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Dunn, Katherine Margaret. "Prototyping Models of Climate Change: New Approaches to Modelling Climate Change Data. 3D printed models of Climate Change research created in collaboration with Climate Scientists." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17623.
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Prototyping Models of Climate Change: New Approaches to Modelling Climate Change Data, identifies a gap in existing knowledge on the topic of 3D Printed, three dimensional creative visualisations of data on the impact of climate change. Communication, visualisation and dissemination of scientific research data to the general-public is a priority of science organisations. Creative visualisation projects that encourage meaningful cross-disciplinary collaboration are urgently needed, from a communication standpoint and, to act as models for agile responsive means of addressing climate change. Three-dimensional creative visualisations can give audiences alternate and more direct means of understanding information by engaging visual and haptic experience. This project contributes new knowledge in the field by way of an innovative framework and praxis for the communication and dissemination of climate change information across the disciplines of contemporary art, design and science. The focus is on projects that can effectively and affectively, communicate climate science research between the disciplines and the general-public. The research generates artefacts using 3D printing techniques. A contribution to new knowledge is the development of systems and materials for 3D printing that embody principles of sustainable fabrication. The artefacts or visualisations produced as part of the research project are made from sustainable materials that have been rigorously developed and tested. Through a series of collaborations with climate scientists, the research investigates methodologies and techniques for modelling and fabricating three-dimensional artefacts that represent climate change data. The collaborations and the research outputs are evaluated using boundary object theory. Expanding on existing boundary object categories, the research introduces new categories with parameters specifically designed to evaluate creative practice- science collaborations and their outputs.
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Lanzi, Elisa <1981>. "Essays in technical change and climate change." Doctoral thesis, Università Ca' Foscari Venezia, 2010. http://hdl.handle.net/10579/949.
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Characteristics of the economy such as the share of polluting sectors, and their pollution intensity are key to understanding how to limit costs of climate policies. In particular, this thesis focuses on four different aspects. The first is the increase in climate policy costs in the presence limited sectoral malleability of capital, and thus the impossibility to reallocate capital from the “dirty” to the “clean” sector. Second, we consider the influence of the sectoral coverage of climate policies such as the EU Emissions Trading Scheme. Third, we focus on innovation within the energy sector to illustrate that there are internal dynamics that should be considered as they can lead to higher efficiency in the production of the dirty energy good. Finally, we estimate a model of directed technical change to study the effect of climate policies on the direction of technical change in the energy sector.Questa tesi é focalizzata sulla connessione tra I cambiamenti climatic, l’innovazione ed il capital fisico. Caratteristiche dell’economia di un paese come il contributo dei settori inquinanti, e la loro intensitá di inquinamento sono fondamentali per comprendere come limitare i costi delle politiche sui cambiamenti climatici. In particolare questa tesi e’incentrata su quattro aspetti. Primo, sull’aumento dei costi delle politiche sui cambiamenti climatici in presenza di una possibilitá limitata ti riallocare il capitale dai settori inquinanti a quelli“puliti”. Secondo, consideriamo l’influenza della copertura settoriale delle politiche sul cambiamento climatico come il sistema di scambio di permessi dell’UE. Terzo, ci focalizziamo sull’innovazione nel settore energetico per illustrare che ci sono dinamiche interne che dovrebbero essere considerate, poiché possono portare a un miglioramento dell’efficienza energetica. Infine, stimiamo un modello di cambiamento technologico per studiare gli effetti delle politiche sul cambiamento climatico sulla direzione del cambiamento tecnologico nel settore ambientale.
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Tidwell, Amy C. "Assessing the impacts of climate change on river basin management a new method with application to the Nile river/." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/19830.
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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2007.Committee Chair: Georgakakos, Aris; Committee Member: Fu, Rong; Committee Member: Peters-Lidard, Christa; Committee Member: Roberts, Phil; Committee Member: Sturm, Terry; Committee Member: Webster, Don.
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Engström, Gustav. "Essays on Economic Modeling of Climate Change." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-79149.
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Structural change in a two-sector model of the climate and the economy introduces issues concerning substitutability among goods in a two-sector economic growth model where emissions from fossil fuels give rise to a climate externality. Substitution is modeled using a CES-production function where the intermediate inputs differ only in their technologies and the way they are affected by the climate externality. I derive a simple formula for optimal taxes and resource allocation over time and highlight model sensitivity w.r.t the elasticity of substitution and distribution parameters. Energy Balance Climate Models and General Equilibrium Optimal Mitigation Policies develops a one-dimensional energy balance climate model with heat diffusion and anthropogenic forcing across latitudes driven by global fossil fuel use coupled to an economic growth model. Our results suggest that if the implementation of international transfers across latitudes are not possible or costly, then optimal taxes are in general spatially non-uniform and may be lower at poorer latitudes. Energy Balance Climate Models, Damage Reservoirs and the Time Profile of Climate Change Policy explores optimal mitigation policies through the lens of a latitude dependent energy balance climate model coupled to an economic growth model. We associate the movement of an endogenous polar ice cap with the idea of a damage reservoir being a finite source of climate related damages affecting the economy. The analysis shows that the introduction of damage reservoirs can generate multiple steady states and Skiba points. Assessing Sustainable Development in a DICE World investigates a method for assessing sustainable development under climate change in the Dynamic Integrated model of Climate and the Economy (DICE-2007 model). The analysis shows that the sustainability measure is highly sensitive to the calibration of the inter-temporal elasticity parameter and discount rate of the social welfare function.
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Barth, Volker. "Integrated assessment of climate change using structural dynamic models." Hamburg : Max-Planck-Inst. für Meteorologie, 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968535933.
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Sue, Wing Ian 1970. "Induced technical change in computable general equilibrium models for climate-change policy analysis." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/16783.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Technology, Management, and Policy Program, 2001.Includes bibliographical references (p. 329-352).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Policies to avert the threat of dangerous climate change focus on stabilizing atmospheric carbon dioxide concentrations by drastically reducing anthropogenic emissions of carbon. Such reductions require limiting the use of fossil fuels-which supply the bulk of energy to economic activity, and for which substitutes are lacking-which is feared will cause large energy price increases and reductions in economic welfare. However, a key determinant of the cost of emissions limits is technological change-especially innovation induced by the price changes that stem from carbon abatement itself, about which little is understood.This thesis investigates the inducement of technological change by limits on carbon emissions, and the effects of such change on the macroeconomic cost of undertaking further reductions. The analysis is conducted using a computable general equilibrium (CGE) model of the US economy-a numerical simulation that determines aggregate welfare based on the interaction of prices with the demands for and supplies of commodities and factors across different markets. Within the model induced technical change (ITC) is represented by the effect of emissions limits on the accumulation of the economy's stock of knowledge, and by the reallocation of the intangible services generated by the stock, which are a priced input to sectoral production functions.
(cont.) The results elucidate four key features of ITC: (1) the inducement process, i.e., the mechanism by which relative prices determine the level and the composition of aggregate R&D; (2) the effects of changes in R&D on knowledge accumulation in the long-run, and of contemporaneous substitution of knowledge services within and among industries; (3) the loci of sectoral changes in intangible investment and knowledge inputs induced by emissions limits; and (4) the ultimate impact of the accumulation and substitution of knowledge on economic welfare.
by Ian Sue Wing.
Ph.D.
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Möller, Thordis Sybille Wilhelma. "Climate change and European agriculture." Doctoral thesis, Humboldt-Universität zu Berlin, Landwirtschaftlich-Gärtnerische Fakultät, 2012. http://dx.doi.org/10.18452/16480.
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Die Dissertation beschäftigt sich mit den Auswirkungen des Klimawandels auf europäische Agrarmärkte im Jahre 2050, unter besonderer Berücksichtigung der Getreide- und Ölsaatenmärkte. Dazu werden die klimabedingten Änderungen der Pflanzenproduktivität des Vegetationsmodells LPJmL, welche auf fünf unterschiedlichen Klimamodellprojektionen basieren, in das Marktmodell ESIM implementiert. ESIM ist ein partielles Gleichgewichtsmodell, welches explizit Agrarmärkte der einzelnen EU-Mitgliedsstaaten simuliert. Zur Berücksichtigung der Unsicherheiten die der Klima-Einfluss-Modellierung zugrunde liegt, werden in dieser Arbeit zwei Ansätze berücksichtigt. Zunächst wird, mittels Gauss-Quadraturen, Stochastizitätin das Marktmodell implementiert, um die Unsicherheit bezüglich klimawandelbedingter steigender Ertragsvariabilität, zu berücksichtigen. Die zweite Methode verwendet die fünf individuellen Produktivitätsänderungen aus dem Vegetationsmodell, woraufhin eine Verteilung der Ergebnisse generiert wird. Darüber hinaus wird das Anpassungsverhalten der Landwirte in das Marktmodell integriert. Dies wird mittels der durch den Klimawandel veänderten Profitabilität der Ackerpflanzen berücksichtigt. Die Ergebnisse weisen darauf hin, dass die Pflanzenproduktivität innerhalb der EU, zumindest bis zum Jahre 2050, weitestgehend positiv vom Klimawandel beeinflusst wird. Die Stärke der Auswirkungen variiert jedoch stark zwischen den einzelnen Ackerpflanzen und Ländern, welche von den zugrundeliegenden Annahmen und Emissionszenarien abhängen. Diese Arbeit leistet einen Beitrag zur aktuellen Klimawandeldiskussion indem sie potentielle Schäden und Nutzen des Klimawandels auf den globalen und den europäischen Agrarsektor quantifizert. Darüber hinaus liefern die stochastische Simulation, sowie die multiplen Simualtionsläufe, ein realistisches Spektrum künftiger potentieller Auswirkungen des Klimawandels.This study aims to assess potential economic effects of climate change on European agricultural markets at member state level by 2050, focusing on cereal and oilseed markets. The future scenarios include social as well as economic developments derived from two potential emission scenarios. In this modelling framework, crop simulation results of crop productivity changes from the dynamic vegetation model LPJmL, which are based on five individual climate projections, serve as inputs which are administered as a supply shock to the European Simulation Model (ESIM). ESIM is a partial equilibrium model depicting the agricultural sector of the EU in substantial detail. Changes in yields, production quantity and crop prices by the year 2050 are simulated. In order to account for the uncertainty inherent in climate impact assessments, two approaches are considered in this thesis. First, in order to account for climate change increased yield variability, stochasticity is implemented in ESIM, using the method of Gaussian Quadratures. The second method uses the five individual LPJmL outputs to generate a distribution of results. Further, a closely connected purpose of this study is to consider climate change induced adaptation of farmers to changes in the relative profitability of crops. Simulation results indicate, that agricultural productivity in most European countries is positively affected by climate change, at least until the year 2050. However, the degree of impacts vary among crop categories and countries and are also dependent on scenario assumptions. This thesis contributes to the current discussion about climate change impacts by quantifying the potential damages and benefits that may arise from climate change on EU member state level, as well as globally. Further, the stochastic and multiple simulation results based on different future climate and emission projections deliver a more realistic spectrum of potential impacts.
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Mangal, Tara Danielle. "Developing spatio-temporal models of schistosomiasis transmission with climate change." Thesis, University of Liverpool, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526800.
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Schistosomiasis is one of the most prevalent diseases in the world and a major cause of morbidity in Africa. Accurate determination of the geographical distribution of schistosomiasis in Africa along with the number of people affected is difficult, since reliable prevalence data are often not available for most of the African continent. Effective schistosomiasis control programmes rely on accurate statistics regarding the geographical distribution of disease, the population at risk, and the intensity of disease transmission. These estimates can be obtained using a number of statistical methods which relate prevalence and intensity of disease to risk factors, measured at the individual level and at the population level. Schistosoma mansoni is largely a climatedriven parasite, which relies on the availability of a suitable snail host. The survival of parasitic infection depends on climatic variables, such as temperature, rainfall and vegetation. Statistical models which incorporate spatial or individual heterogeneity are highly complex and require large numbers of parameters. Until recently, the most common approach was to use regression modelling to identify risk factors for disease transmission. However, this method has a number of limitations. In particular, it gives no information on the dynamics of transmission, e. g. will the disease reach an endemic state under a certain set of conditions or be subject to a periodic cycle? The aim of this thesis was to a) develop mechanistic transmission models to study how schistosomiasis disease dynamics change with water temperature change and to parameterise these models to provide better estimates for a specific host-parasite combination; b) explore how the efficacy of control programmes changes with changing water temperature; c) produce continent-wide maps of schistosomiasis prevalence in Africa, using a combination of geospatial models and environmental data; d) to quantify the impact of climate change over the next 50 years on the prevalence and intensity of disease. A mechanistic model describing the transmission dynamics of schistosomiasis at a range of water temperatures was developed and showed that as the long-term mean temperature increases up to 29°C, the mean worm burden increases. At 34°C, the mean worm burden starts to taper, as the thermal limits of both the snail and the parasite are reached. Adding complexity to the models, such as snail density-dependence and adult parasite density-dependence, had no significant impact on the overall transmission patterns. However, a sensitivity analysis revealed subtle changes in the relative importance of certain parameters. The most detailed model showed that the parameters describing the transmission of schistosomes from snail to man were the most sensitive to change and therefore, provided a useful target point for control strategies. The effects of various control programmes were modelled using discrete time series models and manipulation of the individual parameters. The most effective control programme was repeated mass chemotherapy, although reducing contact with contaminated water also proved highly effective. Producing maps of geo-referenced point prevalence data highlighted the areas in which no data currently exist. This provides an invaluable tool for determining which regions need further study. Four separate geospatial models were developed to predict the distribution of schistosomiasis over Africa, and each was validated using existing data. The ordinary kriging model provided the best estimates for prevalence data and the indicator kriging model provided the best estimates for the probability of infection within a population. These models are useful for determining high-risk populations and locating areas in which control efforts should be focussed. Two types of regression models were used to investigate associations between climatic variables and prevalence of disease. Monthly rainfall and mean annual temperature were shown to have important roles in defining the limits of schistosomiasis transmission. Using these data, it is possible to define a threshold, outside which schistosomiasis transmission is unlikely to occur. These models were used to predict how the distribution of schistosomiasis would change with climate change. It was shown that over the next 50 years, there will be an increase in the number of areas able to support the intermediate vector. Without socio-economic development or intervention strategies, this will almost certainly be followed by an increase in disease transmission. The use of mathematical and geospatial models can greatly enhance our understanding of schistosome epidemiology and are an essential tool in the planning stages of any intervention strategy.
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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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Trigo, Ricardo M. "Improving meteorological downscaling methods with artificial neural network models." Thesis, University of East Anglia, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327283.
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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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Pinto, Izidine S. de Sousa. "Future changes in extreme rainfall events and circulation patterns over southern Africa." Doctoral thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/16781.
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Includes bibliographical referencesChanges in precipitation extremes are projected by many global climate models as a response to greenhouse gas increases, and such changes will have significant environmental and social impacts. These impacts are a function of exposure and vulnerability. Hence there is critical need to understand the nature of weather and climate extremes. Results from an ensemble of regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project are used to investigate projected changes in extreme precipitation characteristics over southern Africa for the middle (2036-2065) and late century (2069-2098) under the representative concentration pathway 4.5 (RCP4.5) and 8.5 (RCP8.5). Two approaches are followed to identify and analyze extreme precipitation events. First, indices for extreme events, which capture moderate extreme events, are calculated on the basis of model data and are compared with indices from two observational gridded datasets at annual basis. The second approach is based on extreme value theory. Here, the Generalized Extreme Value distribution (GEV) is fitted to annual maxima precipitation by a L-moments method. The 20-year return values are analyzed for present and future climate conditions. The physical drivers of the projected change are evaluated by examining the models ability to simulate circulation patterns over the regions with the aid of Self-Organizing Maps (SOM).
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Risbey, James S. (James Sydney). "Climate models and the validation and presentation of greenhouse change theory." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/57930.
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Margolis, Robert M. (Robert Mark). "Using energy-economic-environmental models in the climate change policy process." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/12764.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.Includes bibliographical references (p. 143-149).
by Robert M. Margolis.
M.S.
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Matthews-Pennanen, Neil. "Assessment of Potential Changes in Crop Yields in the Central United States Under Climate Change Regimes." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7017.
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Climate change is one of the great challenges facing agriculture in the 21st century. The goal of this study was to produce projections of crop yields for the central United States in the 2030s, 2060s, and 2090s based on the relationship between weather and yield from historical crop yields from 1980 to 2010. These projections were made across 16 states in the US, from Louisiana in the south to Minnesota in the north. They include projections for maize, soybeans, cotton, spring wheat, and winter wheat.
Simulated weather variables based on three climate scenarios were used to project future crop yields. In addition, factors of soil characteristics, topography, and fertilizer application were used in the crop production models. Two technology scenarios were used: one simulating a future in which crop technology continues to improve and the other a future in which crop technology remains similar to where it is today.
Results showed future crop yields to be responsive to both the different climate scenarios and the different technology scenarios. The effects of a changing climate regime on crop yields varied both geographically throughout the study area and from crop to crop. One broad geographic trend was greater potential for crop yield losses in the south and greater potential for gains in the north.
Whether or not new technologies enable crop yields to continue to increase as the climate becomes less favorable is a major factor in agricultural production in the coming century. Results of this study indicate the degree to which society relies on these new technologies will be largely dependent on the degree of the warming that occurs.
Continued research into the potential negative impacts of climate change on the current crop system in the United States is needed to mitigate the widespread losses in crop productivity that could result. In addition to study of negative impacts, study should be undertaken with an interest to determine any potential new opportunities for crop development with the onset of higher temperatures as a result of climate change. Studies like this one with a broad geographic range should be complemented by studies of narrower scope that can manipulate climatic variables under controlled conditions. Investment into these types of agricultural studies will give the agricultural sector in the United States greater tools with which they can mitigate the disruptive effects of a changing climate.
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Barrow, Elaine M. "On the construction and evaluation of scenarios of climate change for use in crop-climate models." Thesis, University of East Anglia, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297485.
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Geil, Kerrie L., and Kerrie L. Geil. "Assessing the 20th Century Performance of Global Climate Models and Application to Climate Change Adaptation Planning." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/623015.
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Rapid environmental changes linked to human-induced increases in atmospheric greenhouse gas concentrations have been observed on a global scale over recent decades. Given the relative certainty of continued change across many earth systems, the information output from climate models is an essential resource for adaptation planning. But in the face of many known modeling deficiencies, how confident can we be in model projections of future climate? It stands to reason that a realistic simulation of the present climate is at least a necessary (but likely not sufficient) requirement for a model’s ability to realistically simulate the climate of the future. Here, I present the results of three studies that evaluate the 20th century performance of global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The first study examines precipitation, geopotential height, and wind fields from 21 CMIP5 models to determine how well the North American monsoon system (NAMS) is simulated. Models that best capture large-scale circulation patterns at low levels usually have realistic representations of the NAMS, but even the best models poorly represent monsoon retreat. Difficulty in reproducing monsoon retreat results from an inaccurate representation of gradients in low-level geopotential height across the larger region, which causes an unrealistic flux of low-level moisture from the tropics into the NAMS region that extends well into the post-monsoon season. The second study examines the presence and severity of spurious Gibbs-type numerical oscillations across the CMIP5 suite of climate models. The oscillations can appear as unrealistic spatial waves near discontinuities or sharp gradients in global model fields (e.g., orography) and have been a known problem for decades. Multiple methods of oscillation reduction exist; consequently, the oscillations are presumed small in modern climate models and hence are rarely addressed in recent literature. Here we quantify the oscillations in 13 variables from 48 global climate models along a Pacific ocean transect near the Andes. Results show that 48% of nonspectral models and 95% of spectral models have at least one variable with oscillation amplitude as large as, or greater than, atmospheric interannual variability. The third study is an in-depth assessment model simulations of 20th century monthly minimum and maximum surface air temperature over eight US regions, using mean state, trend, and variability bias metrics. Transparent model performance information is provided in the form of model rankings for each bias type. A wide range in model skill is at the regional scale, but no strong relationships are seen between any of the three bias types or between 20th century bias and 21st century projected change. Using our model rankings, two smaller ensembles of models with better performance over the southwestern U.S. are selected, but they result in negligible differences from the all-model ensemble in the average 21st century projected temperature change and model spread. In other words, models of varied quality (and complexity) are projecting very similar changes in temperature, implying that the models are simulating warming for different physical reasons. Despite this result, we suggest that models with smaller 20th century biases have a greater likelihood of being more physically realistic and therefore, more confidence can be placed in their 21st century projections as compared to projections from models that have demonstrably poor skill over the observational period. This type of analysis is essential for responsibly informing climate resilience efforts.
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Sirois-Delisle, Catherine. "Modeling Future Climate Change Impacts on North American Bumblebee Distributions." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37028.
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Climate change is an important contributor to the modification of many bumblebee species’ range boundaries. It was linked to widespread decline at the southern edge of their distribution and to their inability to colonize new areas at the northern edge. Additionally, bumblebee decline is aggravated by other anthropogenic threats like land use change, agricultural practices and pathogen spillover. Predicted consequences are numerous, and could lead to severe economic and ecological impacts on human populations. A species-specific assessment of potential climate change impacts on North American bumblebees, based on the most recent global change scenarios as used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), was done for the first time. Using a massive dataset of georeferenced bumblebee observations and general circulation models, a series of species distribution models explore the impact of different climate change scenarios on climatically suitable areas of 30 bumblebee species. Northward range shifts occur in most bumblebee species’ projected climatic niches, revealing potential hotspots – places projected to be climatically suitable to multiple species – under future climate scenarios. Areas where species are likely to be lost in the absence of intervention are substantial, particularly in eastern parts of the continent. Models showed significant contractions of current ranges even under the very optimistic scenario in which all species disperse at 10 km/year. Results indicate that managed relocation as well as habitat management should be considered as a conservation strategy for some species. This research serves as a foundation for broader discussion and research in a nascent research area. It may assist in establishing localities where first conservation efforts could be directed for vulnerable bumblebee species.
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Christidis, Nikolaos. "Halocarbon radiative forcing in radiation and general circulation models." Thesis, University of Reading, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312563.
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Bocinsky, Ronald Kyle. "Landscape-based null models for archaeological inference." Thesis, Washington State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3684754.
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How do we, as humans and as scientists, learn about the world around us? In this dissertation, I explore how models--epistemological tools that connect theory and reality--not only structure scientific inquiry (including the social sciences), but also reflect how humans experience and understand the world. Using this insight enables anthropologists and other social scientists to build more ontologically powerful understandings of human behavior. Here, I focus on how humans experience physical and social landscapes--the environments in which they live and with which they interact. The dissertation consists of three studies, each of which build on the previous by adding to the complexity of modeled landscapes. The first concerns static landscapes--those that are unchanging over the temporal timescales relevant to human experience. I develop a topographically-derived index of defensibility and use it to infer defensive behavior among prehistoric populations in the Northwest Coast of North America. The second paper introduces dynamic landscapes--those that change at scales experienced by humans, but whose changes are primarily driven by external forces. An example relevant to agrarian societies is climate change. I develop a new method for reconstructing past climate landscapes and explore the potential impacts of those changes on Ancestral Pueblo maize farmers in the southwestern United States over the past two millennia. Finally, the third paper grapples with complex landscapes--dynamic landscapes in which human behaviors play important and recursive causal roles. I highlight the coevolution of locally-adapted maize varieties and human selection and cultivation strategies as an example of these types of landscapes, and develop frameworks for modeling maize paleoproductivity that can better honor the realities of Pueblo agricultural strategies.
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Yang, Wen. "Drought Analysis under Climate Change by Application of Drought Indices and Copulas." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/716.
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Drought is a recurrent extreme climate event with tremendous hazard for every specter of natural environment and human lives. Drought analysis usually involves characterizing drought severity, duration and intensity. Similar to most of the hydrological problems, such characteristic variables are usually not independent. Copula, as a model of multivariate distribution, widely used in finance, actuarial analysis, has won increasingly popularity in hydrological study. Here, the study has two major focuses: (1) fit drought characteristics from Streamflow Drought Index (SDI) or Standardized Runoff Index (SRI) to appropriate copulas, then using fitted copulas to estimate conditional drought severity distribution and joint return periods for both historical time period 1920-2009 and future time period 2020-2090. SDI is calculated based on long term observed streamflow while SRI is based on simulated future runoff. Parameters estimation of marginal distribution and copulas are provided, with goodness fit measures as well; (2) investigate the effects of climate change on the frequency and severity of droughts. In order to quantify the impact, three drought indices have been proposed for this study to characterize the drought duration, severity and intensity changes under the climate change in Upper Klamath River Basin. Since drought can be defined as different types, such as meteorological drought, agricultural drought, hydrological drought and social economical drought, this study chooses Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Surface Water Supply Index (SWSI) to estimate the meteorological, agricultural and hydrological drought, respectively. Climate change effects come from three sources: the inherent reason, the human activity and the GCMs uncertainties. Therefore, the results show the long term drought condition by calculating yearly drought indices, and compared in three ways: First, compare drought characteristics of future time periods with base period; second, show the uncertainties of three greenhouse gas emission scenarios; third, present the uncertainties of six General Circulation Models (GCMs).
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Risbey, James S. (James Sydney). "On the use of climate models to assess the impacts of regional climate change on water resources." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/57652.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1994.Includes bibliographical references (p. 207-213).
by James Sydney Risbey.
Ph.D.
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Myers, Timothy Albert. "Investigating the variability of subtropical marine boundary layer clouds in observations and climate models." Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3714206.
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Low-level clouds found over the eastern subtropical oceans have a substantial cooling effect on Earth’s climate since they strongly reflect solar radiation back to space, and their simulation in climate models contributes to large uncertainty in global warming projections. This thesis aims to increase understanding of these marine boundary layer clouds through observational analysis, theoretical considerations, and an evaluation of their simulation in climate models. Examination of statistical relationships between cloud properties and large-scale meteorological variables is a key method employed throughout the thesis. The meteorological environment of marine boundary layer clouds shapes their properties by affecting the boundary layer’s depth and structure.
It is found that enhanced subsidence, typically thought to promote boundary layer cloudiness, actually reduces cloudiness when the confounding effect of the strength of the temperature inversion capping the boundary layer is taken into account. A conceptual model is able to explain this result. Next, fundamental deficiencies in the simulation of subtropical clouds in two generations of climate models are identified. Remarkably, the newer generation of climate models is in some ways inferior to the older generation in terms of capturing key low-level cloud processes. Subtropical mid- and high-level clouds are also found to contribute more to variability in the radiation budget at the top of the atmosphere than previously thought. In the last portion of the thesis, large inter-model spread in subtropical cloud feedbacks is shown to arise primarily from differences in the simulation of the interannual relationship between shortwave cloud radiative effect and sea surface temperature. An observational constraint on this feedback suggests that subtropical marine boundary layer clouds will act as a positive feedback to global warming.
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Furtado, Jason C. "On the uncertainties and dynamics of Pacific interannual and decadal climate variability and climate change." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37302.
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Tropical and extratropical Pacific decadal climate variability substantially impact physical and biological systems in the Pacific Ocean and strongly influence global climate through teleconnection patterns. Current understanding of Pacific decadal climate variability centers around the El Niño-Southern Oscillation (ENSO), the Aleutian Low (AL), and the Pacific Decadal Oscillation (PDO). However, recent literature has highlighted the emerging roles of secondary modes of variability of the tropical and extratropical Pacific atmosphere and ocean in global climate change: the Central Pacific Warming (CPW) phenomenon, the North Pacific Oscillation (NPO), and the North Pacific Gyre Oscillation (NPGO). This work analyzes the statistics and uncertainties behind Pacific interannual and decadal-scale climate variability, and focuses on better understanding the roles of the CPW, NPO, and NPGO in the climate system.
The study begins by examining the dynamics of the NPO and its role in Pacific interannual and decadal climate variability. Results illustrate that the individual poles of the NPO have relations at high frequencies, but only the southern node contains a deterministic low-frequency component, which is forced by tropical Pacific sea surface temperature (SST) variability, as shown with a modeling experiment. The NPO-induced variability by the tropical Pacific SST is then integrated by the underlying ocean surface to form the decadal-scale NPGO signal. Thus, a new link between the CPW, the NPO, and the NPGO is formed, expanding the current framework of Pacific decadal variability and its implications for weather and climate.
The new framework of North Pacific decadal variability (NPDV) is then evaluated in 24 state-of-the-art coupled climate models. Results indicate that the models in general have difficulty reproducing the leading modes of NPDV in space and time, particularly the NPGO mode and its connection to the NPO. Furthermore, most models lack the proper connections between extratropical and tropical Pacific, for both the ENSO/AL/PDO and CPW/NPO/NPGO connections. Improvements in these teleconnections are thus needed to increase confidence in future climate projections.
The last part of the dissertation explores further the importance of the CPW mode by comparing and contrasting two popular paleoclimate SST anomaly reconstruction methods used for tropical Indo-Pacific SSTs. The first method exploits the high correlation between the canonical ENSO mode and tropical precipitation; the second method uses a multi-regression model that exploits the multiple modes of covariability between tropical precipitation and SSTs, including the CPW mode. The multi-regression approach demonstrates higher skill throughout the tropical Indo-Pacific than the first approach, illustrating the importance of including the CPW phenomenon in understanding past climates.
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Marke, Thomas. "Development and Application of a Model Interface to couple Land Surface Models with Regional Climate Models for Climate Change Risk Assessment in the Upper Danube Watershed." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-91622.
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Marke, Thomas. "Development and application of a model interface to couple land surface models with regional climate models for climate change rusk assessment in the upper danube watershed." kostenfrei, 2008. http://edoc.ub.uni-muenchen.de/9162/.
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O'Hara, Jeffrey Keith. "Water resources planning under climate change and variability." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3259069.
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Thesis (Ph. D.)--University of California, San Diego, 2007.Title from first page of PDF file (viewed June 21, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Endris, Hussen Seid. "Assessing the representation of teleconnective drivers of rainfall over Eastern Africa in global and regional climate models and projected future changes." Doctoral thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/24454.
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Climate variability is an important characteristic of regional climate, and a subject to significant control from teleconnections. An extended diagnosis of the capacity of climate models to represent remote controls of regional climate (teleconnections) is vital for assessing model-based predictions of climate variability, understanding uncertainty in climate projections and model development. An important driver of climate variability for Africa is the sea surface temperature (SST) - rainfall teleconnection, such as the El Ni˜no/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). In this study, an assessment of the teleconnection between tropical SSTs and Eastern African rainfall in global and regional climate models is presented, with particular attention paid to the propagation of large-scale teleconnection signals (as represented by model reanalyses and Coupled Global Climate models (CGCMs)) into the domain of the Regional Climate Models (RCMs). The teleconnection-rainfall relationship with the Eastern Africa region is assessed in two rainfall seasons (June-July-August-September and October-November- December) under present and future periods. Evaluation runs (RCMs driven by reanalysis datasets) and historical simulations (RCMs driven by CGCMs) are assessed to quantify the ability of the models to capture the teleconnection relationship. The future analysis is performed for two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) to assess future change in this relationship as a result of global warming. Using ERA-interim reanalysis as perfect boundary conditions, the RCMs adequately simulate the spatial and temporal distribution of rainfall in comparison with observations, although the model performance varies locally and seasonally within the region. Furthermore, the RCMs correctly capture the magnitude and spatial extent regional-scale seasonal rainfall anomalies associated with large-scale oceanic modes (ENSO and IOD). When the lateral boundary conditions are provided by CGCMs, RCMs barely capture the regional teleconnection patterns associated with large-scale modes, and mostly depend on the selection of the driving CGCM. Comparison of the CGCM-driven RCM simulations with the reanalysis-driven RCM simulations revealed that most of the errors in teleconnection found in the RCM simulations are inherited from the host CGCMs. The ERA-Interim driven downscaled results show better agreement with observed spatial teleconnection patterns than the CGCM driven downscaled results. Analysis of the CGCMs and corresponding downscaled results showed that in most cases both the CGCM and the corresponding downscaled simulations had similar teleconnection patterns, but in some cases the RCM results diverge to those of the driving CGCM results. It has been demonstrated that similarities in SST-rainfall teleconnection patterns between the RCM simulations and respective driving CGCM simulations are noted over the equatorial and southern part of the region during OND season, where the rainfall is primarily controlled by large-scale (synoptic-scale) features, with the RCMs maintaining the overall regional patterns from the forcing models. Di↵erences in RCM simulations from corresponding driving simulations are noted mainly over northern part of the domain during JJAS, which is most likely related to mesoscale processes that are not resolved by CGCMs. Looking at the model projections of the future, although the spatial pattern of teleconnections between ENSO/IOD and rainfall still persist, important changes in the strength of the teleconnection have been found. During JJAS, ENSO is an important driver of rainfall variability in the northern parts of the region where dry anomalies are associated with El Ni˜no and wetter anomalies with La Ni˜na. Both regional and global ensemble projections show higher rainfall during La Ni˜na and lower rainfall during El Ni˜no over the northern part of the region compared to the present period. During OND, the teleconnection between ENSO/IOD and rainfall is projected to strengthen (weaken) over Eastern horn of Africa (southern parts of the region) compared to the present period. This implies heavy seasonal rains associated with positive phases of ENSO and IOD will increase in future across the Eastern horn of Africa. The change OND rainfall teleconnections are stronger and also more consistent between the models and scenarios as compared to the change in JJAS teleconnections. These findings have an important implication for the water and agricultural managers and policies in the region to tackle the anticipated droughts and floods associated anthropogenic climate change. Finally, the analysis demonstrated that the largest source of uncertainty in the regional climate model simulations in the context of teleconnective forcing of rainfall over Eastern Africa is the choice of CGCM used to force the RCMs, reinforcing the understanding that the use of a single GCM to downscale climate predictions/projections and using the downscaled product for assessment of climate change projections is insufficient. Simulations from multiple RCMs nested in more than one GCM, as is undertaken in the Coordinated Regional Downscaling Experiment (CORDEX), are needed to characterize the uncertainty and provide estimates of likely ranges of future regional climate changes.
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Alagador, Diogo André Alves Salgado Rodrigues. "Quantitative methods in spatial conservation planning integrating climate change and uncertainties." Doctoral thesis, ISA/UTL, 2011. http://hdl.handle.net/10400.5/3877.
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Doutoramento em Biologia - Instituto Superior de AgronomiaSpatial Conservation Planning is a scientific-driven procedure to identify cost effective networks of areas capable of representing biodiversity through time. This conceptually simple task accommodates sufficient complexity to justify the existence of an active research line with more than 20 years already. But costefficiency and representation of biodiversity is only part of the whole challenge of Spatial Conservation Planning.The recognition that Nature operates dynamically has stimulated researchers to embrace the additional challenges of developing methods to make conventional (static) conservation approaches more dynamic and therefore increase the chances that biodiversity are preserved in the longer term. In this thesis, I present a set of tools to assist spatial conservation decision-making and address issues such as uncertainty and spatial dynamics of species ranges. These two topics are particularly relevant in the context of ongoing climate changes. I start by investigating two connectivity paradigms for the identification of conservation areas. In the first, a distance-based approach is applied for the identification of areas representing a set of species. In the second, I present a conceptual framework based on the analysis of environmental similarity between protected areas. The framework seeks to identify effective spatial linkages between protected areas while ensuring that these linkages are as efficient as possible. Then, I introduce a methodology to refine the matching of species distributions and protected area data in gap analysis. Forth, I present a comprehensive assessment for the expected impacts of climate change among European conservation areas. Finally, I address a framework for cost-efficient identification of the best areas that, in each time period, assist species’ range adjustments induced by severe climate changes. There exists a wealth of theoretical insight and algorithmic power available to ecologists. This thesis took advantage of it and (I hope) it offers useful guidance for genuine biodiversity protection.
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Kellner, Juliane [Verfasser]. "Coupling agricultural plant growth and hydrological models for climate change projections / Juliane Kellner." Gießen : Universitätsbibliothek, 2019. http://d-nb.info/1196525773/34.
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Barnuud, Nyamdorj Namjildorj. "Determining climate change impacts on viticulture in Western Australia." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1677.
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Global climate model simulations indicate 1.3°C to 1.8°C increase in the Earth’s average temperature by middle of this century above the 1980 to 1999 average. The magnitude and rate of change of this projected warming is greater than the average warming during the last century. Global climate models project an even higher degree of warming later in the century also due to increasing grrenhouse gases concentrations in the atmosphere from human activity. Impacts of future climate change on viticulture are likely to be significant as viticulture requires a narrow climate range to produce grapes of suitable quality for premium wine production.In this thesis, impacts of climate change on winegrape growing conditions across the Western Australian wine regions were spatially and temporally examined by utilising fine resolution downscaled climate projections. Relationships between climate variation and grape maturity or key quality attributes of Cabernet Sauvignon, Shiraz and Chardonnay were modelled from measured fruit and climate data along a natural climate gradient encompassing a 5°C range in winegrape growing season average temperature. Potential future climate change impacts on grape quality were quantitatively evaluated by driving the grape quality models with the downscaled climate projections.Analyses of climate conditions for winegrape growth were carried out under future climate projections for the Western Australian wine regions. A total of 10 global climate models forced with an A2 emission scenario were downscaled. Of these models, the MEDRES Miroc3.2 and CSIRO Mk3.5 climate models, which indicated the low and high warming ranges (projections of these models will be referred as low and high range warming, hereafter) across the study regions, were selected to take into account the uncertainty of future climate change for impact assessment. Our results indicate increasingly warmer and drier climate conditions for the Western Australian wine regions. The current October to April average temperature (averaged across the regions) is projected to be 0.5°C to 1.5°C warmer by 2030, respectively. The magnitude of the warming will likely be uneven across the regions. For example, 0.1 to 0.3°C higher average temperature during October to April period has been projected for the northern regions than the southern regions by 2030, depending on the warming ranges. On the other hand, rainfall is projected to decrease across the regions under the future scenario we assessed in this study. By 2030, annual rainfall, averaged across the regions, is projected to decline by 5 to 8%, respectively, under the low and high warming ranges of climate change under the A2 emission scenario. Among seasons, the greatest decline in rainfall is projected to occur during spring. On average, up to 8% and 19% decline in spring rainfall is projected respectively under the low and high warming ranges by 2030.The magnitude of these changes are projected to increase as time progresses. For example, by 2070, averaged across the study regions, our modelling results show current mean temperature during October to April is projected to be between 1.1°C and 3.9°C warmer, but the annual rainfall is likely to be 15 to 24% lower than the current climate averages (1975 to 2005) under the A2 scenario.Maturity dates of the studied varieties are projected to advance asymmetrically across the study regions. For example, Cabernet Sauvignon may reach 22 °Brix total soluble solid maturity about 4 and 7 days earlier respectively for the northern and the southern regions by 2030 under the low warming range. Our results also indicate maturity date shifting a further 8 and 18 days earlier by 2070 for the northern and the southern regions respectively under the same warming range. Patterns of this maturity date shifting is likely to be similar under the high warming range. However, the magnitude of advancement is projected to be doubled.If no adaptive measures are implemented future climate change will likely reduce wine quality due to declining concentrations of berry anthocyanins and acidity under a warmer climate. The reductions of berry quality attributes are likely to be more pronounced in the warmer northern wine regions compared to the cooler southern regions. For example, Cabernet Sauvignon current median anthocyanins concentration is projected to decline by about 12% and 33% for the warmer northern regions, and about 6 to 18% for the cooler southern wine regions respectively by 2030 and 2070 under the high warming range. In contrast, the maximum decline in Cabernet Sauvignon anthocyanin concentration under the lower warming range is projected to be small, up to 5% for the cooler southern and up to 8% for the warmer northern regions by 2070. Shiraz anthocyanins concentration decrease pattern is similar to that of Cabernet Sauvignon, however, our modelling indicates the magnitude is smaller, with maximum of 18% for Swan District and about 11% for the southern regions by 2070 under the high warming range.Modelled impacts of climate change on grape titratable acidity are also region and variety specific. Among the varieties studied, Chardonnay exhibits the highest decline in median titratable acidity across the regions (17% for the Margaret River and 42% for the Swan District regions), followed by Shiraz (7% for the Margaret River and 15% for the Peel regions) and Cabernet Sauvignon (no change for Blackwood and 12% for the Swan District regions) by 2070 under high climate warming. On the other hand, the median titratable acidity levels are less impacted by low warming scenario (maximum decline is 4% for Shiraz only by 2070).Under the future warming scenarios studied in this thesis currently established wine regions and wine styles across the Western Australian wine regions are likely to be affected to the extent that some regions may not be conducive to premium wine production, while for some regions changing the variety may be the only option to adapt to the climate change. For example, by 2070, under high warming range Swan District, Perth Hills, and some parts of the Peel and Geographe regions are projected to be suited more to producing fortified wines or table grapes due to high average growing season temperature (>24°C). In this future climate the present cool climate southern regions are likely to have the same climate conditions that currently prevail in the warmer Swan District. Apparent differences in currently planted varieties between the cooler southern and warmer northern regions clearly indicate the need to adapt to the warming climate in the southern wine regions.Analysis of other potential factors that influence viticulture such as frequency of hot days, vapour pressure deficit and disease pressure were examined. The results indicated that winegrape fungal disease pressure will likely decrease across the regions due to the declining rainfall, potentially lessening the need for spraying during the growing season. On the other hand, there will likely be increased frequency of hot days and elevated vapour pressure deficit. The impacts of these, combined with the decreasing rainfall during growing season will potentially drive irrigation demand higher requiring altered water management under climate change.Climatically, most of the Western Australian wine regions are known as premium wine producing areas. The results from this study indicate potential challenges of climate change for the Western Australian wine industry. Under the future climate scenarios examined, some currently warmer regions may become less suitable for premium quality wines due to the increased temperature, which is projected to be out of the optimum temperature range for premium wine production. For most of the other regions, the challenge will likely be a decreased grape quality required to produce premium wine with the current varieties. Suitable adaptation strategies may be required to maintain the current market reputation. Furthermore, the warmer and drier conditions under climate change is likely to necessitate revised water management across the wine growing regions, especially some regions which are already limited by available water for grape production. However, the magnitude of the impacts is projected to be dependent upon the magnitude of future climate change.
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Vazquez, Heather. "Evaluating Changes to Natural Variability on a Warming Globe in CMIP5 Models." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3737.
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Global mean surface temperatures (GMST) warmed in the early 20th century, experienced a mid-century lull, and warmed again steadily until 1997. Observations at the turn of the 21st century have revealed another period of quiescent warming of GMSTs from 1998 to 2012, thus prompting the notion of a global warming “hiatus”. The warming hiatus occurred concurrently with steadily increasing atmospheric greenhouse gas concentrations, sea level rise, and retreating arctic sea ice. The occurrence of the warming hiatus suggests that natural variability continues to be a sizable contributor to modern climate change and implies that energy is rearranged or changed within the climate system. Much of the scientific research conducted over the last decade has attempted to identify which modes of natural variability may be contributing to the GMST signal in the presence of anthropogenic warming. Many of these studies concluded that natural variability, operating in the global oceans were the largest contributors to GMST. What remains unclear is how oceanic variability and its contribution to GMST may change on a warmer globe as greenhouse gas concentrations continue to rise.
Our research includes diagnostic analyses of the available observational surface temperature estimates and novel state-of-the-art climate model experiments from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Our analyses seek to understand how the natural modes of variability within the ocean will change under different warming scenarios. Utilizing simulations forced with observed pre-industrial and historical greenhouse gas emissions in combination with several future warming simulations, we quantify the probability of similar “hiatus-like” periods occurring on a warmer globe. To that end employ various metrics and detrending techniques including EOF decomposition, running climatologies, along with linear and nonlinear trends to elucidate how natural variability changes over time. We also examine the changing influence of natural modes of variability with respect to the anthropogenic radiative forcing over different regions on the globe.Results suggest that natural variability for much of the global oceans decreases as the radiative forcing increases in the future warming scenarios.
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Das, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.
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Computer and Information SciencePh.D.
Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommodate extremes and other domain constraints such as dynamical relations among variables, spatial and temporal constraints, need to provide uncertainty estimates and feature correlations, among others. We adopted a hierarchical Bayesian version of the sparse regression framework and exploited its inherent flexibility to accommodate the constraints. We applied sparse regression for the feature selection problem of statistical downscaling of the climate variables with particular focus on their extremes. This is important for many impact studies where the climate change information is required at a spatial scale much finer than that provided by the global or regional climate models. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and inform extremes downscaling. We propose a general-purpose sparse Bayesian framework for covariate discovery that accommodates the non-Gaussian distribution of extremes within a hierarchical Bayesian sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates and provide uncertainty estimates, using a variational Bayes approximation. The method is applied for selecting informative atmospheric covariates at multiple spatial scales as well as indices of large scale circulation and global warming related to frequency of precipitation extremes over continental United States. Our results confirm the dependence relations that may be expected from known precipitation physics and generates novel insights which can inform physical understanding. We plan to extend our model to discover covariates for extreme intensity in future. We further extend our framework to handle the dynamic relationship among the climate variables using a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP). The extended model can achieve simultaneous clustering and discovery of covariates within each cluster. Moreover, the a priori knowledge about association between pairs of data-points is incorporated in the model through must-link constraints on a Markov Random Field (MRF) prior. A scalable and efficient variational Bayes approach is developed to infer posteriors on regression coefficients and cluster variables.
Temple University--Theses
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Martin, M. J. "Models of the interactive effects of rising ozone, carbon dioxide and temperature on canopy carbon dioxide exchange and isoprene emission." Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339238.
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Teutschbein, Claudia. "Hydrological Modeling for Climate Change Impact Assessment : Transferring Large-Scale Information from Global Climate Models to the Catchment Scale." Doctoral thesis, Stockholms universitet, Institutionen för naturgeografi och kvartärgeologi (INK), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-84197.
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A changing climate can severely perturb regional hydrology and thereby affect human societies and life in general. To assess and simulate such potential hydrological climate change impacts, hydrological models require reliable meteorological variables for current and future climate conditions. Global climate models (GCMs) provide such information, but their spatial scale is too coarse for regional impact studies. Thus, GCM output needs to be downscaled to a finer scale either through statistical downscaling or through dynamic regional climate models (RCMs). However, even downscaled meteorological variables are often considerably biased and therefore not directly suitable for hydrological impact modeling. This doctoral thesis discusses biases and other challenges related to incorporating climate model output into hydrological studies and evaluates possible strategies to address them. An analysis of possible sources of uncertainty stressed the need for full ensembles approaches, which should become standard practice to obtain robust and meaningful hydrological projections under changing climate conditions. Furthermore, it was shown that substantial biases in current RCM simulations exist and that correcting them is an essential prerequisite for any subsequent impact simulation. Bias correction algorithms considerably improved RCM output and subsequent streamflow simulations under current conditions. In addition, differential split-sample testing was highlighted as a powerful tool for evaluating the transferability of bias correction algorithms to changed conditions. Finally, meaningful projections of future streamflow regimes could be realized by combining a full ensemble approach with bias correction of RCM output: Current flow regimes in Sweden with a snowmelt-driven spring flood in April will likely change to rather damped flow regimes that are dominated by large winter streamflows.
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Streilein, Andrea Susan. "Making sense of change : how place-specific cultural models and experiential influencers are shaping understandings of climate change in two BC coastal communities." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2647.
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Global climate change has become the imminent issue of our time. Recent literature has stressed the pressing need for adaptation planning, particularly for communities that are most vulnerable to new climatic variations, such as resource dependent and coastal communities. Yet, such cries for adaptation have often glossed over the need for prior examination into the underlying cultural mindsets of such communities. In response, this thesis has sought to examine the various factors that are influencing local understandings of global climate change by leaders in two British Columbia coastal communities, Port Alberni and the Tseshaht First Nation.
Guided by a social (or ecological) constructionist lens and a phenomenological methodological approach, a series of in-depth interviews were conducted with the leadership, both formal and informal, of the two aforementioned B.C. communities during the summer of 2006. Although each community yielded distinct findings, the interviews captured richly nuanced descriptions of local environmental changes, which in turn played a sizeable role in shaping how the leaders conceptualized climate change. A plethora of place-specific historical, experiential and values-based factors interacted and moulded the many contextual culturalmodels (from tsunamis, to recycling, to colonial pasts to reverence for nature), which were imbedded within leaders' discussions of climate change.
Following this core analysis, I explored the community capacity to manage and adapt to future changes by examining local strengths and challenges. The concluding chapter provided a reflection of the results and pointed to new directions.
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Mitchell, Timothy D. "An investigation of the pattern scaling technique for describing future climates." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390635.
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Onyango, Esther Achieng. "Climate Change and Malaria: An Integrated Risk Assessment of Rural Communities in East Africa." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/370358.
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Climate change is the biggest global health threat of the twenty-first century, responsible directly or indirectly for approximately 12.6 million of all deaths globally and projected to cause an additional 250,000 deaths each year. A key area of concern is how climate change will influence the incidence, and spread of infectious diseases such as malaria. Approximately 3.3 billion, or half of the world’s population, are at risk from malaria and under climate change projections; this number is estimated to rise by 1.6 million by the year 2030 and by 1.8 million by the year 2050. Thus, understanding climate change and malaria risk is of significant public health concern, and this is particularly important for East Africa where current research shows that the disease is already spreading into areas where communities were previously unexposed to the disease.
Studies on the impact of climate change on malaria transmission show that even the smallest variation in temperature can have an exponential change in the transmission of the disease. Temperature changes facilitate faster development of the parasite within the mosquito, faster reproduction of the mosquitoes, and more biting by the mosquitoes. Rainfall is also a driver of malaria transmission and can influence the development of the mosquito by creating suitable habitats for larvae and increasing mosquito abundance. Malaria is a disease with a complex transmission cycle which is also influenced at the local level, mainly by land cover, land use and land use change. These changes can introduce the mosquito into new locations, thus extending their range, creating suitable micro-habitat conditions for mosquitoes to breed, and also increasing mosquito abundance.
While climate change does influence the global distribution of malaria, the spatial extent within regions will be determined by local land use factors and by other non-climatic factors. The latter include biological, social, demographic and cultural factors, along with human behaviour, drug resistance and public health interventions. At a local level, these factors can influence malaria transmission independently or by modifying the effects of climate change. Therefore, quantifying and understanding the impact of climate change needs consideration of the interactions between these other factors, climate change and malaria transmission.
While there exist multiple lines of evidence for the influence of climate change on malaria and the risk posed to vulnerable communities, there is insufficient understanding of the factors influencing the spread of the disease at the community level. There is a need for more robust risk assessments that not only consider the impact of climate change on malaria transmission, but also consider differences in topography, characteristics of the landscape, land use activities and other factors influencing risk. An integrated risk assessment is suitable as this can incorporate, at a biophysical level, an understanding of how climate change will impact on the current burden of the disease and at a social level, identify vulnerable populations, how susceptible they are to this risk and their capacity to respond. This PhD study therefore aims to determine risk of malaria infection in a highland and a lowland rural community in East Africa, in the context of climate change, climate variability, land use and other local factors to suggest suitable adaptation strategies.
This study adopted a participatory systems approach, incorporating trans-disciplinary thinking from climate change science, malaria ecology and epidemiology, land use and land use change, social science and public health. Stakeholder engagement and contribution at different levels was used to provide useful and context-specific insights into factors influencing malaria risk at a community level. A mix of quantitative and qualitative data was collected in western Kenya, between August 2014 and February 2015, through focus group discussions, key informant interviews and secondary data analysis. This data was then analysed and integrated using Bayesian belief network models to estimate risk of malaria infection under current and future climate conditions and to evaluate the efficacy of different adaptation options in reducing this risk.
The results of the Bayesian belief network model showed that at the highland study site, there was a significant increase in risk under future climate scenarios, but not so in the lowland study site. This difference in risk is mainly driven by changes in temperature. The model also showed that this risk is seven and a half times more significant if the influence of local factors, such as perception, health-seeking behaviour, information provision and utility, malaria prevention and malaria treatment are not considered in the model.
This thesis identified three areas of interventions as main adaptation options: i) reducing exposure; ii) decreasing generic susceptibility and iii) increasing coping capacity. It further determined that in order to achieve sustainable adaptation strategies, it is critical to consider: i) community engagement; ii) multi-sectoral collaboration, ii) integrated early warning systems, and; iv) gender-differentiated vulnerability. Collaboration and integration between sectors will lead to stronger and more sustainable programs, and engaging the community early on during the risk assessment and adaptation process ensures that their views and needs are included into adaptation solutions, which will increase the prospects for long term program success and sustainability. This study has demonstrated that local stakeholders’ values and interests will influence different adaptation outcomes. This highlights the importance of tailoring adaptation strategies to local circumstances, which has useful implications for development of climate change and health policy.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Environment
Science, Environment, Engineering and Technology
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Samson, Jason. "Forecasting the impacts of climate change with non-stationary models of regional population density." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104602.
Full textAbstract:
Ecological forecasts under climate change are essential to inform biodiversity conservation plans but their non-falsifiable nature requires a thorough evaluation of their framework. Most ecological forecasts under climate change use ecological niche models that correlate environmental variables with the presence or regional density of a species, assuming that the current environmental niche occupied by a species can be used to anticipate its response to environmental change. Despite the large number of ecological forecasts under climate change that have been recently published, most of them are limited to predicting presence using climate variables as predictors. Here I evaluate the importance of incorporating non-climate predictors of regional density in ecological niche models. Given the greater spatial heterogeneity in regional density data compared to presence data, I used geographically weighted regression (GWR), an ecological niche model providing a spatially-explicit description of the influence of ecological predictors. I focused on North American beaver (Castor canadensis) regional density in Québec, Canada, and on human (Homo sapiens) populations in the United States and across the world because of the availability of accurate data on regional density patterns as well as potential non-climatic correlates of regional density. The influence of non-climate predictors of beaver regional density was very important in six commonly used ecological niche models. GWR models of beaver regional density performed as well as the other six ecological models used and the spatial representations of the influence of ecological predictors obtained with GWR models were broadly congruent with current ecological knowledge of beavers. Approximately half the variation in human regional density across the world was explained by GWR models based on climate conditions. Combining these GWR models with forecasted climate change models and forecasted demographic change models led to the first spatially-explicit, global human vulnerability index to climate change. There was a significant negative correlation between the predicted vulnerability to climate change and the per-capita CO2 emissions, suggesting a moral hazard in international climate change policies. The demographic trends in the United States during the 20th century were more strongly correlated with climate variables than socio-economical variables. Additionally, the regional demographic trends were such that the average climate conditions experienced by American citizen became hotter and drier throughout the century. The demographically driven temperature change was six times greater than the natural temperature change. Non-stationary ecological niche models of regional density represent a useful tool in the development of climate change forecasts and adaptation policy for biodiversity in general and human societies in particular.Les prédictions écologiques dans un contexte de changements climatiques sont essentielles pour l'élaboration de plans de gestion de la biodiversité. Par contre, il est important de s'assurer qu'elles sont conçues de manière appropriée puisqu'elles sont scientifiquement infalsifiables. La majorité de ces prédictions utilise des modèles de niche écologique basés sur des corrélations entre la présence ou la densité régionale d'une espèce et des variables environnementales, en supposant que la niche écologique actuelle d'une espèce peut nous permettre d'anticiper sa réaction face à des conditions environnementales différentes. Malgré le nombre important d'études sur le sujet, la plupart d'entre elles se limite à prédire la distribution d'une espèce en fonction de variables climatiques. J'évalue dans cette thèse l'importance de variables non-climatiques dans les modèles de niche écologique de densité régionale. Compte tenu que la densité régionale d'une espèce a une plus grande hétérogénéité spatiale que sa simple présence, j'ai utilisé des régressions pondérées géographiquement (RPG). Ces RPGs sont des modèles de niche écologique qui permettent de visualiser spatialement l'influence des variables utilisées pour modéliser la niche écologique d'une espèce. Mes analyses sont basées sur le castor (Castor canadensis) au Québec et sur les populations humaines (Homo sapiens) aux États-Unis et sur la planète parce que nous avons des données précises tant sur leur densité régionale que sur les facteurs environnementaux qui peuvent influencer leur densité régionale. Les densités régionales du castor étaient fortement influencées par des facteurs non-climatiques selon six modèles de niche écologique fréquemment utilisés. Les RPGs ont été aussi performant que ces six modèles de niche écologique et la représentation spatiale de l'influence des variables utilisées pour décrire la niche écologique du castor est en accord avec nos connaissances écologiques actuelles. Des RPGs basées sur quelques variables climatiques m'ont permis d'expliquer environ la moitié de la variation des densités régionales humaines sur la planète. J'ai créé le premier indice global et quantitatif des vulnérabilités humaines aux changements climatiques en combinant ces RPGs avec des prévisions démographiques des Nations-Unies. Les vulnérabilités prédites par mes modèles sont significativement négativement corrélées avec les émissions de CO2 per-capita; ce qui suggère un risque subjectif inhérent dans les négations internationales sur les changements climatiques. Les tendances démographiques aux États-Unis durant le 20ième siècle étaient plus fortement corrélées avec des variables climatiques qu'avec des variables socio-économiques. De plus, les tendances démographiques régionales ont fait que la température ressentie par les américains a augmenter au cours du dernier siècle. Ces changements de températures ressentie causés par les tendances démographiques régionales sont six fois plus important que les changements de températures d'origine naturelle. Les modèles de niche écologique spatialement non-stationnaire de densité régionale représente un outil important dans le développement de prédictions écologiques dans un contexte de changements climatiques et peuvent contribuer à l'amélioration des politiques d'adaptations aux changements climatiques tant pour la biodiversité que pour les sociétés humaines.
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MISTRY, MALCOLM. "Impacts of climate change and variability on crop yields using emulators and empirical models." Doctoral thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10278/3716714.
Full textAbstract:
La tesi valuta gli impatti dei cambiamenti climatici e della variabilità climatica sulla produttività agricola a scala regionale e globale analizzando dati ad alta risoluzione spaziale con metodi econometrici. La tesi utilizza dati provenienti da sei modelli globali delle rese agricole per quattro coltivazioni non irrigate (mais, riso, soia, e grano) per costruire un emulatore da integrare in modelli di valutazione integrata (IAMs).
La prestazione dell’emulatore statistico è valutata su scala regionale utilizzando modelli empirici basati su osservazioni storiche per gli Stati Uniti. Il Capitolo 1 fornisce il contesto della ricerca esistente e descrive le metodologie disponibili nell’ambito
dell’agronomia. Introduce la motivazione e gli obiettivi della ricerca sviluppata nei capitoli successivi. Il Capitolo 2 discute i dati, la metodologia usata per sviluppare un semplice emulatore statistico della funzione di risposta delle rese agricole a shock meteorologici simulati da modelli di processo. Per facilitare l’integrazione dell’emulatore in modelli IAMs, questo capitolo testa un modello semplice ad effetti fissi con l’interazione con trend temporali. Il Capitolo 3 esplora delle varianti del modello base che esplorano 1) altre variabili esplicative 2) variazioni geografiche in base a diverse aree agronomiche (Agro-Ecological Zones, AEZs), 3) il ruolo della dipendenza spaziale nei dati. Il Capitolo 4 confronta la performance dell’emulatore statistico
calibrato sui dati dei modelli di processo con dei modelli empirici basati su dati storici. Il confronto analizza i dati per gli Stati Uniti. Si basa sul modello base sviluppato nel Capitolo 2 e dati storici per gli Stati Uniti dal Dipartimento dell’Agricoltura (USDA).
Nel loro insieme i tre capitoli 2-4 affrontano diverse importanti domande: 1) come si caratterizzano le funzioni di risposta in forma ridotta stimate a partire da dati generati da modelli di processo 2) come queste variano geograficamente e in base al modello che genera i dati 3) come queste differiscono rispetto a funzioni di risposta stimate a partire dai dati osservati storicamente e 4) quali sono le implicazioni per l’analisi del rischio climatico. Il Capitolo 5 conclude la tesi con un riassunto dei contributi chiave e suggerimenti per lavori futuri.The thesis assesses impacts of climate change and variability on regional and global crop yields using econometric approaches to analyze global gridded data. Using a large dimension panel data of six Global Gridded Crop Models (GGCMs) for four rainfed crops (maize, rice, soybeans and wheat) an emulator suitable/amenable of being integrated into Integrated Assessment Models (IAMs) is built. The performance of the emulator is evaluated against observational-based, empirical models at regional scale by building a statistical model calibrated on historical observed crop yields data for United States (U.S.) counties. Chapter 1 provides the background of existing research methodologies in agronomic literature. The gaps in existing research and scope for research are laid down as motivation and objectives of the research that follows in the subsequent chapters. Chapter 2 discusses the data, methodology and framework used in the construction of a simple statistical emulator of the response of crops to weather shocks simulated by crop models. To facilitate the integration of the emulator into IAMs, the simplest model using a base specification of linear fixed effect with time trend interactions is developed. Chapter 3 investigates modifications to the base specification with a series of robustness checks exploring the suitability of an additional predictor variable, the stratification of coefficients geographically by groups of Agro-Ecological Zones (AEZs); and most importantly, the role of spatial dependence in variables by applying a spatial model. Chapter 4 compares the performance of the statistical emulator calibrated on crop model results, with an empirical models of crop responses based on historical data. The comparison focuses on U.S. counties. The base specification from Chapter 2 together with historical observed data from the U.S. Department of Agriculture (USDA), are utilized in an inter-comparison exercise for divergence in results and subsequent implications. Collectively, the three chapters (2-4) address several important questions: (1) what do reduced-form statistical response surfaces trained on crop model outputs from various simulation specifications look like; (2) do model-based crop response functions vary systematically over space (e.g., crop suitability zones) and across crop models?, (3) how do model-based crop response functions compare to crop responses estimated using historical observations? and (4) what are the implications for the characterization of future climate risks? Chapter 5 concludes the thesis providing a summary of key contributions and suggestions for future work.
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Mistry, Malcolm Noshir <1977>. "Impacts of climate change and variability on crop yields using emulators and empirical models." Doctoral thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/10345.
Full textAbstract:
The thesis assesses impacts of climate change and variability on regional and global crop yields using econometric approaches to analyze global gridded data. Using a large dimension panel data of six Global Gridded Crop Models (GGCMs) for four rainfed crops (maize, rice, soybeans and wheat) an emulator suitable/amenable of being integrated into Integrated Assessment Models (IAMs) is built. The performance of the emulator is evaluated against observational-based, empirical models at regional scale by building a statistical model calibrated on historical observed crop yields data for United States (U.S.) counties. Chapter 1 provides the background of existing research methodologies in agronomic literature. The gaps in existing research and scope for research are laid down as motivation and objectives of the research that follows in the subsequent chapters. Chapter 2 discusses the data, methodology and framework used in the construction of a simple statistical emulator of the response of crops to weather shocks simulated by crop models. To facilitate the integration of the emulator into IAMs, the simplest model using a base specification of linear fixed effect with time trend interactions is developed. Chapter 3 investigates modifications to the base specification with a series of robustness checks exploring the suitability of an additional predictor variable, the stratification of coefficients geographically by groups of Agro-Ecological Zones (AEZs); and most importantly, the role of spatial dependence in variables by applying a spatial model. Chapter 4 compares the performance of the statistical emulator calibrated on crop model results, with an empirical models of crop responses based on historical data. The comparison focuses on U.S. counties. The base specification from Chapter 2 together with historical observed data from the U.S. Department of Agriculture (USDA), are utilized in an inter-comparison exercise for divergence in results and subsequent implications. Collectively, the three chapters (2-4) address several important questions: (1) what do reduced-form statistical response surfaces trained on crop model outputs from various simulation specifications look like; (2) do model-based crop response functions vary systematically over space (e.g., crop suitability zones) and across crop models?, (3) how do model-based crop response functions compare to crop responses estimated using historical observations? and (4) what are the implications for the characterization of future climate risks? Chapter 5 concludes the thesis providing a summary of key contributions and suggestions for future work.
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Serra, Díaz Josep M. "Applying correlative ecological niche models to global change studies." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96302.
Full textAbstract:
La distribució de les espècies ha estat objecte d’estudi per part de diverses disciplines donada la seva naturalesa multifactorial. Així, entendre veritablement la distribució de les espècies implica necessàriament un millor coneixement del funcionament de la biosfera. D’altra banda, el canvi global que està patint el nostre planeta previsiblement afectarà en gran mesura moltes espècies, variant així la distribució i composició dels ecosistemes tal i com els coneixem avui dia i implícitament dels serveis que proporcionen.
La modelització ha permès augmentar el nostre grau de comprensió sobre el sistema Terra així com de les potencials conseqüències que els canvis antropogènics poden provocar (canvi climàtic, alteració de cicles biogeoquímics, destrucció d’hàbitats, etc.). En el camp de la distribució d’espècies, els models de nínxol ecològic han estat àmpliament utilitzats per estudiar i preveure canvis en la distribució dels organismes. Aquests models es basen en la determinació de les condicions ambientals òptimes on una determinada espècie pot viure i reproduir-se (nínxol).
Tanmateix, aquests models fan ús d’aproximacions correlatives entre presències i variables ambientals actuals, fet que presenta diverses desavantatges que posen de manifest una gran incertesa en les prediccions i fins i tot, qüestionen la seva utilitat en el context de canvi global. El conjunt dels treballs que s’exposen pretenen donar una visió sintètica de la possibilitat d’ús d’aquests models per a prediccions de distribució d’espècies vegetals, tant presents com futures.
La present recerca se centra en l’anàlisi de diversos aspectes problemàtics per a aquests models en la predicció de la distribució´ d’espècies vegetals en el context del canvi global. Específicament s’ha avaluat la diferència entre prediccions basades en models ecofisiològics i models correlatius sobre l’efecte de prediccions actuals i futures , la variació entre prediccions a nivell de taxó o a nivell de comunitat, la variació en la predicció de canvi de nínxol davant possibles invasions i finalment, l’addició de l’escala temporal en les prediccions.
S’ha pogut constatar que el fet de basar-se en correlacions estàtiques minva la seva capacitat de transferència a noves situacions i no incorpora trets biològics que poden tenir una importància cabdal (p.ex. fisiologia). En situacions de projeccions en l’espai i en el temps, s’observen importants variacions espacials en les prediccions, tant a nivell de comunitat com a nivell de poblacions de diversa provinença. Això comporta que les assumpcions i l’escala geogràfica i biològica hagin de ser adaptades segons la qüestió a la que el model s’adreça així com de la disponibilitat de dades. A més, incorporar l’escala temporal pot afegir una cert grau de dinamisme a aquests models estàtics, malgrat que no es poden inferir efectes a una resolució temporal adequada per a alguns fenòmens climàtics extrems .
Dels resultats se’n desprèn que la utilització d’aquests models pot servir com a una bona eina de generació d’hipòtesis sobre dels diferents factors que actualment constrenyen la distribució de les espècies. A més, pot ser una tècnica potent per estimar el grau d’exposició de les espècies davant noves situacions de canvi global. Tot i això, les seves prediccions han de ser confrontades amb d’altres tècniques oimés quan es tracta de valorar escenaris plausibles subjectes a una gran incertesa. En general, aquests models són molt significatius per a la caracterització de l’exposició a noves situacions.La distribución de las especies ha sido objetos de estudio por parte de diversas disciplinas dada su naturaleza multifactorial. Así, entender verdaderamente la distribución de las especies implica necesariamente un mejor conocimiento del funcionamiento de la biosfera. Por otro lado, el cambio global que esta sufriendo nuestro planeta previsiblemente afectará en gran medida muchas especies, variando su distribución y en última instancia, la composición de los ecosistemas tal y como los conocemos hoy día así como los servicios que proporcionan. La modelización ha permitido aumentar nuestro grado de comprensión sobre el sistema Tierra así como de las potenciales consecuencias que los cambios antropogénicos pueden provocar (cambio climático, alteración de ciclos biogeoquímicos, destrucción de hábitats, etc.). En el campo de la distribución de especies, los modelos de nicho ecológico han sido ampliamente utilizados para estudiar y predecir cambios en la distribución de los organismos. Estos modelos se basan en la determinación de las condiciones ambientales óptimas en las que una determinada especie puede vivir y reproducirse (nicho). Sin embargo, estos modelos utilizan una aproximación correlativa entre presencia de un organismo y las variables ambientales actuales, hecho que presenta diversas desventajas que ponen de manifiesto una gran incertidumbre en las predicciones e incluso, cuestionan su utilidad en el contexto del cambio global. El conjunto de los trabajos que aquí se exponen pretenden dar una visión sintética de la posibilidad de uso de estos modelos para predicciones de la distribución de especies vegetales, tanto presentes como futuras. La presente investigación se centra en el análisis de aspectos problemáticos de índole diversa de este tipo de modelos, cuando son aplicados para predecir la distribución de especies vegetales bajo supuestos de cambio global. Específicamente se ha evaluado la diferencia entre predicciones basadas en modelos ecofisiológicos y modelos correlativos en la predicción de distribuciones presentes y futuras, la variación entre predicciones a nivel de taxón o a niveles de comunidad, la variación en la predicción según la población bajo riesgos potenciales de cambio de nicho i finalmente, la adición de la escala temporal en las predicciones. Se ha podido constatar que el hecho de basarse en correlaciones estáticas disminuye su capacidad de transferencia a nuevas situaciones i no incorpora características biológicas que pueden tener una importancia vital (p.ej. fisiología). En situaciones de proyecciones en el espacio y el tiempo, se observan variaciones espaciales significativas en las predicciones, tanto a nivel de comunidad como a nivel de poblaciones de diverso origen. Esto comporta que las asunción i la correcta elección de la escala geográfica i biológica según el objetivo del modelo. Además, la incorporación de la escala temporal puede añadir un cierto grado de dinamismo a estos modelos estáticos, a pesar que no se pueden inferir efectos a una resolución temporal adecuada para algunos fenómenos climáticos extremos. En general, dichos modelos son relevantes para caracterizar la exposición a nuevas situaciones.
The distribution of species has been studied by various disciplines due to its multifactorial nature. Thus, to truly understand the distribution of species necessarily implies a better understanding of the functioning of the biosphere. On the other hand, the overall change our planet is undergoing, it is expected to greatly affect many species, varying distribution and ultimately the composition of ecosystems as we know them today and the services they provide. The modeling has enhanced our level of understanding of the Earth system and the potential consequences that anthropogenic changes can cause (climate change, alteration of biogeochemical cycles, habitat destruction, etc..). In the field of species distribution, ecological niche models have been widely used to study and predict changes in the distribution of organisms. These models are based on determining the optimum environmental conditions in which a species can live and reproduce (niche). However, these models use a correlative approach between the presence of an organism and the current environmental variables, which has several disadvantages that cause a uncertainty in predictions and even question their usefulness in the context of global change. All the works presented here are intended to give a synthetic view of the possibility of using these models for predictions of the distribution of plant species, both present and future. This research focuses on the analysis of problematic aspects of these models, when applied to predict the distribution of plant species under global change scenarios. Specifically we evaluated the difference between model predictions and ecophysiological models to predict correlative and future distributions, the variation between predictions at the level of taxon or community levels, the variation in the prediction at the population levels and finally, the addition of the timescale in the predictions. It has been shown that basing predictions on static correlations diminishes their transference capacity to new situations and does not incorporate key biological traits that may play a key role (e.g. physiology). In projections in space and time, it has been observed significant spatial variations in predictions, whether at the community or individual level of species or different populations across continents. This implies that the choice of the biological or geographical scale may be fit for model’s purpose. Furthermore, the incorporation of the temporal scale may add a certain degree of dynamism to these static modles, despite they cannot be infered for effects at higher temporal resolution for some extreme climatic events. In general, such models are relevant to characterize exposure to new situations.
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FILIPPI, LUCA. "The fluid dynamics of climate: General Circulation Models and applications to past, present and future climatic changes." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2644371.
Full textAbstract:
La presente tesi di ricerca riguarda l’ottimizzazione e l’utilizzo di modelli climatici globali, ed in modo particolare del modello climatico globale ad alta risoluzione EC-Earth, per affrontare una serie di problemi di interesse nel contesto della dinamica del clima e del cambiamento climatico. In particolare, l’attività di ricerca ha riguardato il lavoro di ottimizzazione, ovvero di tuning, del modello climatico globale EC-Earth, appartenente alla categoria degli Earth System Models, e nello specifico della componente atmosferica del modello. Lo studio del cosiddetto “Equable Climate” dell’Eocene, un periodo caldo verificatosi circa 50 milioni di anni fa caratterizzato da una bassa differenza di temperatura tra equatore e poli e ridotto ciclo annuale alle alte latitudini. Gli “Equable Climates” sono un problema tutt’ora irrisolto nelle scienze del clima e la loro comprensione potrebbe avere importanti implicazioni circa la nostra comprensione ed interpretazione dei cambiamenti climatici in corso. L'analisi delle caratteristiche della precipitazione invernale nella regione montuosa dell’Hindu-Kush Karakoram, nell’Himalaya occidentale, e delle sue teleconnessioni, con particolare riferimento all’Oscillazione Nord Atlantica. Lo studio é stato condotto mediante l’utilizzo congiunto di dati osservativi, rianalisi atmosferiche e simulazioni climatiche realizzate con il modello EC-Earth. Lo studio del cambiamento climatico nelle regioni montane, ed in particolare della dipendenza dalla quota dell’aumento delle temperature superficiali terrestri registrato durante il corso del XX secolo e previsto per le prossime decadi (Elevation-Dependent Warming, EDW). Lo studio si é focalizzato principalmente sulla regione montuosa dell’Himalaya- Tibetan Plateau ed é stato condotto mediante l’utilizzo di un ensemble di modelli climatici globali che hanno partecipato al Coupled Model Intercomparison Project Phase 5 (CMIP5) e all’analisi dei dati osservativi disponibili.
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Huybers, Peter 1974. "On the origins of the ice ages : insolation forcing, age models, and nonlinear climate change." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/88360.
Full textAbstract:
Thesis (Sc. D. in Climate Physics and Chemistry)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2004.Includes bibliographical references (p. 229-245).
This thesis revolves about the relationship between orbital forcing and climate variability. To place paleo and modern climate variability in context, the spectrum of temperature variability is estimated from time-scales of months to hundreds of thou- sands of years using a patchwork of proxy and instrumental records. There is an energetic background continuum and rich spatial structure associated with temperature variability which both scale according to simple spectral power-laws. To complement the spatial and temporal analysis of temperature variability, a description of the full insolation forcing is also developed using Legendre polynomials to represent the spatial modes of variability and singular vectors to represent seasonal and long-term changes. The leading four spatial and temporal modes describe over 99% of the insolation variability making this a relatively simple and compact description of the full insolation forcing. Particular attention is paid to the insolation variations resulting from the precession of the equinoxes. There is no mean annual insolation variability associated with precession - precession only modulates the seasonal cycle. Nonlinear rectification of the seasonal cycle generates precession-period variability, and such rectification naturally occurs in the climate system but also results from the seasonality inherent to many climate proxies. One must distinguish this latter instrumental effect from true climate responses. Another potential source of spurious low-frequency variability results from the stretching and squeezing of an age-model so that noise in a record is made to align with an orbital signal.
(cont.) Furthermore, and contrary to assertions made elsewhere, such orbital-tuning can also generate an eccentricity-like amplitude modulation in records that have been narrow-band-pass filtered over the precession bands. An accurate age-model is the linchpin required to connect insolation forcing with any resulting climatic responses, and to avoid circular reasoning, this age-model should make no orbital assumptions. A new chronology of glaciation, spanning the last 780 kilo-years, is estimated from 21 marine sediment cores using a compaction corrected depth scale as a proxy for time. Age-model uncertainty estimates are made using a stochastic model of marine sediment accumulation. The depth-derived ages are estimated to be accurate to within L9, 000 years, and within this uncertainty are consistent with the orbitally-tuned age estimates. Nonetheless, the remaining differences between the depth and orbitally derived chronologies produce important differences in the spectral domain. From the 6180 record, using the depth-derived ages, evidence is found for a nonlinear coupling involving the 100KY and obliquity frequency bands which generates interaction bands at sum and difference frequencies. If an orbitally-tuned age-model is instead applied, these interactions are suppressed, with the system appearing more nearly linear. A generalized phase synchronization analysis is used to further assess the nonlinear coupling between obliquity and the glacial cycles. Using a formal hypothesis testing procedure, it is shown that glacial terminations are associated with high obliquity states at the 95% significance level. The association of terminations with eccentricity or precession is indistinguishable from chance.
(cont.) A simple excitable system is introduced to explore potential mechanisms by which obliquity paces the glacial cycles. After tuning a small number of adjustable parameters, the excitable model repro- duces the correct timing for each termination as well as the linear and nonlinear features earlier identified in the 6180 record. Under a wide range of conditions the model exhibits a chaotic amplitude response to insolation forcing. One chaotic mode gives a train of small and nearly equal amplitude 40KY cycles. Another mode permits ice to accumulate over two (80KY) or three obliquity cycles (120KY) prior to rapidly ablating and thus, on average, generates 100KY variability. The model spontaneously switches between these 40 and 100KY chaotic modes, suggesting that the Mid-Pleistocene Transition may be independent of any major shifts in the background state of the climate system.
by Peter Huybers.
Sc.D.in Climate Physics and Chemistry
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Ranatunga, Channa. "Introducing an effect of climate change into global models of rain fade on telecommunications links." Thesis, University of Hull, 2014. http://hydra.hull.ac.uk/resources/hull:11321.
Full textAbstract:
Rain attenuation limits the performance of microwave telecommunication links functioning above approximately 5 GHz. Recent studies have revealed that over the last twenty years the occurrence of rain, at intensities that cause outage on terrestrial links, has experienced a strongly increasing trend in the UK. Globally, the height of rain events has also been observed to increase, which may compound increasing trends in rain fade experienced by Earth-Space communication systems. These climatic changes are almost certainly having significant effect on the performance of existing radio systems, and need to be taken into consideration when planning future systems. The International Telecommunication Union – Radio Section (ITU-R), maintains a set of internationally accepted models for the engineering and regulation of radio systems globally. Although under constant revision, these models assume that atmospheric fading is stationary. This assumption is inherent in the way models are tested. In this project, a method is developed to estimate global trends in one of the most fundamental parameters to the ITU-R models: the one-minute rain rate exceeded for 0.01% of an average year. This method introduces climate change into the ITU-R model of this parameter: Rec. ITU-R P.837. The new model is tested using a method that does not make a stationary climate assumption. Salonen-Poiares Baptista distribution, which is the fundamental method for developing ITU-R Rec. P.837 has been tested using UK Environment Agency data, but no correlations was found between measured annual accumulations and distribution parameters. Nonetheless a link was found between mean annual total precipitations (MT) and rain exceeded at larger time percentages such as; 0.1% and 1%.
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Daniels, Benjamin. "Effects of Climate Nonstationarity on Low-Flow Models for Southern New England." Thesis, Boston College, 2014. http://hdl.handle.net/2345/bc-ir:103565.
Full textAbstract:
Thesis advisor: Noah SnyderIncreasing attention has been drawn to the need for reliable streamflow estimates at ungaged locations under a range of climatic and hydrologic conditions. Climate projections for the northeastern United States over the 21st century--which include significant increases in temperature and precipitation--could have broad impacts on streamflows, potentially reducing the accuracies of existing streamflow models for the region. This thesis investigates recent changes in daily flow-durations in southern New England, and examines their influence on the reliability of the low-flow models for Massachusetts presented by Ries and Friesz (2000). An analysis of discharge data collected at gaging sites through water year 2012 revealed increases in nearly all flow durations at sites across southern New England since the mid-20th century, whereas very low flows (quantiles at or above the 95-percent exceedance probability) generally showed decreases, especially since the 1990s. Twenty-year moving streamflow quantiles at each of ten selected exceedance probabilities were examined for the periods of record of 16 streamflow-gaging stations in southern New England. The beginning of water year 1992 appeared to mark an inflection point in low-flow quantiles, before which very low flows were steady or increasing, and after which these flows showed near-universal decreases. While the observed peak in 20-year low-flow quantiles around 1992 may be due to the statistical method used to calculate the quantile trends, the inflection point could also be an indicator of when increasing evapotranspiration surpassed increasing precipitation as the principal climatic driver of changes in low flows in southern New England. The general upward translation of the flow-duration curve observed over the last 60 years is very likely linked to increases in annual precipitation during this period, while the decreases in very low flows are likely due to changes in climatic variables (increasing summer temperatures and evapotranspiration rates), and amplified by anthropogenic factors (greater areas of impervious surfaces and increasing rates of surface- and ground-water withdrawal). The data suggest that increasing precipitation rates have already caused the Ries and Friesz (2000) equations for the median low flows (Q50 to Q75) to become biased towards underestimation, and decreases in very low flows threaten to render the models for these flows biased towards overestimation in the coming decades. The streamflow quantile trends (for both the entire period of record of the gaging stations and just the post-1992 period) for each of the ten flow-durations of interest were extended into the future to the point where the corresponding Ries and Friesz (2000) model would fail (when actual flow durations would be outside the 90-percent prediction intervals for the estimated flows for greater than 10% of sites). The models for the lowest streamflows are estimated to lose validity by as early as 2018. Climate change is predicted to have significant effects on streamflow characteristics in southern New England over the 21st century, and the results of this study indicate that the Ries and Freisz (2000) low-flow models should be reformulated using more recent streamflow data within the next decade, and validated every 20 years thereafter to ensure their accuracies are maintained despite the effects of regional nonstationarity
Thesis (MS) — Boston College, 2014
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Earth and Environmental Sciences
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Meque, Arlindo Oliva. "Investigating the link between southern African droughts and global atmospheric teleconnections using regional climate models." Doctoral thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/16686.
Full textAbstract:
Includes bibliographical referencesDrought is one of the natural hazards that threaten the economy of many nations, especially in Southern Africa, where many socio-economic activities depend on rain-fed agriculture. This study evaluates the capability of Regional Climate Models (RCMs) in simulating the Southern African droughts. It uses the Standardized Precipitation-Evapotranspiration Index (SPEI, computed using rainfall and temperature data) to identify 3-month droughts over Southern Africa, and compares the observed and simulated drought patterns. The observation data are from the Climate Research Unit (CRU), while the simulation data are from 10 RCMs (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF, and CRCM) that participated in the Regional Climate Downscaling Experiment (CORDEX) project. The study also categorizes drought patterns over Southern Africa, examines the persistence and transition of these patterns, and investigates the roles of atmospheric teleconnections on the drought patterns. The results show that the drought patterns can occur in any season, but they have preference for seasons. Some droughts patterns may persist up to three seasons, while others are transient. Only about 20% of the droughts patterns are induced solely by El Niño Southern Oscillation (ENSO), other drought patterns are caused by complex interactions among the atmospheric teleconnections. The study also reveals that the Southern Africa drought pattern is generally shifting from a wet condition to a dry condition, and that the shifting can only be captured with a drought monitoring index that accounts for temperature influence on drought. Only few CORDEX RCMs simulate the Southern African droughts as observed. In this regard, the ARPEGE model shows the best simulation. The best performance may be because the stretching capability of ARPEGE helps the model to eliminate boundary condition problems, which are present in other RCMs. In ARPEGE simulations, the stretching capability would allow a better interaction between large and small scale features, and may lead to a better representation of the rain producing systems in Southern Africa. The results of the study may be applied to improve monitoring and prediction of regionally-extensive drought over Southern Africa, and to reduce the socio-economic impacts of drought in the region.
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Sery, Roy Aharon. "Strategic response of private healthcare funders in South Africa to global climate change." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/26045.
Full textAbstract:
Climate change is an environmental issue that has actual or potential strategic impacts on many companies. The research problem emanates from the scientific work on climate change and the vast health effects that would pose as implications within the healthcare industry. The aim of the research was to explore the strategic response of private healthcare funders in South Africa to global climate change. By means of a case-study based research design, the stimuli for strategic response, risks and opportunities related to global climate change and strategy and an overall strategic organisational posture under the RDAP continuum scale framework had evolved. Evidence from the results and analysis brings light to the fact that global climate change as a strategic concern to private healthcare funders remains a point of scepticism. Although some of the organisations from the sample have considered climate change as a strategic concern, there are others that do not. The study showed that global climate change continues to remain an issue of complexity and uncertainty in the external business environment such that strategy formulation and implementation and acting proactively on the matter remains complicated. CopyrightDissertation (MBA)--University of Pretoria, 2010.
Gordon Institute of Business Science (GIBS)
unrestricted
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Irby, Isaac. "Using Water Quality Models in Management - A Multiple Model Assessment, Analysis of Confidence, and Evaluation of Climate Change Impacts." W&M ScholarWorks, 2017. https://scholarworks.wm.edu/etd/1516639464.
Full textAbstract:
Human impacts on the Chesapeake Bay through increased nutrient run-off as a result of land-use change, urbanization, and industrialization, have resulted in a degradation of water quality over the last half-century. These direct impacts, compounded with human-induced climate changes such as warming, rising sea-level, and changes in precipitation, have elevated the conversation surrounding the future of water quality in the Bay. The overall goal of this dissertation project is to use a combination of models and data to better understand and quantify the impact of changes in nutrient loads and climate on water quality in the Chesapeake Bay. This research achieves that goal in three parts.
First, a set of eight water quality models is used to establish a model mean and assess model skill. All models were found to exhibit similar skill in resolving dissolved oxygen concentrations as well as a number of dissolved oxygen-influencing variables (temperature, salinity, stratification, chlorophyll and nitrate) and the model mean exhibited the highest individual skill. The location of stratification within the water column was found to be a limiting factor in the models’ ability to adequately simulate habitat compression resulting from low-oxygen conditions.
Second, two of the previous models underwent the regulatory Chesapeake Bay pollution diet mandated by the Environmental Protection Agency. Both models exhibited a similar relative improvement in dissolved oxygen concentrations as a result of the reduction of nutrients stipulated in the pollution diet. A Confidence Index was developed to identify the locations of the Bay where the models are in agreement and disagreement regarding the impacts of the pollution diet. The models were least certain in the deep part of the upper main stem of the Bay and the uncertainty primarily stemmed from the post-processing methodology.
Finally, by projecting the impacts of climate change in 2050 on the Bay, the potential success of the pollution diet in light of future projections for air temperature, sea level, and precipitation was examined. While a changing climate will reduce the ability of the nutrient reduction to improve oxygen concentrations, that effect is trumped by the improvements in dissolved oxygen stemming from the pollution diet itself. However, climate change still has the potential to cause the current level of nutrient reduction to be inadequate. This is primarily due to the fact that low-oxygen conditions are predicted to start one week earlier, on average, in the future, with the primary changes resulting from the increase in temperature.
Overall, this research lends an increased degree of confidence in the water quality modeling of the potential impact of the Chesapeake Bay pollution diet. This research also establishes the efficacy of utilizing a multiple model approach to examining projected changes in water quality while establishing that the pollution diet trumps the impact from climate change. This work will lead directly to advances in scientific understanding of the response of water quality, ecosystem health, and ecological resilience to the impacts of nutrient reduction and climate change.
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Dars, Ghulam Hussain. "Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/979.
Full textAbstract:
Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario.
We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons.
We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period.
The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure.