Includes bibliographical references / Uncertainty in climate system initial conditions (ICs) is known to limit the predictability of future atmospheric states. On weather time scales (i.e. hours to days), the separation between two atmospheric model trajectories, initially "indistinguishable" (compared to unavoidable uncertainties) from one another, diverges exponentially-on-average over time, so that the "memory" of model ICs is eventually lost. In other words, there is a theoretical limit in the lead time for skilful weather forecasts. However, the influence of perturbations to climate system model ICs - particularly in more slowly evolving climate system components (e.g., the oceans and ice sheets) - on the evolution of model "climates" on longer time scales is less well understood. Hence, in order to better understand the role of IC uncertainty in climate predictability, particularly in the context of climate change, it is necessary to develop approaches for investigating and quantifying - at various spatial and temporal scales - the nature of the influence of ICs on the evolution of climate system trajectories. To this end, this study explores different conceptualisations and competing definitions of climate and the climate system, focussing on the role of ICs. The influence of ICs on climate quantifications, using probability distributions, is subsequently investigated in a climate model experiments using a low-resolution version of the Community Climate System Model version 4 (CCSM4). The model experiment consists of 11 different 50-member ensemble simulations with constant forcing, and three 50-member ensemble simulations under a climate change scenario with transient forcing. By analysing the output at global and regional scales, at least three distinct levels of IC influence are detected: (a) microscopic influence; (b) interannual-scale influence; and (c) intercentennial-scale influence. Distinct patterns of interannual-scale IC influence appear to be attributable to aperiodic and quasi-periodic variability in the model. It is found that, over some spatial domains, significant (p < 0.01) differences in atmospheric variable "climatologies", taken from 60-year distributions of model trajectories, occur due to IC differences of a similar order to round-off error. In addition, climate distributions constructed using different approaches are found to differ significantly. There is some evidence that ensemble distributions of multidecadal temperature response to transient forcing conditions can be influenced by ICs. The implications for quantifying and conceptualising climate are considered in the context of the experimental results. It is concluded that IC ensemble experiments can play a valuable role in better understanding climate variability and change, as well as allowing for superior quantification of model climates.

This study examines the relationship between African Easterly Waves (AEWs) and extreme rainfall events over West Africa, and investigates how climate change could alter this relationship in the future. Satellite observations, reanalysis data, and regional climate model (RCA4) simulations (forced with eight global climate simulations) were analysed for the study. The study used the 95th percentile of daily rainfall as a threshold to identify extreme rainfall events, and applied spectral analysis to extract 3-5 days and 6-9 days AEWs from 700hPa meridional wind component over West Africa. The capability of RCA4 to reproduce the rainfall climatology, extreme rainfall events, the characteristics of AEWs and the contribution of AEWs to extreme rainfall events over the region during the past climate (1971-2005) was examined and quantified using statistical analysis. The future changes (2031-2065) in these parameters were projected for the RCP4.5 and RCP8.5 climate-change scenarios. The results of the study show that RCA4 gives a realistic simulation of the West African climate, including the annual rainfall pattern, the structure of AEWs, and the characteristics of the African Easterly Jet that feeds AEWs. The bias in the simulated threshold of extreme rainfall is within the uncertainty of the observed values. The model also captures the link between the structure of AEWs and the rainfall pattern over West Africa, and shows that the percentage contribution of AEWs to extreme rainfall events over the region ranges from 20 to 60%, as depicted by reanalysis data. For the RCP4.5 and RCP8.5 scenarios, the RCA4 ensemble mean projects a future increase in annual rainfall and in the frequency and intensity of extreme rainfall events over the sub-continent, but the increase is generally higher for the RCP8.5 scenario. It also projects a decrease in the frequency of rain days, no changes in the structure of the AEWs, and an increase in the variance of the waves. However, the simulations from the ensemble mean shows no substantial changes in the contribution of AEWs to the extreme rainfall events, suggesting that the increase in the frequency and intensity of the extreme rainfall events may not be attributable to the changes in AEWs. The study's application is in understanding and mitigating the future impact of climate extremes over West Africa.

abstract: Population growth, social interaction, and environmental variability are interrelated facets of the same complex system. Tracing the flow of food, water, information, and energy within these social-ecological systems is essential for understanding their long-term behavior. Leveraging an archaeological perspective of how past societies coevolved with their natural environments will be critical to anticipating the impact of impending climate change on farming communities in the developing world. However, there is currently a lack of formal, quantitative theory rooted in first principles of human behavior that can predict the empirical regularities of the archaeological record in semiarid regions. Through a series of models -- statistical, computational, and mathematical -- and empirical data from two long-term archaeological case studies in the pre-Hispanic American Southwest and Roman North Africa, I explore the feedbacks between population growth and social interaction in water-limited agrarian societies. First, I use a statistical model to analyze a database of 7.5 million artifacts collected from nearly 500 archaeological sites in the Southwest and found that sites located in different climatic zones were more likely to interact with one another than a sites occupying the same zone. Next, I develop a computational model of demography and food production in ancient agrarian societies and, using North Africa as a motivating example, show how the concrete actions and interactions of millions of individual people lead to emergent patterns of population growth and stability. Finally, I build a simple mathematical model of trade and migration among agricultural settlements to determine how the relative costs and benefits of social interaction drive population growth and shape long-term settlement patterns. Together, these studies form the foundation for a unified quantitative approach to regional social-ecological systems. By combining theory and methods from ecology, geography, and climate science, archaeologists can better leverage insights from diverse times and places to fill critical knowledge gaps in the study of food security and sustainability in the drylands of today. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2019

Forests provide important ecosystem services and play a dominant role in the global carbon and hydrologic cycles. These ecosystems are becoming more vulnerable to climate change-related threats such as extreme temperature and precipitation events, drought and wildfires. In addition, forest ecosystems have also undergone land use changes and a significant reduction in cover area, specifically in North America. There has been renewed realization to restore and rehabilitate forest ecosystems because they are a major carbon sink and play a key role in sequestering atmospheric carbon dioxide. In response, plantation forests are being widely established to sequester carbon, increase biodiversity, secure water resources and generate economic revenue when harvested. Forest managers employ different management practices such as thinning or retention harvesting to enhance growth, plant structural and species diversity within forest plantations, with the ultimate goal of emulating the characteristics and benefits of natural forests. However, the influence of these forest management practices on the growth, productivity and specifically water cycling in plantation forests is not well studied and reported in the literature. This experimental study investigated the effect of four different variable retention harvesting (VRH) treatments on evapotranspiration and water balance in an 83-year-old red pine (Pinus resinosa) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. Tree-level experimental work was conducted in the control plot and showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively. These results help to avoid overestimation of transpiration, which may cause large uncertainties in water budgets in pine forests. Study results further showed that the 55D treatment had the highest tree-level transpiration followed by 33D, 55A, 33A and CN plots. During periods of low precipitation, vapor pressure deficit (VPD) was the main driver or control on transpiration in VRH treatments. However, transpiration was more closely coupled with photosynthetically active radiation (PAR) in the control plot. Moreover, the 55D treatment resulted in on average 58% of total water loss from canopy as transpiration and 42% from the understory and ground surface as evapotranspiration. These findings suggest that dispersed or distributed retention of 55% basal area (55D) provides the optimal environmental conditions for forest growth with reduced competition of trees for water as shown by enhanced transpiration. This study will help researchers, forest managers and decision-makers to improve their understanding of thinning impacts on water and carbon exchanges in forest ecosystems and select and adopt viable forest management practices to enhance their carbon sequestration capabilities, water use efficiency and resilience to climate change. / Thesis / Doctor of Philosophy (PhD)

No description available.

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

No description available.

Created in 2018, the Climate Fresk workshop aims to raise awareness on climate change issues in an engaging and accessible way. This thesis draws on Social Interactionism, Uses and Gratification and Sociocultural Theory of Learning to conceptualize the workshop’s communicative strategies for conveying the complexities of climate change to the workshop participants. Through a synthetization of insights from various disciplines and exploration of innovative communication strategies, the study bridges the gap between knowledge and action, contributing to the semi-underexplored field of communication roles in game-based learning approaches to climate change education. Key findings reveal that tools such as informational materials, visuals, simplified scientific data, and creative techniques significantly enhance participants' understanding of climate change. The emphasis on group autonomy and collective discussions fosters a deeper understanding of climate change's complexities, highlighting the necessity for collective action. These results underscore the potential of effective communication in educational workshops to inspire individuals and communities to proactively respond to climate change, through collaborative action and by shifting the focus from individual to broader community-based efforts.

<p> The population and industrial complexes of Choctaw County obtains much of its water from an aquifer system in the Tertiary age Wilcox unit of the Mississippi Embayment. Utilizing 20 years of physical chemistry (P-Chem) analysis, potentiometric groundwater records of Choctaw County public water wells as well as industrial P-Chem analysis and surface and ground water level records from an industrial complex, this study examined the changes to the hydrosphere that has taken place since 1995. Analysis of the hydrosphere shows that over the last 20 years, there has been a drop in the potentiometric surface of the Wilcox aquifer system. The analysis also shows changes in the P-Chem of the hydrosphere, changes such as a decrease in the concentration of free CO2 and chloride, and fluctuations of Alkalinity. Comparisons between groundwater records taken from the industrial complex and other locations around Choctaw County, show little variation in the physical chemistry.</p>

A widely observed tracer in the field of oceanography is dissolved oxygen (O2). A tracer crucial to ocean biogeochemical cycles, O2 plays an active role in chemical processes, marine life, and ecosystems. Recent advances in observation and numerical simulation have introduced opportunities for furthering our understanding of the variability and long-term changes in oceanic O2. This work examines the underlying mechanisms driving O2 variability and long-term changes. It focuses on two distinct time-scales: intra-seasonal variability (i.e., a time scale of less than a month) and centennial changes in O2. The first half of this work analyzes state-of-the-art observations from a profiling float in an investigation of the mechanisms driving the intra-seasonal variability of oceanic O2. Observations from the float show enhanced intra-seasonal variability (i.e., a time scale of about two weeks) that could be driven by isopycnal heaving resulting from internal waves or tidal processes. Observed signals could result from aliased signals from internal waves or tides and should be taken into account in analyses of the growing observational dataset. The methods proposed in this study may be useful for future analyses of high-frequency tracer variability associated with mesoscale and sub-mesoscale processes. Using outputs from state-of-the-art earth system models and a suite of sensitivity experiments based on a general circulation and biogeochemistry ocean model, the second half of this work focuses on investigating mechanisms regulating centennial changes in O2. It explores the aspect of anthropogenic climate change (e.g., changes in the sea surface temperature and wind stress fields) that significantly impacts oceanic O2, focusing specifically on tropical oxygen minimum zones. Results suggest that ocean heating induces a water mass shift, leads to decrease apparent oxygen utilization (AOU) in the tropical thermocline. The AOU decrease compensates the effect of decrease in oxygen saturation due to the ocean warming. Our sensitivity experiments show that both physically (i.e., age) and biologically (i.e., the oxygen utilization rate) driven AOU will contribute almost equally to controlling changes in oceanic O2 in the next century. However, additional sensitivity experiments indicate that physically and biologically driven AOU balance has regional characteristics. We need to address the unanswered question of how varying large-scale oceanic circulations regulate this balance and answer fundamental questions that lead to a more comprehensive understanding of the mechanisms that control the variability and the future evolution of oceanic O2.