Atmospheric & Oceanic Science
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Formerly known as the Department of Meteorology.
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Item The Role of Soil Hydro-physical Properties in Land-atmosphere Interactions and Regional Climate(2021) Dennis, Eli; Berbery, Ernesto H; Kalnay, Eugenia E; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Soil hydro-physical properties are necessary components in weather and climate simulation; yet, the parameter inaccuracies introduce considerable uncertainty in the representation of surface water and energy fluxes. The surface fluxes not only affect the terrestrial water and energy budgets, but through land-atmosphere interactions, they can influence the boundary layer, atmospheric stability, moisture transports, and regional precipitation characteristics. This set of three experiments explores aspects of soil hydro-physical properties, and their impact on coupled regional climate simulations in the North American region. In the first two experiments, two soil datasets are considered: State Soil Geographic dataset (STATSGO) and Global Soil Dataset for Earth System Modeling (GSDE). Each dataset’s dominant soil category allocations differ significantly at the model’s resolution. Large regional discrepancies exist in the assignments of soil category, such that, for instance, in the Midwestern United States, there is a systematic reduction in soil grain size. Because the soil grain size is regionally biased, it allows for analysis of the impact of soil hydro-physical properties projected onto regional scales. In the first experiment, in areas of reduced soil grain size, there is also a reduction in latent heat flux and an increase in sensible heat flux following the physical understanding of soil properties. These differences in surface fluxes affected low-level thermodynamics, and PBLH. The second experiment analyzed soil-induced differences in the general circulation, emphasizing horizontal moisture transports, vertically-integrated moisture flux convergence, and regional precipitation. It found that soil-induced differences in surface fluxes influenced each term of the atmospheric water budget via both thermodynamic and dynamic means. The third experiment assesses the impact of soil hydro-physical parameters on surface fluxes, and the atmospheric response. The default soil hydro-physical parameter table is replaced with a modernized soil parameter table. The findings indicate that the role of each soil hydro-physical parameter is sensitive to both climatic regimes (i.e., arid vs. temperate), and vegetation assignment. Collectively, this series of experiments improves our understanding of the physical mechanisms that link the soil to the atmosphere in the coupled land-atmosphere system. The improved understanding will inform the development of the next generation of land surface models.Item Satellite Remote Sensing of Smoke Particle Optical Properties, Their Evolution and Controlling Factors(2021) Junghenn, Katherine Teresa; Li, Zhanqing; Kahn, Ralph A.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The optical and chemical properties of biomass burning (BB) smoke particles greatly affect the impact wildfires have on climate and air quality. Previous work has demonstrated some links between smoke properties and factors such as fuel type and meteorology. However, the factors controlling BB particle speciation at emission are not adequately understood, nor are those driving particle aging during atmospheric transport. As such, modeling wildfire smoke impacts on climate and air quality remains challenging. The potential to provide robust, statistical characterizations of BB particles based on ecosystem and ambient conditions with remote sensing data is investigated here. Space-based Multi-angle Imaging Spectrometer (MISR) observations, combined with the MISR Research Aerosol (RA) algorithm and the MISR Interactive Explorer (MINX) tool, are used to retrieve smoke plume aerosol optical depth (AOD), and to provide constraints on plume vertical extent, smoke age, and particle size, shape, light-absorption, and absorption spectral dependence. These capabilities are evaluated using near-coincident in situ data from two aircraft field campaigns. Results indicate that the satellite retrievals successfully map particle-type distributions, and that the observed trends in retrieved particle size and light-absorption can be reliably attributed to aging processes such as gravitational settling, oxidation, secondary particle formation, and condensational growth. The remote-sensing methods are then applied to numerous wildfire plumes in Canada and Alaska that are not constrained by field observations. For these plumes, satellite measurements of fire radiative power and land cover characteristics are also collected, as well as short-term meteorological data and drought index. We find statistically significant differences in the retrieved smoke properties based on land cover type, with fires in forests producing the tallest and thickest plumes containing the largest, brightest particles, and fires in savannas and grasslands exhibiting the opposite. Additionally, the inferred dominant aging mechanisms and the timescales over which they occur vary between land types. This work demonstrates the potential of remote sensing to constrain BB particle properties and the mechanisms governing their evolution, over entire ecosystems. It also begins to realize this potential, as a means of improving regional and global climate and air quality modeling in a rapidly changing world.Item Quantification of the Past and Future Anthropogenic Effect on Climate Change Using the Empirical Model of Global Climate, an Energy Balance Multiple Linear Regression Model(2020) Hope, Austin Patrick; Salawitch, Ross J; Canty, Timothy P; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The current episode of global warming is one of, if not the, biggest challenge to modern society as the world moves into the 21st century. Rising global temperatures due to anthropogenic emissions of greenhouse gases are causing sea level rise, extreme heat waves, droughts and floods, and other major social and economic disruptions. To prepare for and potentially reverse this warming trend, the causes of climate change must not only be understood, but thoroughly quantified so that we can attempt to make reasonable predictions of the future rise in global temperature and its associated consequences. The project described in this dissertation seeks to use a simple model of global climate, utilizing an energy balance and multiple linear regression approach, to provide a quantification of historical temperature trends and use that knowledge to provide probabilistic projections of future temperature. By considering many different greenhouse gas and aerosol emissions scenarios along with multiple possibilities for the role of the ocean in the climate system and the extent of climate feedbacks, I have determined that there is a 50% probability of keeping global warming beneath 2 °C if society can keep future emissions on the pathway suggested by the RCP 4.5 scenario, which includes moderately ambitious emissions reductions policies, and a 67% probability of keeping global warming beneath 1.5 °C if society can keep emissions in line with the very ambitious RCP 2.6 scenario. These probabilities are higher, e.g. more optimistic, than similar probabilities for the same scenarios given by the most recent IPCC assessment report. Similarly, we find larger carbon budgets than those from GCM analyses for any warming limitation target and confidence level, e.g. the EM-GC predicts a total carbon budget of 710 GtC for limiting global warming to 1.5 °C with 95% confidence. The results from our simple climate model suggest that the difference in future temperatures is related to an overestimation of recent warming by the IPCC global climate models. We postulate that this difference is partially due to an overestimation of cloud feedback processes in the global climate models. Importantly, though, I also reaffirm the consensus that anthropogenic emissions are driving current warming trends, and discuss both the effects of shifting the energy sector toward increase methane emissions and the timeline we have for emitting the remainder of our carbon budget – less than a decade if we wish to prevent global warming from exceeding the 1.5 °C threshold with 95% certainty.Item DECADAL TO CENTENNIAL SCALE CLIMATE DYNAMICS IN MODELS OF VARYING COMPLEXITY(2020) Schwarber, Adria; Smith, Steven J; Hartin, Corinne A; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Though concerted climate action by the world's governments intends to limit long-term (e.g. 100 years) global average temperature rise, attention has recently focused on reducing climate impacts in our lifetime by reducing emissions of short-lived climate forcers (SLCFs). SLCFs are pollutants that remain in the atmosphere for a short time (e.g. methane or black carbon) and have the potential to impact the climate in the near-term by increasing or decreasing temperature, depending on the species emitted. There is a more limited set of literature, however, that robustly characterizes short-term climate dynamics in the 20-30 year time horizon within models or observations that can be used to inform scientific and policy work. In this dissertation, we seek to clarify climate dynamics on shorter time scales using models of varying complexity---from complex models, which take several months to simulate 100 years of climate on a supercomputer, to simple climate models (SCMs) that can simulate the same period on a personal computer in less than a minute, in addition to using several observational datasets. We first characterize the basic climate processes within several SCMs, finding that some comprehensive SCMs fail to capture response timescales of more complex models, for example under BC forcing perturbations. These results suggest where improvements should be made to SCMs, which affect numerous scientific endeavors and illustrates the necessity of integrating fundamental tests into SCM development. We then robustly determine how realistic complex model variability is compared to observations across all time scales using power spectra of temperature-time series. We investigate model variability at the regional level, using the continental-scale regions defined by PAGES2k. We find that compared to observations the suite of CMIP5 models investigated have lower variability in certain regions (e.g. Antarctica) and higher variability in others (e.g., Australasia), with some consistency across timescales. Our approach allows for a more robust assessment of complex model variability at time periods and regional levels important to human systems. From this, we analyze the range of temperature responses over time in complex model results from phase 5 of the Coupled Model Intercomparison Project (CMIP5) at the hemispheric scale to create a realistic range of possible temperature changes. We find that the range of responses of land/ocean varied less than the range of hemispheric responses. Our results are a first step of better quantifying the short-term climate responses to changes in SLCFs.Item 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.Item 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.Item ON THE ORIGIN OF HYDROCLIMATE CHANGE OVER CONTINENTS THROUGH SEASONALLY-STRATIFIED TRENDS(2019) Thomas, Natalie Paige; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global hydroclimate has undergone significant changes over the twentieth century. Temperature and precipitation changes have not been uniform in space or time and mechanisms driving many continental changes remain to be understood. Annual-mean changes are the most often cited; in this dissertation, trends in hydroclimate variables (specifically, temperature and precipitation) are examined seasonally to gain physical insights. Century-scale seasonal hydroclimate trends are also used as a new metric for evaluation of a subset of leading global climate models informing the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPCC-AR5). Surface air temperature (SAT) trends over Northern Hemisphere continental regions are examined first. Warming over the twentieth century is found to exhibit striking seasonality; it is strong in winter and spring and muted in summer and fall. Examined climate models are unable to reproduce the observed SAT trend seasonality. Two potential mechanisms are explored for explanation of the observed temperature trend seasonality; one relating to changes in winter circulation and one based on summer changes in land-surface-hydroclimate interactions. To further probe the causes of temperature trend seasonality at the surface, seasonal trends in upper-air temperatures are examined from radiosonde data sets and global reanalysis. It is found that the temperature trend seasonality is greatest at the surface and decreases gradually through the troposphere. The seasonality resumes in the stratosphere. Seasonal twentieth century hydroclimate trends are next characterized over the African continent. Examination of trends in SAT reveals that heat stress has increased in several regions, including Sudan and northern Africa, where greatest trends occur in the warm season. Precipitation trends are varied but notable declining trends are found in the countries along the Gulf of Guinea. Using a precipitation-based threshold, it is shown that the area of the Sahara Desert expanded significantly over the twentieth century, by 11-18% depending on the season, and by 10% when defined using annual-mean rainfall. Evaluation of climate models reveals difficulty in simulating seasonal century-scale hydroclimate trends over Africa. These studies together offer support for the use of the seasonal perspective when analyzing changes of the past century, both for its relevance to society and for the potential to gain new process-level insights.Item EL NIÑO SOUTHERN OSCILLATION AND RELATED PRECIPITATION IN RECENT ATMOSPHERIC REANALYSES AND CMIP5 MODEL SIMULATIONS(2018) Dai, Ni; Arkin, Phillip A; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The El Niño-Southern Oscillation (ENSO), originating in the tropical Pacific, is the most significant mode of interannual variability of the ocean-atmosphere system. ENSO can modulate global climate through teleconnections with significant socioeconomic consequences, especially in the Tropics and along the western coastline of the Americas. It is thus important for the general circulation models (GCMs) of the oceans and atmosphere to simulate ENSO and its regional hydroclimate impacts with some fidelity. Although our understanding of the ENSO structure and dynamics has improved in the past few decades, its modeling remains challenging. Analysis of climate simulations produced by the Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs and long-term global precipitation datasets as well as recent high-resolution atmospheric reanalyses provides insights on improving the ENSO simulation as well as the recent and projected ENSO-related changes under global warming. A classification of CMIP5 models into two groups is developed on the basis of pattern correlation of the precipitation climatology and the ENSO-related precipitation anomalies with their counterparts in the 20th Century Reanalysis (20CR) and a statistically reconstructed precipitation dataset (REC). ENSO-related diabatic heating, atmospheric circulations, and air-sea interaction in the two model groups are then assessed using the state-of-the-art high-resolution atmospheric reanalysis, ERA-Interim, whose representation of tropical diabatic heating is considered optimal. The better performing model group simulates the ENSO-related features well, while the underperforming group exhibits severe biases, including deficient equatorial precipitation in both climatology and ENSO precipitation anomalies. This group also simulates a more westward-located and less robust ENSO precipitation/diabatic heating anomaly center together with weaker associated Walker and Hadley circulations and air-sea interaction compared to the better performing group. Regarding multidecadal and centennial change in ENSO variability during the 20th and 21st centuries, ENSO-related SST anomalies strengthened in the later part of the last century, while the changes in ENSO-related precipitation were diverse and included both zonal shift and intensification. The underperforming group of models exhibits a robust increase and zonal shift of ENSO-related precipitation, SST and diabatic heating in the 21st century. The other group shows an increase in ENSO precipitation in the central-eastern equatorial Pacific, with related intensification of diabatic heating anomalies in the mid-to-upper troposphere.Item Remote Sensing of Clouds and Precipitation: Event-based Characterization, Life Cycle Evolution, and Aerosol Influences(2016) Esmaili, Rebekah; Zeng, Ning; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global climate models, numerical weather prediction, and flood models rely on accurate satellite precipitation products, which are the only datasets that are continuous in time and space across the globe. While there are more earth observing satellites than ever before, gaps in precipitation retrievals exist due to sensor and orbital limitations of low-earth (LEO) satellites, which are overcome by merging data from different sensors in satellite precipitation products (SPPs). Using cloud tracking at higher resolutions than the spatio-temporal scales of LEO satellites, this thesis examines how clouds typically form in the atmosphere, the rate that cloud size and temperature evolve over the life cycle, and the time of day that cloud development take place. This thesis found that cloud evolution was non-linear, which disagrees with the linear interpolation schemes used in SPPs. Longer lasting clouds tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lasting clouds. Over the ocean, longer lasting clouds were found to occur more frequently at night, while shorter lasting clouds were more common during the daytime. This thesis also examines whether large-scale Saharan dust outbreaks can impact the trajectories and intensity of cloud clusters in the tropical Atlantic, which is predicted by modeling studies. The presented results show that proximity to Saharan dust outbreaks shifts Atlantic cloud development northward and intense storms becoming more common, whereas on days with low dust loading small-scale, warmer clouds are more common. A simplified view of cloud evolution in merged rainfall retrievals is a possible source of errors, which can propagate into higher level analysis. This thesis investigates the difference in the intensity, duration, and frequency of precipitation in IMERG, a next-generation satellite precipitation product with ground radar observations over the contiguous United States. There was agreement on seasonal totals, but closer examination shows that the average intensity and duration of events is too high, and too infrequent compared to events detected on the ground. Awareness of the strengths and limitations, particularly in context of high-resolution cloud development, can enhance SPPs and can complement climate model simulations.Item CHANGES IN THE SEASONAL AMPLITUDE OF ATMOSPHERIC CARBON DIOXIDE CONCENTRATION: CAUSES AND FUTURE PROJECTIONS(2015) Zhao, Fang; Zeng, Ning; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The atmospheric carbon dioxide (CO2) observations reveal a seasonal cycle that is dominated by the growth and decay of land vegetation. Ground-based and aircraft-based observations indicate that the amplitude of this seasonal cycle has increased over the past five decades, suggesting enhanced biosphere activity. Previous studies have tried to explain the amplitude increase with stimulated vegetation growth by higher concentrations of CO2 and warming, but the understanding of all the important mechanisms and their relative contribution is still lacking. This work comprises of three individual studies that contribute to better understanding of the CO2 amplitude increase over time and space. With improved crop simulation scheme in a terrestrial carbon model, a new mechanism—the intensive farming practices of the agricultural Green Revolution—is presented as a driver of changes in the seasonal features of the global carbon cycle. Results are further compared with eight other models’ simulations and a number of observation-based datasets on the seasonal characteristics of simulated carbon flux, and on the relative contribution of rising CO2, climate and land use/cover change. In addition, future projections on the amplitude change of CO2 seasonal cycle are examined using simulations from 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) earth system models. Results from this work demonstrate that human land-management activities are powerful enough to modify the basic seasonal characteristics of the biosphere, as reflected by atmospheric CO2. Models attribute 83±56%, −3±74% and 20±30% of global carbon flux amplitude increase to the CO2, climate and land use/cover factors, respectively. Additionally, the models’ underlying mechanisms for the simulated carbon flux amplitude increase in different regions are substantially different. Strong productivity increase under higher CO2 concentration is also seen in the CMIP5 models, leading to 62±19% global mean CO2 amplitude increase in 2081-2090 compared to 1961-1970. Both groups of models suggest that models simulating larger amplitude increase tend to show a larger gain in land carbon sink (with a cross-model R2 of ~0.5 in both cases). Overall, this work presents significant insights in the change of CO2 amplitude and model representation of global carbon cycle.