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Ogutu, Benjamin Keroboto Za'Ngoti. "Energy balance mathematical model on climate change." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066224/document.
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Dans cet étude, un modèle de complexité réduite des interactions et rétroactions du système couplé climat-économie-biosphère est construit avec le minimum de variables et d'équations nécessaires. Le Coupled Climate-Economy-Biosphere (CoCEB) est un modèle d’évaluation intégrée (IAM pour Integrated assessment model) du changement globale. Alors que beaucoup IAM traitent les coûts de réduction des émissions (abattement) simplement comme une perte non productive de revenu, cet étude considère également les activités d’abattement comme un investissement dans l'efficacité énergétique globale de l'économie et dans la diminution de l’ « intensité carbone » du système énergétique. L’étude montre que ces efforts aident à l’abattement du changement climatique et ont un effet positif sur l’économie. La plupart des IAM actuels se concentrent principalement sur le secteur énergétique pour les mesures d’abattement, et ne tiennent compte des émissions provenant de l'utilisation des terres que comme un forçage exogène. Le CoCEB a donc été étendu en ajoutant une équation pour la biomasse ses échanges de carbone. Cela permet d’étudier les aspects économiques de la séquestration de carbone du au contrôle du déboisement dans les forêts, et aussi à l’application généralisée des technologies de capture et stockage du carbone (CCS). L’étude confirme que ces mesures réduisent l’impact du changement climatique sur la croissance économique, mais ces résultats restent très dépendants des grandes incertitudes sur le cout des CCS et du contrôle de la deforestation. Ce modèle est un cadre formel qui représente de façon simple les différents éléments du système couplé et leurs interactions, il rassemble les différentes estimations des coûts afférents aux mesures de mitigation et permet de les comparer de façon cohérente<br>The goal of this study is to build a global reduced-complexity model of coupled climate-economy-biosphere interactions, which uses the minimum number of variables and equations needed to capture the fundamental mechanisms involved and can thus help clarify the role of the different mechanisms and parameters. The Coupled Climate-Economy-Biosphere (CoCEB) model takes an integrated assessment approach to simulating global change. While many integrated assessment models treat abatement costs merely as an unproductive loss of income, the study considered abatement activities also as an investment in overall energy efficiency of the economy and decrease of overall carbon intensity of the energy system. The study shows that these efforts help to abate climate change and lead to positive effects in economic growth. Due to the fact that integrated assessment models in the literature mainly focus on mitigation in the energy sector and consider emissions from land-use as exogenous, the global climate-economy-biosphere (CoCEB) model was extended by adding a biomass equation and the related exchanges of CO2 and used to investigate the relationship between the effects of using carbon capture and storage (CCS) and deforestation control, and the economy growth rate. These measures are found to reduce the impacts of climate change and positively affect the economy growth. These results remain nevertheless sensitive to the formulation of CCS costs while those for deforestation control were less sensitive. The model developed brings together and summarizes information from diverse estimates of climate change mitigation measures and their associated costs, and allows comparing them in a coherent way
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Zhou, Jian. "Integrating geospatial web 2.0 and global climate model for communicating climate change." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114508.
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This study investigates the use of Geospatial Web 2.0 and Global Climate Models for climate change communication. The aim of this research has been to integrate the data, models, and tools of climate science with Geoweb to advance climate change communication. Several Geoweb applications have been developed to demonstrate the solutions for this integration and to fulfil two research objectives: (1) develop a method to employ Geoweb technologies for communicating climate change, (2) improve the accessibility of Global Climate Model by providing tools to engage people in the practice of climate science as well as the fundamental procedures involved in global climate modeling. My research method is to extend Geoweb functionality to existing climate science tools, with the goal of easing the interface and increasing the interactivity of those tools to elaborate the scientific process of climate modeling. Geoweb has the power to manipulate climate change datasets from diverse sources for creating interactive climate change visualization. This power can be further enhanced if we integrate Geoweb with scientific climate data analysis and visualization systems. Nonetheless, Geoweb technologies that provide 2D visualization are more stable, faster, and popularly used than the 3D visualization. It is more robust to use Geoweb for climate model output. Instead, employing Geoweb for other aspects of global climate model requires close cooperation between climate modeling scientists and Geoweb technology experts due to its complexity. It is crucial to balance an easy-to-use user interface and the complexity of information transferred. Following this study, it is hoped that much more efforts from global climate modeling groups and Geoweb science researchers can be drawn together to facilitate climate change communication.<br>Cette étude porte sur l'utilisation de Géospatiales Web 2.0 et Modèle Climatique Global pour le communication du changement climatique. Le but de cette recherche a été d'intégrer les données, les modèles et les outils de la science du climat avec Geoweb pour faire progresser la communication du changement climatique. Plusieurs applications de GeoWeb ont été développés pour démontrer les solutions de cette intégration et de remplir deux objectifs de recherche: (1) développer une méthode d' utiliser les technologies GeoWeb pour communiquer du changement climatique, (2) améliorer l'accessibilité de Modèle Climatique Global en fournissant des outils pour engager personnes dans la pratique de la science du climat, ainsi que les procédures fondamentales liées à la modélisation du climat mondial. Ma méthode de recherche est d'étendre les fonctionnalités de Geoweb à des outils existants des sciences du climat, dans le but d'alléger l'interface et en augmentant l'interactivité de ces outils pour élaborer le processus scientifique de la modélisation du climat. Geoweb a le pouvoir de manipuler des ensembles de données du changement climatique provenant de diverses sources pour créer une visualisation interactive du changement climatique. Ce pouvoir peut être encore améliorée si l'on intègre Geoweb avec analyse scientifique des données climatiques et des systèmes de visualisation. Néanmoins, les technologies GeoWeb qui fournissent une visualisation 2D sont plus stables, plus rapide et couramment utilisée que la visualisation 3D. Il est plus robuste à utiliser Geoweb pour la sortie des modèles climatiques. Au lieu de cela, en utilisant Geoweb pour d'autres aspects du modèle climatique global nécessite des coopérations étroites entre les scientifiques de modélisation du climat et des experts en technologie de GeoWeb en raison de sa complexité. Il est essentiel d'équilibrer un outil facile à utiliser l'interface utilisateur et la complexité des informations transférées. Suite à cette étude, il est à espérer que beaucoup plus d'efforts de groupes mondiaux de modélisation du climat et des chercheurs en sciences GeoWeb peuvent être réunis pour faciliter la communication pour le changement climatique.
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Wi, Sungwook. "Impact of Climate Change on Hydroclimatic Variables." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/265344.
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The conventional approach to the frequency analysis of extreme rainfall is complicated by non-stationarity resulting from climate change. In this study significant trends in extreme rainfall are detected using statistical trend tests (Mann-Kendall test and t-test) for all over the Korean Peninsula. The violation of the stationarity for 1 hour annual maximum series is detected for large part of the area especially for southwestern and northeastern regions. For stations showing non-stationarity, the non-stationary generalized extreme value (GEV) distribution model with a location parameter in the form of linear function of time makes significant improvement in modeling rainfall extremes when compared to the stationary GEV model. The Bartlett-Lewis rainfall model is used to generate annual maximum series for the purpose of generating the Intensity-Duration-Frequency (IDF) curve. Using 100 sets of 50 year synthetic annual maxima, it is found that the observed annual rainfall maximum series are reasonably represented by the model. The observed data is perturbed by change factors to incorporate the climate change scenario from the WRF (Weather Research and Forecasting) regional climate model into IDF estimates. The IDF curves for the future period 2040-2079 show highest estimates for all return periods and rainfall durations. The future IDF estimates show significant difference from the IDF estimates of the historical period (1968-2000). Overall, IDF curves show an increasing tendency over time. A historical and future climate simulation is evaluated over the Colorado River Basin using a 111-year simulation (1969-2079) of the WRF climate change scenario. We find the future projections show statistically significant increases in temperature with larger increases in the northern part of the basin. There are statistically insignificant increases in precipitation, while snowfall shows a statistically significant decrease throughout the period in all but the highest elevations and latitudes. The strongest decrease in snowfall is seen at high elevations in the southern part of the basin and low elevations in the northern part of the basin.
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Alberth, Stephan Eric. "Valuing technical change information in an integrated assessment model of climate change." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613302.
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Otto, Vincent M., Andreas Loeschel, and John M. Reilly. "Directed Technical Change and Climate Policy." MIT Joint Program on the Science and Policy of Global Change, 2006. http://hdl.handle.net/1721.1/32541.
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This paper studies the cost effectiveness of climate policy if there are technology externalities. For this purpose, we develop a forward-looking CGE model that captures empirical links between CO2 emissions associated with energy use, directed technical change and the economy. We find the cost-effective climate policy to include a combination of R&D subsidies and CO2 emission constraints, although R&D subsidies raise the shadow value of the CO2 constraint (i.e. CO2 price) because of a strong rebound effect from stimulating innovation. Furthermore, we find that CO2 constraints differentiated toward CO2-intensive sectors are more cost effective than constraints that generate uniform CO2 prices among sectors. Differentiated CO2 prices, through technical change and concomitant technology externalities, encourage growth in the non-CO2 intensive sectors and discourage growth in CO2-intensive sectors. Thus, it is cost effective to let the latter bear relatively more of the abatement burden. This result is robust to whether emission constraints, R&D subsidies or combinations of both are used to reduce CO2 emissions.<br>Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).
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Gars, Johan. "Essays on the Macroeconomics of Climate Change." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-74555.
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This thesis consists of three essays on macroeconomic aspects of climate change. Technological Trends and the Intertemporal Incentives For Fossil-Fuel Use analyzes how (the expectations about) the future developments of different kinds of technology affect the intertemporal incentives for fossil-fuel use. I find that improvements in the future state of technologies for alternative-energy generation, energy efficiency and total factor productivity all increase fossil-fuel use before the change takes place. The effect of changes in the efficiency of non-energy inputs is the reverse, while the effect of changes in fossil-fuel based energy technology is ambiguous. These conclusions are robust to a number of variations of the assumptions made. The Role of the Nature of Damages considers to what extent the choice of modeling climate impacts as affecting productivity, utility or the depreciation of capital affects the behavior of integrated assessment models. I carry out my analysis in two different ways. Firstly, under some simplifying assumptions, I derive a simple formula for the optimal tax on fossil-fuel use that adds up the three different types of climate effects. Secondly, I use a two-period model with exogenous climate to analyze how the allocation of fossil-fuel use over time is affected by the effects of climate change. I find that this is sensitive to the assumptions made. Indirect Effects of Climate Change investigates how direct effects of climate change in some countries have indirect effects on other countries going through changing world market prices of goods and financial instruments. When calculating the total effects of climate change, these indirect effects must also be taken into account. I first derive these indirect effects in a many-country model. Reaching agreements about reductions in the emissions of greenhouse gases is made difficult by the negative correlation there seems to be between emissions of greenhouse gases and the vulnerability to climate change. I argue, based on a stylized two country example, that trade in goods will tend to make the countries' interests more aligned while trade in financial instruments will tend to make the countries' interests less aligned.
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Betts, Richard Arthur. "Modelling the influence of the vegetated land surface on climate and climate change." Thesis, University of Reading, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312335.
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Conradie, Willem Stefaan. "Conceptualising and quantifying the nonlinear, chaotic climate: implications for climate model experimental design." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/16527.
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Includes bibliographical references<br>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.
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Yettella, Vineel. "The Role of Internal Variability in Climate Change Projections within an Initial Condition Climate Model Ensemble." Thesis, University of Colorado at Boulder, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10981737.
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<p> Unforced internal variability abounds in the climate system and often confounds the identification of climate change due to external forcings. Given that greenhouse gas concentrations are projected to increase for the foreseeable future, separating forced climate change from internal variability is a key concern with important implications. Here, we leverage a 40-member ensemble, the Community Earth System Model Large Ensemble (CESM-LE) to investigate the influence of internal variability on the detection of forced changes in two climate phenomena. First, using cyclone identification and compositing techniques within the CESM-LE, we investigate precipitation changes in extratropical cyclones under greenhouse gas forcing and the effect of internal variability on the detection of these changes. We find that the ensemble projects increased cyclone precipitation under twenty-first century business-as-usual greenhouse gas forcing and this response exceeds internal variability in both near- and far- futures. Further, we find that these changes are almost entirely driven by increases in cyclone moisture. Next, we explore the role of internal variability in projections of the annual cycle of surface temperature over Northern Hemisphere land. Internal variability strongly confounds forced changes in the annual cycle over many regions of the Northern Hemisphere. Changes over Europe, North Africa and Siberia, however, are large and easily detectable and further, are remarkably robust across model ensembles from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Using a simple energy balance model, we find that changes in the annual cycle over the three regions are mostly driven by changes in surface heat fluxes. </p><p> The thesis also presents a novel ensemble-based framework for diagnosing forced changes in regional climate variability. Changes in climate variability are commonly assessed in terms of changes in the variances of climate variables. The covariance response has received much less attention, despite the existence of large-scale modes of variability that induce covariations in climate variables over a wide range of spatial scales. Addressing this, the framework facilitiates a unified assessment of forced changes in the regional variances and covariances of climate variables.</p><p>
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Clark, Logan N. "Southern Hemisphere Pressure Relationships during the 20th Century - Implications for Climate Reconstructions and Model Evaluation." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1586778291377432.
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Rahiz, Muhammad. "Droughts in future climate change in the UK." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:cde09ece-1da9-4374-964e-ebea462bc956.
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This thesis seeks to investigate the changes in the characteristics of 20th and 21st century meteorological droughts in the UK to address the following lines of inquiry: 1) How credible are rainfall-based indices in representing hydrological droughts, 2) How coherent are droughts?, 3) Can alternative method of analysis provide new (or additional) information on the uncertainties in climate models?, and 4) Will future drought characteristics change?. Key results, respectively, are summarized as follows: • The drought severity index (DSI), can be considered a good proxy for assessing hydrological droughts as can be seen in its ability to capture the major hydrological events. The main caveat of the DSI is that there is often an underestimation of drought intensity and duration, • Drought covariance is higher for the (i) wet season, (ii) moderate and (iii) shorter duration droughts, • Evaluating climate models using drought statistics produces contrasting results compared with that using the model's precipitation fields. Drought statistics show biases which are largely negative, more intense, and have a greater spatial coverage. • The projected ensemble-mean change is generally greater (and more widespread) for (i) moderate droughts and (ii) wet season compared with extreme droughts and the dry season, respectively with increases in drought intensity, drought covariance index, frequency of drought months and frequency of drought events for a given duration shown for England.
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Pesenti, Sara <1997>. "Climate Change Adaptation and the Role of Innovation: A Model of Directed Technological Change." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/20926.
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Adaptation is considered a key element in the defense against the negative effects of climate change on human societies. In this thesis, I study adaptation to climate change by focusing on the role of innovation. I develop a three-sector general equilibrium model with endogenous directed technological change and an environmental damage function. The three sectors represent polluting, green, and adaptation technologies and agents are free to choose how much research to allocate to each sector. I describe the decentralized equilibrium of the model and analyse which are the incentives shaping agents' choices on adaptation. Finally, I try to understand whether an interior solution in which innovation happens both in adaptation and green technologies is possible in this model.
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Inthacha, Sujittra. "The climatology of Thailand and future climate change projections using the regional climate model precis." Thesis, University of East Anglia, 2011. https://ueaeprints.uea.ac.uk/36354/.
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The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days.
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Rackauckas, Christopher V. "The Jormungand Climate Model." Oberlin College Honors Theses / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1368151558.
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Chamberlain, Matthew Allyn. "Response of Martian Ground Ice to Orbit-Induced Climate Change." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/195434.
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A thermal model is developed to find the distribution of stable near-surface ground ice on Mars that is in diffusive contact with the atmosphere for past and present epochs. Variations in the orbit of Mars are able to drive climate changes that affect both surface temperatures and atmospheric water content so the distribution of ground ice will vary significantly in past epochs. A technique is developed to correct the average water vapor density above the surface for depletion due to diurnal frost formation. Also presented is a simple model to estimate the atmospheric water content, based on the water vapor carrying capacity of the atmosphere over water ice on the martian surface.Maps of the distribution of ground ice are generated for the present epoch of Mars with varying amounts of water vapor in the atmosphere. The water vapor depletion scheme restricts the extent of stable ground ice as more water is put into the atmosphere so that ice never becomes stable at low latitudes. As the position of the perihelion varies, the extent of ground ice changes several degrees in the latitudinal extent, primarily in the northern hemisphere. The extent of ground ice is sensitive to the obliquity of Mars, however high obliquities are still not able to make ground ice stable at low latitudes. Finding ice is never stable at low latitudes is consistent with the lack of terrain softening at low latitudes and models that indicate Mars had high obliquities for much of its history.Also presented is the first L-band spectrum of an irregular satellite from the outer Solar System. Spectra of Himalia were obtained with the Visual and Infrared Mapping Spectrometer onboard the Cassini spacecraft. The Himalia spectrum is essentially featureless, showing a slight red slope and a suggestion of an absorption feature at 3 microns that would indicate the presence of water. Better measurements of the spectrum of Himalia, particularly in the region of the apparent 3-micron band, could help determine whether water is present, and if so, in what form.
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Wiebe, Edward Carl. "Climate change and sub-grid-scale mixing in a coupled model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0002/MQ36625.pdf.
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Bogart, Tianna A. "Sensitivity of a global climate model to the urban land unit." Thesis, University of Delaware, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3598618.
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<p> With more than half of the world's population living in urban areas, it is important that the relationships between the urban environment and climate are better understood. The current research aims to continue the effort in assessing and understanding the urban environment through the use of a global climate model (GCM). Given the relative newness of the presence of an urban land type and model in a GCM, there are many more facets of the urban-climate relationship to be investigated. By comparing thirty-year ensembles of CAM4 coupled with CLM4 both with (U) and without (U<sub>n</sub>) the inclusion of the urban land type, the sensitivity of the atmospheric model to urban land cover is assessed. As expected, largest differences tend to be in the Northern Hemisphere due to the location of most of the globe's densest and expansive cities. Significant differences in the basic climate variables of temperature and precipitation are present at annual, seasonal, and monthly scales in some regions. Seasonality to the urban influence also exists with the transition months of Spring and Fall having the largest difference in temperatures. Of the eleven regions defined by Oleson (2012), three were most impacted by the presence of urban land cover in the model—Europe, Central Asia, and East Asia. </p><p> Since urban attributes can vary greatly within one world continent, the sensitivity of regional climates to the urban type parameters is also explored. By setting all urban land cover to only one urban density type, the importance of city composition on climate, even within the same city, is highlighted. While preserving the distinct urban regional characteristics and the geographical distribution of urbanized areas, the model is run with homogeneous urban types: high density and tall building district. As with the default urban and excluded urban runs, a strong seasonality to the differences between the solo-high-density simulation and default urban (U<sub>HD</sub> – U) and solo-tall-building-district-density simulation and default urban (U<sub>TBD</sub> – U) exists. Overall, the transition and winter months are most sensitive to changes in urban density type.</p>
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Tamanna, Marzia. "Dynamically Downscaled NARCCAP Climate Model Simulations| An Evaluation Analysis over Louisiana." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1594520.
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<p> In order to make informed decisions in response to future climate change, researchers, policy-makers, and the public need climate projections at the scale of few kilometers, rather than the scales provided by Global Climate Models. The North American Regional Climate Change Assessment Program (NARCCAP) is such a recent effort that addresses this necessity. As the climate models contain various levels of uncertainty, it is essential to evaluate the performance of such models and their representativeness of regional climate characteristics. When assessing climate change impacts, precipitation is a crucial variable, due to its direct influence on many aspects of our natural-human ecosystems such as freshwater resources, agriculture and energy production, and health and infrastructure. The current study performs an evaluation analysis of precipitation simulations produced by a set of dynamically downscaled climate models provided by the NARCCAP program. The Assessment analysis is implemented for a period that covers 20 to 30 years (1970-1999), depending on joint availability of both the observational and the NARCCAP datasets. In addition to direct comparison versus observations, the hindcast NARCCAP simulations are used within a hydrologic modeling analysis for a regional ecosystem in coastal Louisiana (Chenier Plain). The study concludes the NARCCAP simulations have systematic biases in representing average precipitation amounts, but are successful at capturing some of the characteristics on spatial and temporal variability. The study also reveals the effect of precipitation on salinity concentrations in the Chenier Plain as a result of using different precipitation forcing fields. In the future, special efforts should be made to reduce biases in the NARCCAP simulations, which can then lead to a better presentation of regional climate scenarios for use by decision makers and resource managers.</p>
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Kolus, Hannah. "Assessing Terrestrial Biosphere Model Simulation of Ecosystem Drought Response and Recovery." Thesis, Northern Arizona University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10283123.
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<p> Severe drought plays a critical role in altering the magnitude and interannual variability of the net terrestrial carbon sink. Drought events immediately decrease net primary production (NPP), and drought length and magnitude tend to enhance this negative impact. However, satellite and in-situ measurements have also indicated that ecosystem recovery from extreme drought can extend several years beyond the return to normal climate conditions. If an ecosystem’s drought recovery time exceeds the time interval between successive droughts, these legacy effects may reinforce the impact of future drought. Since the frequency and severity of extreme climate events are expected to increase with climate change, both the immediate and prolonged impact of drought may contribute to amplified climate warming by decreasing the strength of the land carbon sink. However, it is unknown whether terrestrial biosphere models capture the impact of drought legacy effects on carbon stocks and cycling. Using a suite of twelve land surface models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed model ability to simulate drought legacy effects by analyzing the modeled NPP response to drought events across forested regions of the US and Europe. We found that modeled drought legacy effects last about one year (2% reduction in NPP), with complete NPP recovery in the second post-drought year. Since observations suggest that legacy effects extend up to four years post-drought, with a 9% growth reduction in the first post-drought year, models appear to underestimate both the timescales and magnitude of drought legacy effects. We further explored vegetation sensitivity to climate anomalies through global, time-lagged correlation analysis of NPP and climatic water deficit. Regional differences in the lag time between climate anomaly and NPP response are prevalent, but low sensitivities (correlations) characterize the entire region. Significant correlations coincided with characteristic lag times of 0 to 6 months, indicating relatively immediate NPP response to moisture anomalies. Model ability to accurately simulate vegetation’s response to drought and sensitivity to climate anomalies is necessary in order to produce reliable forecasts of land carbon sink strength and, consequently, to predict the rate at which climate change will progress in the future. Thus, the discrepancies between observed and simulated vegetation recovery from drought points to a potential critical model deficiency.</p><p>
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Shayegh, Soheil. "Learning in integrated optimization models of climate change and economy." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54012.
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Integrated assessment models are powerful tools for providing insight into the interaction between the economy and climate change over a long time horizon. However, knowledge of climate parameters and their behavior under extreme circumstances of global warming is still an active area of research. In this thesis we incorporated the uncertainty in one of the key parameters of climate change, climate sensitivity, into an integrated assessment model and showed how this affects the choice of optimal policies and actions. We constructed a new, multi-step-ahead approximate dynamic programing (ADP) algorithm to study the effects of the stochastic nature of climate parameters. We considered the effect of stochastic extreme events in climate change (tipping points) with large economic loss. The risk of an extreme event drives tougher GHG reduction actions in the near term. On the other hand, the optimal policies in post-tipping point stages are similar to or below the deterministic optimal policies. Once the tipping point occurs, the ensuing optimal actions tend toward more moderate policies. Previous studies have shown the impacts of economic and climate shocks on the optimal abatement policies but did not address the correlation among uncertain parameters. With uncertain climate sensitivity, the risk of extreme events is linked to the variations in climate sensitivity distribution. We developed a novel Bayesian framework to endogenously interrelate the two stochastic parameters. The results in this case are clustered around the pre-tipping point optimal policies of the deterministic climate sensitivity model. Tougher actions are more frequent as there is more uncertainty in likelihood of extreme events in the near future. This affects the optimal policies in post-tipping point states as well, as they tend to utilize more conservative actions. As we proceed in time toward the future, the (binary) status of the climate will be observed and the prior distribution of the climate sensitivity parameter will be updated. The cost and climate tradeoffs of new technologies are key to decisions in climate policy. Here we focus on electricity generation industry and contrast the extremes in electricity generation choices: making choices on new generation facilities based on cost only and in the absence of any climate policy, versus making choices based on climate impacts only regardless of the generation costs. Taking the expected drop in cost as experience grows into account when selecting the portfolio of generation, on a pure cost-minimization basis, renewable technologies displace coal and natural gas within two decades even when climate damage is not considered in the choice of technologies. This is the natural gas as a bridge fuel scenario, and technology advancement to bring down the cost of renewables requires some commitment to renewables generation in the near term. Adopting the objective of minimizing climate damage, essentially moving immediately to low greenhouse gas generation technologies, results in faster cost reduction of new technologies and may result in different technologies becoming dominant in global electricity generation. Thus today’s choices for new electricity generation by individual countries and utilities have implications not only for their direct costs and the global climate, but also for the future costs and availability of emerging electricity generation options.
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Baran, Ayden Alexander. "Integrated Model-Based Impact Assessment of Climate Change and Land Use Change on the Occoquan Watershed." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99706.
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Forecasted changes to climate and land use were used to model variations in the streamflow characteristics of Occoquan watershed and water quality in the Occoquan reservoir. The combination of these two driving forces has created four themes and an integrated complexly-linked watershed-reservoir model was used to run the simulations. Two emission scenarios from the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), along with four General Circulation Models (GCMs) by using two statistical downscaling methods, were applied to drive the Hydrological Simulation Program - Fortran (HSPF) and CE-QUAL-W2 (W2) in two future time periods (2046-2065 and 2081-2100). Incorporation of these factors yielded 68 simulation models which were compared with historical streamflow and water quality data from the late 20th century. Climate change is projected to increase surface air temperature and precipitation depth in the study area in the future. Using climate change only, an increase in high and median flows and decrease in low flows are projected. Changes in flow characteristics are more pronounced when only future land use changes are considered, with increases in high, median and low flows. Under the joint examination of the driving forces, an amplifying effect on the high flows and median flows observed. In contrast, climate change is projected to dampen the extreme increases in the low flows created by the land use change. Surface water temperatures are projected to increase as a result of climate change in the Occoquan reservoir, while these changes are not very noticeable under the effect of land use change only. It is expected that higher water temperatures will promote decreased oxygen solubility and greater heterotrophy. Moreover, longer anoxic conditions are projected at the bottom of the reservoir. Results indicate that higher water temperature will increase the denitrifying capacity of the reservoir, especially during summer months, further reducing the nitrate concentration in the reservoir.<br>PHD
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Sansom, Philip George. "Statistical methods for quantifying uncertainty in climate projections from ensembles of climate models." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15292.
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Appropriate and defensible statistical frameworks are required in order to make credible inferences about future climate based on projections derived from multiple climate models. It is shown that a two-way analysis of variance framework can be used to estimate the response of the actual climate, if all the climate models in an ensemble simulate the same response. The maximum likelihood estimate of the expected response provides a set of weights for combining projections from multiple climate models. Statistical F tests are used to show that the differences between the climate response of the North Atlantic storm track simulated by a large ensemble of climate models cannot be distinguished from internal variability. When climate models simulate different responses, the differences between the re- sponses represent an additional source of uncertainty. Projections simulated by climate models that share common components cannot be considered independent. Ensemble thinning is advocated in order to obtain a subset of climate models whose outputs are judged to be exchangeable and can be modelled as a random sample. It is shown that the agreement between models on the climate response in the North Atlantic storm track is overestimated due to model dependence. Correlations between the climate responses and historical climates simulated by cli- mate models can be used to constrain projections of future climate. It is shown that the estimate of any such emergent relationship will be biased, if internal variability is large compared to the model uncertainty about the historical climate. A Bayesian hierarchical framework is proposed that is able to separate model uncertainty from internal variability, and to estimate emergent constraints without bias. Conditional cross-validation is used to show that an apparent emergent relationship in the North Atlantic storm track is not robust. The uncertain relationship between an ensemble of climate models and the actual climate can be represented by a random discrepancy. It is shown that identical inferences are obtained whether the climate models are treated as predictors for the actual climate or vice versa, provided that the discrepancy is assumed to be sym- metric. Emergent relationships are reinterpreted as constraints on the discrepancy between the expected response of the ensemble and the actual climate response, onditional on observations of the recent climate. A simple method is proposed for estimating observation uncertainty from reanalysis data. It is estimated that natural variability accounts for 30-45% of the spread in projections of the climate response in the North Atlantic storm track.
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Lawson, Callum Robert. "From microhabitat to metapopulations : a model system for conservation under climate change." Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/9557.
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Zhang, Feng. "Climate change assessment for the southeastern United States." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45770.
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Water resource planning and management practices in the southeastern United States may be vulnerable to climate change. This vulnerability has not been quantified, and decision makers, although generally concerned, are unable to appreciate the extent of the possible impact of climate change nor formulate and adopt mitigating management strategies. Thus, this dissertation aims to fulfill this need by generating decision worthy data and information using an integrated climate change assessment framework.
To begin this work, we develop a new joint variable spatial downscaling technique for statistically downscaling gridded climatic variables to generate high-resolution, gridded datasets for regional watershed modeling and assessment. The approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study.
In the second component of the integrated assessment framework, we develop a data-driven, downward hydrological watershed model for transforming the climate variables obtained from the downscaling procedures to hydrological variables. The watershed model includes several water balance elements with nonlinear storage-release functions. The release functions and parameters are data driven and estimated using a recursive identification methodology suitable for multiple, inter-linked modeling components. The model evolves from larger spatial/temporal scales down to smaller spatial/temporal scales with increasing model structure complexity. For ungauged or poorly-gauged watersheds, we developed and applied regionalization hydrologic models based on stepwise regressions to relate the parameters of the hydrological models to observed watershed responses at specific scales.
Finally, we present the climate change assessment results for six river basins in the southeastern United States. The historical (baseline) assessment is based on climatic data for the period 1901 through 2009. The future assessment consists of running the assessment models under all IPCC A1B and A2 climate scenarios for the period from 2000 through 2099. The climate assessment includes temperature, precipitation, and potential evapotranspiration; the hydrology assessment includes primary hydrologic variables (i.e., soil moisture, evapotranspiration, and runoff) for each watershed.
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Chang, Biao. "Spatial analysis of sea level rise associated with climate change." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49062.
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Sea level rise (SLR) is one of the most damaging impacts associated with climate change. The objective of this study is to develop a comprehensive framework to identify the spatial patterns of sea level in the historical records, project regional mean sea levels in the future, and assess the corresponding impacts on the coastal communities. The first part of the study suggests a spatial pattern recognition methodology to characterize the spatial variations of sea level and to investigate the sea level footprints of climatic signals. A technique based on artificial neural network is proposed to reconstruct average sea levels for the characteristic regions identified. In the second part of the study, a spatial dynamic system model (DSM) is developed to simulate and project the changes in regional sea levels and sea surface temperatures (SST) under different development scenarios of the world. The highest sea levels are predicted under the scenario A1FI, ranging from 71 cm to 86 cm (relative to 1990 global mean sea level); the lowest predicted sea levels are under the scenario B1, ranging from 51 cm to 64 cm (relative to 1990 global mean sea level). Predicted sea levels and SST's of the Indian Ocean are significantly lower than those of the Pacific and the Atlantic Ocean under all six scenarios. The last part of this dissertation assesses the inundation impacts of projected regional SLR on three representative coastal U.S. states through a geographic information system (GIS) analysis. Critical issues in the inundation impact assessment process are identified and discussed.
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Ribeiro, Duarte Tiago. "Expertise and the fractal model : communication and collaboration between climate-change scientists." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/49632/.
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This thesis examines how scientific communities which are heterogeneous among themselves communicate and collaborate to produce knowledge on climate change. Climate-change science is a relatively new field of investigation and it includes experts from virtually all areas of scientific enquiry. This field is not, however, a homogeneous transdisciplinary area of research so that the different scientific communities that compose it have to bridge the gaps among themselves to be able effectively to communicate and collaborate. I use Collins and Evans’ (2007) realist theory of expertise combined with other relevant Science and Technology Studies concepts, particularly the notions of trading zones (Galison 1997; Collins et al. 2007) and trust (Giddens 1990, Shackley and Wynne 1995b; Reyes-Galindo 2011) to explain how different groups of experts build bridges between their heterogeneous forms of life. As climate-change science is too broad to be covered in one PhD project, I focus on paleoceanography, a subfield of geology that reconstructs past oceans and their interactions with the climate system. I use the fractal model (Collins 2011) to move through different levels of analysis and examine the different bridge-building mechanisms between expert communities at work at each of them. The main contribution of the present work is to identify and explain how the various mechanisms that mediate communication between expert communities come into play at different levels of analysis.
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Kellie-Smith, Owen. "Relating forced climate change to natural variability and emergent dynamics of the climate-economy system." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/115194.
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This thesis is in two parts. The first part considers a theoretical relationship between the natural variability of a stochastic model and its response to a small change in forcing. Over a large enough scale, both the real climate and a climate model are characterised as stochastic dynamical systems. The dynamics of the systems are encoded in the probabilities that the systems move from one state into another. When the systems’ states are discretised and listed, then transition matrices of all these transition probabilities may be formed. The responses of the systems to a small change in forcing are expanded in terms of the eigenfunctions and eigenvalues of the Fokker-Planck equations governing the systems’ transition densities, which may be estimated from the eigenvalues and eigenvectors of the transition matrices. Smoothing the data with a Gaussian kernel improves the estimate of the eigenfunctions, but not the eigenvalues. The significance of differences in two systems’ eigenvalues and eigenfunctions is considered. Three time series from HadCM3 are compared with corresponding series from ERA-40 and the eigenvalues derived from the three pairs of series differ significantly. The second part analyses a model of the coupled climate-economic system, which suggests that the pace of economic growth needs to be reduced and the resilience to climate change needs to be increased in order to avoid a collapse of the human economy. The model condenses the climate-economic system into just three variables: a measure of human wealth, the associated accumulation of greenhouse gases, and the consequent level of global warming. Global warming is assumed to dictate the pace of economic growth. Depending on the sensitivity of economic growth to global warming, the model climate-economy system either reaches an equilibrium or oscillates in century-scale booms and busts.
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Zhang, Hengyue. "Using satellite remote sensing, field observations and WRF/single-layer urban canopy model simulation to analyze the Oklahoma City UHI effect." Thesis, San Jose State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1594250.
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<p> The Urban Heat Island (UHI) was investigated using satellite data, ground observations, and simulations with an Urban Canopy Parameterization in a numerical weather prediction model. Satellite-observed surface skin temperatures at Xi'an City and Oklahoma City (OKC) were analyzed to compare the UHI intensity for the two inland cities. A larger population density and larger building density in Xi'an City creates a stronger skin-level UHI effect. However, ground observed 2-m surface air temperature (Tair) data showed an urban cooling island (UCI) effect that occurred over an urban region in OKC during the daytime of July 19, 2003. </p><p> The sensitivity and accuracy of an Urban Canopy Model were evaluated by comparing simulation results between the urban and rural areas of OKC. The model reproduced skin temperature differences between the rural and urban area and reproduced a UCI effect in OKC. Furthermore, the Weather Research and Forecasting (WRF)/Noah/Single-Layer Urban Canopy Model (SLUCM) simulations were also compared with ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLCUM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions and energy fluxes reasonably well.</p>
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Arif, Faisal. "Three Essays on the Economics of Climate Change." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20721.
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Thesis Abstract:
Chapter I: Regional burden sharing of GHG mitigation policies – A Canadian perspective. The distribution of the burden of cost arising from the reduction of greenhouse gas (GHG) emissions is a contentious issue in policy discussions; more so among regional jurisdictions in the federalist countries with decentralized authorities over environmental regulations. In this setting, often the policy discussions are focused on the distribution of regional emission reduction targets that, in turn, entails negotiations over the distribution of the scarcity rents and the regional transfers of wealth. The allocation of regional emission entitlements is thus a key factor that could hinder the political feasibility of a national GHG mitigation policy. In this paper, we build a multi-region computable general equilibrium (CGE) model of the Canadian economy to assess the implications of different burden sharing rules governing the national GHG abatement policy with a cap-and-trade system of emission permits. In addition to assessing the impacts of traditional regional emissions allocation rules that involve intra-regional transfers of wealth, we consider a particular emission allocation that avoids such transfers, which may be a more palatable option given the context of likely fierce negotiations over the issue. Our results indicate to differing outcomes depending on the allocation policy in use. The CGE framework is also able to shed light on the transmission mechanisms that drive the results underlying the policy options.
Chapter II: Endogenous technological change and emission allowances. Given the imminent threat of global warming due to GHG emissions, a number of emission mitigation policies have been proposed in the literature. However, they generally suffer from the classical equity-efficiency trade-off. High costs from equity concerns often render environmental policies politically unattractive and thus hard to implement. Recent advancement in the climate policy modeling literature that incorporates endogenous technological change (ETC) into the framework can potentially bring new insights into this debate. Using an inter-temporal, multi-sector CGE approach with ETC incorporated into the framework, this paper builds a model that focuses on the equity-efficiency debate for the policymakers. Canada is chosen as the country of investigation for this purpose. The paper provides a new welfare ranking of four permit allocation policies that address the equity-efficiency trade-off. In a second-best setting with pre-existing distortions, output-based allocation (OBA) of emission permits is compared to three other policy options: (i) an emissions trading system with grandfathered allocation (GFA), (ii) an auction permit trading system where permit revenue is recycled to lower payroll taxes (RPT), and (iii) a hybrid of OBA and R&D subsidy (O-R&D). We find that adapting OBA, as well as O-R&D, is welfare improving over GFA. The implicit output subsidy, entailed in the OBA policy, mitigates against the rising cost effect in the GFA policy. This is reinforced through added investment incentive in R&D when ETC in incorporated into the framework. With O-R&D, since the R&D subsidy corrects for market imperfections in the knowledge accumulation process, the effect is further bolstered, culminating into mitigation of uneven distributional outcome for energy-intensive industries as a whole. Contrary to previous results, we also find that, in terms of the welfare metric, OBA unequivocally improves the distributional outcome across sectors as compared to the RPT policy. Inclusion of ETC also unequivocally generates a higher welfare ranking for all permit policy schemes.
Chapter III: Emission permit banking and induced technological change. This paper attempts to undertake an exploratory research by integrating two themes in the emission mitigation policy literature, which include: the inter-temporal emission permit banking and borrowing and the role of induced technological change in emission mitigation. Using a simple optimal control approach, we construct a unified framework that evaluates the optimal path of emissions and the optimal trajectory of permit price when both inter-temporal banking and borrowing of permits and the effects of induced technological change (ITC) are present. We find that ITC leads to a declining emission trajectory over time. The effect of ITC on the optimal permit price path, however, is ambiguous and critically depends on the extent of marginal cost saving that emanates from emission-saving technological innovation.
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Beraki, Asmeron Fissehatsion. "Climate change scenario simulations over Eritrea by using a fine resolution limited area climate model temperature and moisture sensitivity /." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-02102006-152327.
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Vaghefi, Parshin. "Assessment and Improvement of CLIGEN for Climate Change Impact Analysis in Australia." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/366770.
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Climate change is a complex phenomenon and can have considerable impact on hydrological and bio-physical systems as well as the society. To evaluate the impact of climate change, stochastic weather generators (SWG) are commonly used to produce synthetic weather sequences that are statistically similar to the observed weather data, and these SWGs have been widely used for downscaling global climate model (GCM) outputs. CLIGEN is one such weather generator that has been used for impact analysis. As a unique SWG, CLIGEN can produce variables describing storm patterns, including time to peak, peak intensity, and storm duration, in addition to precipitation amount and other daily weather variables. CLIGEN has been used for WEPP (Water Erosion Prediction Project) to predict runoff, soil erosion, and crop production. Although in recent years several research papers have been published to evaluate approaches that adjust CLIGEN parameters to simulate non-stationary climate change scenarios using observed data prior researches was limited to use simple approaches, e.g. multiplying the CLIGEN-generated daily precipitation by a fixed factor.
The main goal of this research was to develop methodologies to adjust precipitation related parameters of CLIGEN for climate change impact analysis in two regions of Australia. On a broader scale, this research has three objectives:
1. To adjust relevant CLIGEN parameters when annual precipitation has changed abruptly and significantly;
2. To adjust relevant CLIGEN parameters when there is a significantly decreasing trend in annual precipitation; and
3. To validate the adjustment method in terms of simulated streamflow using conceptual hydrological models.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>Griffith School of Engineering<br>Science, Environment, Engineering and Technology<br>Full Text
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Paulis, Victor. "THE RESPONSE OF A GENERAL CIRCULATION CLIMATE MODEL TOHIGH LATITUDE FRESHWATER FORCING IN THE ATLANTIC BASINWITH RESPECT TOTROPI." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3848.
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The current cycle of climate change along with increases in hurricane activity, changing precipitation patterns, glacial melt, and other extremes of weather has led to interest and research into the global correlation or teleconnection between these events. Examination of historical climate records, proxies and observations is leading to formulation of hypotheses of climate dynamics with modeling and simulation being used to test these hypotheses as well as making projections. Ocean currents are believed to be an important factor in climate change with thermohaline circulation (THC) fluctuations being implicated in past cycles of abrupt change. Freshwater water discharge into high-latitude oceans attributed to changing precipitation patterns and glacial melt, particularly the North Atlantic, has also been associated with historical abrupt climate changes and is believed to have inhibited or shut down the THC overturning mechanism by diluting saline surface waters transported from the tropics. Here we analyze outputs of general circulation model (GCM) simulations parameterized by different levels of freshwater flux (no flux (control), 0.1 Sverdrup (Sv) and 1.0 Sv) with respect to tropical cyclone-like vortices (TCLVs) to determine any trend in simulated tropical storm frequency, duration, and location relative to flux level, as well as considering the applicability of using GCMs for tropical weather research. Increasing flux levels produced fewer storms and storm days, increased storm duration, a southerly and westerly shift (more pronounced for the 0.1 Sv level) in geographic distribution and increased activity near the African coast (more pronounced for the 1.0 Sv level). Storm intensities and tracks were not realistic compared to observational (real-life) values and is attributed to the GCM resolution not being fine enough to realistically simulate storm (microscale) dynamics.<br>Ph.D.<br>Other<br>Sciences<br>Modeling and Simulation PhD
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Braneon, Christian V. "Agricultural water demand assessment in the Southeast U.S. under climate change." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53409.
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This study utilized (a) actual measured agricultural water use along with (b) geostatistical techniques, (c) crop simulation models, and (d) general circulation models (GCMs) to assess irrigation demand and the uncertainty associated with demand projections at spatial scales relevant to water resources management. In the first part of the study, crop production systems in Southwest Georgia are characterized and the crop simulation model error that may be associated with aggregated model inputs is estimated for multiple spatial scales.
In the second portion of this study, a methodology is presented for characterizing regional irrigation strategies in the Lower Flint River basin and estimating regional water demand. Regional irrigation strategies are shown to be well represented with the moisture stress threshold (MST) algorithm, metered annual agricultural water use, and crop management data. Crop coefficient approaches applied at the regional scale to estimate agricultural water demand are shown to lack the interannual variability observed with this novel approach.
In the third portion of this study, projections of regional agricultural demand under climate change in the Lower Flint River basin are presented. GCMs indicate a range of possible futures that include the possibility of relatively small changes in irrigation demand in the Lower Flint River basin. However, most of the GCMs utilized in this work project significant increases in median water demand towards the end of this century. In particular, results suggest that peak agricultural water demands in July and August may increase significantly.
Overall, crop simulation models are shown to be useful tools for representing the intra-annual and interannual variability of regional irrigation demand. The novel approach developed may be applied to other locations in the world as agricultural water metering programs become more common.
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Geer, Alan Jon. "Climate studies and model validation using satellite 6.7#mu#m water vapour data." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325605.
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Poiani, Karen A. "Response of semi-permanent prairie wetland to climate change: a spatial simulation model." Diss., Virginia Tech, 1990. http://hdl.handle.net/10919/39952.
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Dusselier, Hallie E. "Understanding 20th Century Antarctic Pressure Variability and Change in Multiple Climate Model Simulations." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1469189473.
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Blanco, González Víctor. "Modelling adaptation strategies for Swedish forestry under climate and global change." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25380.
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Adaptation is necessary to cope with, or take advantage of, the effects of climate change on socio-ecological systems. This is especially important in the forestry sector, which is sensitive to the ecological and economic impacts of climate change, and where the adaptive decisions of owners play out over long periods of time. These decisions are subject to experienced and expected impacts, and depend upon the temporal interactions of a range of individual and institutional actors. Knowledge of, and responses to, climate change are therefore very important if forestry is to cope with, or take advantage of, the effects of climate change over longer timescales. It is important to understand the role of human behaviour and decision-making processes in the study of complex socio-ecological systems and modelling is a method that can support experiments to advance this understanding. This study is based on the development of CRAFTY-Sweden; an agent-based model that allows the exploration of Swedish land-use dynamics and adaptation to climate change through scenario analysis. In CRAFTY-Sweden, forest and farmland owners make land use and management decisions according to their objectives, management preferences and capabilities. As a result of their management and location characteristics they are able to provide ecosystem services. To explore future change, quantitative scenarios were used that considered both socio-economic development pathways and climatic change. Simulations were run under the different scenarios for the period 2010-2100, for the whole of Sweden. Furthermore, because institutions (i.e. organisations) also influence socio-ecological systems through their actions and interactions between them and with land owners and the environment, a conceptual model of institutional actions applied to socio-ecological systems was developed. The application of this conceptual model was explored through a model of institutions that can act, interact and adapt to environmental change in attempting to affect ecosystem service provision within a simple forestry governance system. I found that forestry in the future will likely be unable to meet societal demands for forest services solely on the basis of autonomous adaptation. A northward expansion of agriculture and especially of forestry proved positive for both sectors to adapt to changing conditions, under several scenarios, given the substantial land availability and the improved environmental conditions for plant growth. Legacy effects of past land-use change can have a great impact on future land-use change and adaptation processes, especially in forestry. Also, greater competition for land may lead to shorter forest rotation times. Socio-economic change and land owner behavioural differences may have a larger impact on owner competitiveness, land-use change and ecosystem service provision than climate-driven changes in land productivity. Different owner objectives and behaviour resulted in different levels of ecosystem service provision. Also, particular forest types were differently suitable for adaptation depending on the sets of objectives under which they were managed. Owners implementing particular management strategies can be differently competitive under different future scenarios, and the suitability of such strategies for adaptation is not a static, inherent characteristic of a system. Instead, it evolves in response to changing contexts that include both the external global change drivers and the internal dynamics of agent interactions. Additionally, institutional conceptual models as presented here can support better understanding of the key institutional decision-making dynamics and their consequences, endogenously, flexibly across different socio-ecological systems. Finally, study limitations, future research and the policy relevance of findings are discussed.
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James, Rachel Anne. "Implications of global warming for African climate." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:5c49af92-1739-422c-b8f2-e4433c792cc6.
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A 2°C increase in global mean temperature (ΔTg) has been widely adopted as a benchmark for dangerous climate change. However, there has been a lack of research into the implications of 2°C, or any other degree of warming, for Africa. In this thesis changes in African temperature and precipitation associated with 1°C, 2°C, 3°C, 4°C, and beyond are investigated for the first time, using output from 350 climate model experiments: a collection of simulations from international modelling centres (CMIP3), two Perturbed Physics Ensembles (PPEs), and a group of five regional models. The models project temperature and precipitation anomalies which increase in magnitude and spatial extent as global temperature rises, including a wet signal in East Africa, and drier conditions for African rainforests. The models consistently show that the evolution of change with global warming is gradual, even at 4°C and beyond; but the amplitude and direction of precipitation change at each ΔTg increment vary between models and between datasets. The PPEs project precipitation signals which are not represented by CMIP3, in particular a large drying (>0.5 mm day-1 °C-1) of western Africa. There are also important differences between global and regional models, especially in southern and West Africa (>1 mm day-1). Analysis of atmospheric circulation responses suggests that the higher resolution projections are no more credible in this case. Some of the variation between models can be understood as the result of untrustworthy simulations, leading to constraints on the PPEs, and casting doubt on the strong drying of west Sahel; but model evaluation is found to be limited by observations in the case of the Congo Basin. The implications of global warming are different depending on which models are consulted. The findings emphasise that caution should be exercised in the application of climate model data to inform mitigation debates.
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Siebenmorgen, Christopher B. "Potential climate change impacts on hydrologic regimes in northeast Kansas." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/6993.
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Master of Science<br>Department of Biological & Agricultural Engineering<br>Kyle R. Douglas-Mankin<br>The Great Plains once encompassed 160 million hectares of grassland in the central United States. In the last several decades, conversion of grassland to urban and agricultural production areas has caused significant increases in runoff and erosion. Past attempts to slow this hydrologic system degradation have shown success, but climate change could once again significantly alter the hydrology. The Intergovernmental Panel on Climate Change (IPCC) studies the state of knowledge pertaining to climate change. The IPCC has developed four possible future scenarios (A1, A2, B1 and B2). The output temperature and precipitation data for Northeast Kansas from fifteen A2 General Circulation Models (GCMs) were analyzed in this study. This analysis showed that future temperature increases are consistent among the GCMs. On the other hand, precipitation projections varied greatly among GCMs both on annual and monthly scales. It is clear that the results of a hydrologic study will vary depending on which GCM is used to generate future climate data. To overcome this difficulty, a way to take all GCMs into account in a hydrologic analysis is needed. Separate methods were used to develop three groups of scenarios from the output of fifteen A2 GCMs. Using a stochastic weather generator, WINDS, monthly adjustments for future temperature and precipitation were applied to actual statistics from the 1961 – 1990 to generate 105 years of data for each climate scenario. The SWAT model was used to simulate watershed processes for each scenario. The streamflow output was analyzed with the Indicators of Hydrologic Alteration program, which calculated multiple hydrologic indices that were then compared back to a baseline scenario. This analysis showed that large changes in projected annual precipitation caused significant hydrologic alteration. Similar alterations were obtained using scenarios with minimal annual precipitation change. This was accomplished with seasonal shifts in precipitation, or by significantly increasing annual temperature. One scenario showing an increase in spring precipitation accompanied by a decrease in summer precipitation caused an increase in both flood and drought events for the study area. The results of this study show that climate change has the potential to alter hydrologic regimes in Northeast Kansas.
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CHEN, CHEN. "Mitigation, Adaptation and Climate Change: Policy Balance under Uncertainty." Doctoral thesis, Università Cattolica del Sacro Cuore, 2011. http://hdl.handle.net/10280/1062.
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CHEN, CHEN. "Mitigation, Adaptation and Climate Change: Policy Balance under Uncertainty." Doctoral thesis, Università Cattolica del Sacro Cuore, 2011. http://hdl.handle.net/10280/1062.
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AlSarmi, Said Hamed Mohammed. "Recent climate change over the Arabian Peninsula : trends and mechanisms." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:39a76447-65a8-4e30-a4fa-70f531fc91e0.
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The global climate is changing. Compared with many parts of the world, especially North America and Europe, relatively little is known about how climate has changed over the Arabian Peninsula (AP) in recent decades. Quantifying the climate change in the mean and extreme temperature and precipitation variables and understanding the mechanisms behind these changes are essential for establishing adequate and proper adaptation strategies to ensure sustainability, reduce vulnerability and safeguard livelihoods. Four papers in this thesis contribute to that objective, utilising a combination of in situ high quality meteorological station data and high resolution regional climate model data. The first paper quantifies the trends in monthly, seasonal and annual mean, maximum, minimum temperatures and Diurnal Temperature Range (DTR) variables and total precipitation. The station dataseries are tested for quality control and homogeneity. A non-parametric test is used to calculate the trends and evaluate the trend significance for individual stations, subregions (Non-monsoonal and Monsoonal) and for the whole area average. There is a high significant increase in the temperature variables especially the minimum temperature (during 1980-2008 and over all the AP the trend of annual minimum temperature is 0.55 °C decade<sup>-1</sup> while the annual maximum temperature trend is 0.32 °C decade<sup>-1</sup>) which leads to significant decrease in the DTR. The precipitation is decline but insignificantly. The non-monsoonal region located north of 20° N has experienced higher rates of warming than the monsoonal region. Spring and summer seasons witness the highest significant warming. The interannual variability of the AP temperature and precipitation shows marked negative association after 1998. The second paper utilises the AP daily data of maximum temperature, minimum temperature and precipitation to calculate climate extremes indices, evaluate the regional/subregional trends of these indices and assess the trend significance. There is a clear significant decrease of cold temperature extremes and a significant increase in the warm temperature extremes. The increase in the nighttime temperature extremes is remarkable in the last two decades (the rate of increase of the warm night frequency is 3.6% decade<sup>-1</sup> during 1986-2008). The spatial trend patterns reveal a latitudinal distinction whereby the northern AP experiences an increase associated with day-time extremes while for the night-time extremes the trends are higher and significant for the southern region. Precipitation indices trends are weak and although they show general decrease in the last two decades they are insignificant. The changes in the Dew Point (Td) and the Mean Sea Level Pressure (MSLP) indicate possible changes in the regional dynamics. The third paper uses the Providing Regional Climates for Impact Studies (PRECIS) regional climate model forced by the European Centre for ERA-Interim re-analysis (ERA-Interim) to simulate the AP climate during 1990-2008. PRECIS simulation is validated based on climate mean and trends. The model simulation captures the mean climatic conditions and patterns, the increasing temperature tendency, as well as the decreasing precipitation observed in the last two decades. However, PRECIS has cold bias especially with the minimum temperature and it overestimates the precipitation over the high lands or regions close to them over the southwestern mountains and underestimates the precipitation over the southeastern mountains. The model products provide indications on the reasons behind the highest daytime spring warming (decrease of specific humidity) and significant nighttime summer warming (increase of Sea Surface Temperature (SST)). The model fails to simulate the recent increase of the nighttime temperature parameters over AP. The final paper addresses the possible local atmospheric circulations, SST and remote modes of variability associated with the recent AP climate extreme changes. Using the PRECIS simulation, composite difference maps for some surface, upper atmospheric circulation maps and SSTs between two period 1990-1997 and 1998-2008 have been calculated. The composite difference maps reveal significant local changes in these atmospheric and oceanic variables which possibly partly explain the recent regional warming and drying conditions during the last two decades. In addition, relationships of the regional/subregional extremes indices timeseries have been calculated with some known remote modes of variability. There is a clear, strong relation of El Niño Southern Oscillation (ENSO) with the AP climate in all the seasons except in winter. The North Atlantic Caspian Sea Pattern (NCP) influences the regional climate in winter especially the temperature variables.
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Hartmann, Gabriele Maria. "Investigation of evapotranspiration concepts in hydrological modelling for climate change impact assessment." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-30863.
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Ali, Syed Mahtab. "Climate change and water management impacts on land and water resources." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/202.
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This study evaluated the impacts of shallow and deep open drains on groundwater levels and drain performance under varying climate scenarios and irrigation application rates. The MIKE SHE model used for this study is an advanced and fully spatially distributed hydrological model. Three drain depths, climates and irrigation application rates were considered. The drains depths included 0, 1 and 2 m deep drains. The annual rainfall and meteorological data were collected from study area from 1976 to 2004 and analysed to identify the typical wet, average and dry years within the record. Similarly three irrigation application rates included 0, 10 and 16 ML/ha-annum. All together twenty seven scenarios (3 drains depths, 3 climates and 3 irrigation application rates) were simulated. The observed soil physical and hydrological data were used to calibrate and validate the model. Mean square error (R[superscript]2) of the simulated and observed water table data varied from 0.7 to 0.87. Once validated the MIKE SHE model was used to evaluate the effectiveness of 1 and 2 metre deep drains. The simulated water table depth, unsaturated zone deficit, exchange between unsaturated and saturated zones, drain outflow and overland flow were used to analyse their performance. The modeling results showed that the waterlogging was extensive and prolonged during winter months under the no drainage and no irrigation scenario. In the wet climate scenario, the duration of water logging was longer than in the average climate scenario during the winter months. In the dry climate scenario no waterlogging occurred during the high rainfall period. The water table reached soil surface during the winter season in the case of wet and average climate. For the dry climate, the water table was about 0.9 metres below soil surface during winter.One and 2 metre deep drains lowered the water table up to 0.9 and 1.8 metres in winter for the wet climate when there was no irrigation application. One metre deep drains proved effective in controlling water table during wet and average climate without application of irrigation water. One metre deep drains were more effective in controlling waterlogging a in wet, average and dry years when the irrigation application rate was 10 ML/ha-annum. With 16 ML/ha-annum irrigation application, 1 metre deep drains did not perform as efficiently as 2 metre deep drains in controlling the water table and waterlogging. In the dry climate scenario, without irrigation application, 1 metre deep drains were not required as there was not enough flux from rainfall and irrigation to raise the water table and create waterlogging risks. Two metre deep drains lowered the water table to greater depths in the wet, average and dry climate scenarios respectively when no irrigation was applied. They managed water table better in wet and average climate with 10 and 16 ML/ha-annum irrigation application rate. Again in the dry climate, without irrigation application 2 metre deep drains were not required as there was a minimal risk of waterlogging. The recharge to the groundwater table in the no drainage case was far greater than for the 1 and 2 metre deep drainage scenarios. The recharge was higher in case of 1 metre deep drains than 2 metre deep drains in wet and average climate during winter season.There was no recharge to ground water with 1 and 2 metre deep drains under the dry climate scenarios and summer season without irrigation application as there was not enough water to move from the ground surface to the unsaturated and saturated zones. When 10 ML/ha-annum irrigation rate was applied during wet, average and dry climate respectively, 1 metre deep drains proved enough drainage to manage the recharge into the groundwater table with a dry climate. For the wet and average climate scenarios, given a 10 ML/ha-annum irrigation application rate, 2 metre deep drains managed recharge better than 1 metre deep drains. Two metres deep drains with a 10 ML/ha-annum irrigation application rate led to excessive drainage of water from the saturated zone in the dry climate scenario. Two metres deep drains managed recharge better with a 16 ML/ha-annum irrigation application rate in the wet and average climate scenarios than the 1 metre deep drains. Two metres deep drains again led to excessive drainage of water from the saturated zone in dry climate. In brief, 1 metre deep drains performed efficiently in the wet and average climate scenarios with and without a 10 ML/ha-annum irrigation application rate. One metre deep drains are not required for the dry climate scenario. Two metre deep drains performed efficiently in the wet and average climate scenarios with 16 ML/ha-annum irrigation application rate. Two metre deep drains are not required for the dry climate scenario.
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Mohammadipour, Gishani Azadeh. "An Introduction to Application of Statistical Methods in Modeling the Climate Change." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175770.
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There are many unsolved questions about the future of climate, and most of them are due to lack of knowledgeabout the complex system of atmosphere, but still there are models that produce relatively realistic projectionsof the future although there are uncertainties in the presentation of them, and that's where statistical methodscould be of help. Here a short introduction is given to the projection of future climate with GCM ensembles andthe uncertainties about them, the emerging probabilistic approach, as well as the REA (Reliability EnsembleAverage) method for measuring the reliability of the model projections. In order to have an impression of theresults of the GCM ensemble results and their uncertainties the results of the weather forecast over a time periodof one year in three dierent cities of Sweden is studied as well.
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Shepherd, Anita. "Model to predict the effects of climate change on the yields of winter wheat." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309828.
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Franck, Travis Read. "Coastal communities and climate change : a dynamic model of risk perception, storms, and adaptation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54846.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 303-311).<br>Climate change impacts, including sea-level rise and changes in tropical storm frequency and intensity, will pose signicant challenges to city planners and coastal zone managers trying to make wise investment and protection decisions. Meanwhile, policymakers are working to mitigate impacts by regulating greenhouse gas emissions. To design effective policies, policymakers need more accurate information than is currently available to understand how coastal communities will be affected by climate change. My research aims to improve coastal impact and adaptation assessments, which inform climate and adaptation policies. I relax previous assumptions of probabilistic annual storm damage and rational economic expectations-variables in previous studies that are suspect, given the stochastic nature of storm events and the real-world behavior of people. I develop a dynamic stochastic adaptation model that includes explicit storm events and boundedly rational storm perception. I also include endogenous economic growth, population growth, public adaptation measures, and relative sea-level rise. The frequency and intensity of stochastic storm events can change a region's long- term economic growth pattern and introduce the possibility of community decline. Previous studies using likely annual storm damage are unable to show this result. Additionally, I consider three decision makers (coastal managers, infrastructure investors, and residents) who differ regarding their perception of storm risk. The decision makers' perception of risk varies depending on their rationality assumptions.<br>(cont.) Boundedly rational investors and residents perceive storm risk to be higher immediately after a storm event, which can drive down investment, decrease economic 3 growth, and increase economic recovery time, proving that previous studies provide overly optimistic economic predictions. Rationality assumptions are shown to change economic growth and recovery time estimates. Including stochastic storms and variable rationality assumptions will improve adaptation research and, therefore, coastal adaptation and climate change policies.<br>by Travis Read Franck.<br>Ph.D.
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Ayinde, OE, M. Munchie, and GB Olatunji. "Effect of climate change on agricultural productivity in Nigeria: A co-integration model approach." Kamla Raj Enterprise, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000781.
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Climatic fluctuation is putting Nigeria’s agriculture system under serious threat and stress. The study of the
effect of climate change on agricultural productivity is critical given its impact in changing livelihood patterns in the
country. Descriptive and co-integration analysis are the techniques used to analyze the Time series data used in this work.
The finding demonstrates that the rate in agricultural productivity is persistently higher between 1981 and 1995, followed
by a much lower growth rate in the 1996–2000 sub period. There was variation in the trend pattern of rainfall. Temperature
was not relatively constant either. The Augmented Dickey-Fuller test for unit root revealed that agricultural productivity is
not stationary and likewise the annual rainfall but became stationary after the differencing. Annual temperature on the
other hand is stationary at its level. Temperature change was revealed to exert negative effect while rainfall change exerts
positive effect on agricultural productivity. However previous year rainfall was negatively significant in affecting current
year agricultural productivity. It is recommended that if agricultural productivity was to be increased and sustained,
environmentally and agricultural sensitive technologies and innovations that can prevent climate fluctuation should be
encouraged.
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Veprauskas, Amy, and J. M. Cushing. "A juvenile–adult population model: climate change, cannibalism, reproductive synchrony, and strong Allee effects." TAYLOR & FRANCIS LTD, 2016. http://hdl.handle.net/10150/623279.
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We study a discrete time, structured population dynamic model that is motivated by recent field observations concerning certain life history strategies of colonial- nesting gulls, specifically the glaucouswinged gull ( Larus glaucescens). The model focuses on mechanisms hypothesized to play key roles in a population's response to degraded environment resources, namely, increased cannibalism and adjustments in reproductive timing. We explore the dynamic consequences of these mechanics using a juvenile- adult structure model. Mathematically, the model is unusual in that it involves a high co- dimension bifurcation at R0 = 1 which, in turn, leads to a dynamic dichotomy between equilibrium states and synchronized oscillatory states. We give diagnostic criteria that determine which dynamic is stable. We also explore strong Allee effects caused by positive feedback mechanisms in the model and the possible consequence that a cannibalistic population can survive when a non- cannibalistic population cannot.
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Dodds, Paul Edward. "Development of a crop model to examine crop management and climate change in Senegal." Thesis, University of Leeds, 2010. http://etheses.whiterose.ac.uk/1121/.
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Frequent droughts and sub-optimal crop management have been identified as the principal constraints on agricultural intensifcation in the Sahel. A new model, the Crop Model for Sahelian Adaptation Studies (CROMSAS), was developed to examine the influence of climatic variability, climate change and crop management strategies on millet yields. To improve the simulation of environmental stresses, several original features were implemented including a new leaf expansion methodology, semi-independent tillers, stress-dependent partitioning, and intercropping. CROMSAS was designed in a structured, accessible way to facilitate the use of the model by other researchers who want to examine climate change impacts in Africa. The influences of rainfall and crop management decisions over the period 1950{2009 were assessed for six locations in Senegal with average rainfall from 200mm to 1200 mm. Poor rainfall severely restricted yields in the north of the country in most years while having little impact in the more humid south. In the highly-populated groundnut basin, rainfall variability reduced the effectiveness and hence the profitability of fertiliser application. Current planting densities were found to lie within the optimal range but higher grain yields could have been produced, with lower risk of crop failure, by delaying planting by 2-3 weeks. The benefits of adapting crop management strategies according to the conditions in previous years were assessed. Using a fixed long-term strategy produced higher long-term yields and profits, at lower risk of crop failure, than frequently changing strategies. Projections from three GCMs for the period 2000-2100 were converted to daily weather data using a novel methodology and used to examine the impact of climate change on millet cultivation across Senegal. Grain yields were projected to be relatively unchanged for the SRES A2 and B1 scenarios, with losses due to temperature increases and higher vapour pressure deficits being balanced by CO2 fertilisation.