importance of ocean model resolution is stressed. Finally, the correlation-cutoff method is realized for global atmosphere-ocean strongly coupled DA with neural networks. The combination of static information provided by the neural networks and flow-dependent error covariance estimated by the ensemble improves the atmospheric analysis in our proof-of-concept experiment. The neural networks' ability to reproduce the error statistics, computation cost in a DA system, as well as analysis quality are evaluated.
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    The role of consistent turbulence energetics in the representation of dry and shallow convection
    (2019) New, David Andrew; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this doctoral dissertation, the role of consistent turbulence energetics is examined in the context of sub-grid turbulence, convection, and cloud condensation parameterizations for numerical weather and climate models. The property of energetic consistency is formally defined and divided into two categories, local and non-local, and various common parameterization approaches are classified according this framework. I show theoretically that the basis of local energetic consistency is the inclusion of mean-gradient transport and buoyancy acceleration terms in the diagnostic and prognostic budget equations of all second-order statistical moments, including fluxes. Effectively, these terms account for the conversion between turbulent kinetic energy (TKE) and turbulent potential energy (TPE) under stably stratified conditions. With simple numerical experiments, I show that if local energetic consistency is not satisfied, then thermodynamic profiles cannot be correctly predicted under stably conditions, such as in the boundary layer capping inversion. I then extend the concept of energetic consistency from local turbulent mixing to non-local convective transport. I show that the popular eddy diffusivity-mass flux (EDMF) approach for unified parameterization of turbulence and convection treats the turbulent transport of turbulent energy in two parallel but inconsistent ways: advectively and diffusively. I introduce a novel parameterization approach, inspired by EDMF, that consistently partitions all second-order moments, including TKE, between convective and non-convective parts of a grid cell and show that this approach predicts significantly more realistic depths of convective boundary layers than conventional EDMF schemes. Finally, I introduce a novel method for validating this parameterization approach, based on Langragian particle tracking in large-eddy simulations.
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    Socioeconomic and Climate Impacts on the Future of Water: An Integrated Assessment Approach to Demand, Scarcity, and Trade
    (2019) Graham, Neal Thornton; Miralles-Wilhelm, Fernando; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Changes to socioeconomics and an evolving climate system are likely to play a vital role in how regions around the world use water into the future. Water projections for the future, while prolific, remain highly variable and dependent upon underlying scenario and model assumptions. In this study, the Global Change Assessment Model (GCAM) is used, where interactions between population, economic growth, energy, land, water, and climate systems interact dynamically within a market equilibrium economic modeling framework, to address how changing socioeconomic and climate conditions alter global water futures, and in turn, how water constrains the future of other systems. First, the impacts of efficiency changes are investigated with the addition of socioeconomically consistent water technologies across several sectors. Quantitative assumptions for the Shared Socioeconomic Pathways are extended to the water sector for the first time in a water constrained – Integrated Assessment Modeling framework. It is found that significant water use reductions are possible under certain socioeconomic conditions, provided the ability to adopt appropriate technological advances in lower income regions. Secondly, the relative contributions of climate and human systems on water scarcity are analyzed at global and basin scales under the Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP-RCP) framework. Ninety scenarios are explored to determine how the coevolution of energy-water-land systems affects not only the driver behind water scarcity changes in different water basins, but how human and climate systems interact in tandem to alter water scarcity. Human systems are found to dominate water scarcity changes into the future, regardless of socioeconomic or climate future. However, the sign of these changes has a significant scenario dependence, with an increased number of basins experiencing improving water scarcity conditions due to human interventions in the sustainability focused scenario. Finally, the reliance on international agricultural trade is analyzed to understand how future socioeconomic growth and climatic change will impact the dependency on international water sources. The differentiation between renewable and nonrenewable water sources allow for the quantification of the various water sources needed to produce enough agricultural goods to meet global demands. The first Integrated Assessment Model projection of the evolution of external water sources to meet domestic agricultural demands show that there will be an increasing international dependencies. China, the United States, and portions of South America are pivotal in providing the necessary exports to meet demands in water scarce or high demand areas of the Middle East and Africa.
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    WINTER STORM TRACKS, RELATED WEATHER, AND SUBSEASONAL-TO-SEASONAL (S2S) PREDICTION IN THE NCEP CLIMATE FORECAST SYSTEM FOR NORTH AMERICA
    (2019) Lukens, Katherine Elizabeth; Berbery, Ernesto H; Ide, Kayo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The subseasonal-to-seasonal (S2S) forecast period (2 weeks—2 months) represents a major gap in operational forecasting. Advancing S2S prediction is an international priority, particularly for disaster mitigation and resource management decisions. If storm tracks contain S2S signals, their characterization in long term forecasts could advance S2S prediction by providing important information at longer lead times that may not be acquired from standard wind and precipitation forecasts. Potential damaging effects of Northern Hemisphere winter storm tracks on North American weather are investigated using the NCEP Climate Forecast System (CFS) reanalysis (CFSR). Storm tracks are described by objectively tracking 320-K isentropic potential vorticity anomalies (PV320). Large increases in deep convective heating, near-surface winds, and precipitation are found where strong storms (those with higher PV320) are most intense. The eastern US and North American coasts are most vulnerable to strong-storm related losses, which depend on the dynamics and local population density. Despite representing a small fraction (16%) of all storms, strong-storm tracks have a significant imprint on winter weather potentially leading to structural/economic loss. Storm tracks in weeks 3-4 CFS reforecasts (CFSRR) are examined to assess their potential use in S2S prediction. Removal of statistically significant positive biases in PV320 storm intensity improves general storm track features. CFSRR reproduces observed storm-related weather and the characteristic intensity/frequency of hazardous strong-storm winds. Bias-corrected reforecasts better depict the observed variability in storm-related weather. CFSRR contains useful storm track-related information supporting our hypothesis that storm track statistics contribute to the advancement of S2S prediction of hazardous weather in North America. The weeks 3-4 CFS version 2 (CFSv2) operational forecast performance is evaluated from a storm-focused perspective. CFSv2 retains the ability to predict general storm track behavior. Significant negative biases in storm intensity are apparently driven by mean static stability, with relative vorticity being a secondary driver. CFSv2 partially encapsulates the variability in storm winds and generally reproduces more extreme precipitation observations. Bias corrections improve storm wind forecasts. This work demonstrates that the use of climatological PV storm track statistics coupled with an appropriate storm track bias correction is a powerful instrument for the advancement of S2S prediction.