Relevant bibliographies by topics / 190501 Climate change models

Academic literature on the topic '190501 Climate change models'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "190501 Climate change models"

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Pitman, A. J., and R. J. Stouffer. "Abrupt change in climate and climate models." Hydrology and Earth System Sciences Discussions 3, no. 4 (July 19, 2006): 1745–71. http://dx.doi.org/10.5194/hessd-3-1745-2006.

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Abstract. First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulate seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse of the thermohaline circulation in the 21st century is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.
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Pitman, A. J., and R. J. Stouffer. "Abrupt change in climate and climate models." Hydrology and Earth System Sciences 10, no. 6 (November 28, 2006): 903–12. http://dx.doi.org/10.5194/hess-10-903-2006.

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Abstract. First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulation seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse in the 21st century of the thermohaline circulation is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.
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Dooge, J. C. I. "Hydrologic models and climate change." Journal of Geophysical Research 97, no. D3 (1992): 2677. http://dx.doi.org/10.1029/91jd02156.

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Anonymous. "Numerical models of climate change." Eos, Transactions American Geophysical Union 69, no. 45 (1988): 1556. http://dx.doi.org/10.1029/88eo01181.

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Schär, Christoph, Christoph Frei, Daniel Lüthi, and Huw C. Davies. "Surrogate climate-change scenarios for regional climate models." Geophysical Research Letters 23, no. 6 (March 15, 1996): 669–72. http://dx.doi.org/10.1029/96gl00265.

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Ewert, F., J. R. Porter, M. D. A. Rounsevell;, S. P. Long, E. A. Ainsworth, A. D. B. Leakey, D. R. Ort, J. Nosberger, and D. Schimel. "Crop Models, CO2, and Climate Change." Science 315, no. 5811 (January 26, 2007): 459c—460c. http://dx.doi.org/10.1126/science.315.5811.459c.

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Dowlatabadi, Hadi. "Integrated assessment models of climate change." Energy Policy 23, no. 4-5 (April 1995): 289–96. http://dx.doi.org/10.1016/0301-4215(95)90155-z.

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Herrando-Pérez, Salvador. "Climate change heats matrix population models." Journal of Animal Ecology 82, no. 6 (October 24, 2013): 1117–19. http://dx.doi.org/10.1111/1365-2656.12146.

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Araujo, Miguel B., Richard G. Pearson, Wilfried Thuiller, and Markus Erhard. "Validation of species-climate impact models under climate change." Global Change Biology 11, no. 9 (September 2005): 1504–13. http://dx.doi.org/10.1111/j.1365-2486.2005.01000.x.

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Bush, Drew, Renee Sieber, Mark A. Chandler, and Linda E. Sohl. "Teaching anthropogenic global climate change (AGCC) using climate models." Journal of Geography in Higher Education 43, no. 4 (September 9, 2019): 527–43. http://dx.doi.org/10.1080/03098265.2019.1661370.

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Dissertations / Theses on the topic "190501 Climate change models"

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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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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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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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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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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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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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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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Books on the topic "190501 Climate change models"

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DeCanio, Stephen J. Economic Models of Climate Change. London: Palgrave Macmillan UK, 2003. http://dx.doi.org/10.1057/9780230509467.

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Ward, George H. Hydrological predictands for climate-change modeling. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Denver Office, 1996.

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Ward, George H. Hydrological predictands for climate-change modeling. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Denver Office, 1996.

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Wang, Zheng, Jing Wu, Changxin Liu, and Gaoxiang Gu. Integrated Assessment Models of Climate Change Economics. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3945-4.

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National Research Council (U.S.). Climate Research Committee. Capacity of U.S. climate modeling to support climate change assessment activities. Washington, D.C: National Academy Press, 1998.

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Environmental and Water Resources Institute (U.S.), ed. Climate change modeling, mitigation, and adaptation. Reston, Virginia: American Society of Civil Engineers, 2013.

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Parson, Edward. Climate treaties and models: Issues in the international management of climate change. Washington, DC: Office of Technology Assessment, 1994.

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Parson, Edward. Climate treaties and models: Issues in the international management of climate change. Washington, DC: The Office, 1994.

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Hodkinson, Trevor R. Climate change, ecology, and systematics. Cambridge: Cambridge University Press, 2011.

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Hodkinson, Trevor R. Climate change, ecology, and systematics. Cambridge: Cambridge University Press, 2011.

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Book chapters on the topic "190501 Climate change models"

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Majumder, Mrinmoy. "Climate Change and Climate Models." In Impact of Urbanization on Water Shortage in Face of Climatic Aberrations, 55–66. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-4560-73-3_4.

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Leung, L. Ruby. "Regional Climate Models." In Climate Change Modeling Methodology, 211–33. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5767-1_9.

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Dethloff, K., A. Rinke, A. Lynch, W. Dorn, S. Saha, and D. Handorf. "Arctic Regional Climate Models." In Arctic Climate Change, 325–56. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2027-5_8.

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Weisse, Ralf, and Hans von Storch. "Models for the marine environment." In Marine Climate and Climate Change, 77–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68491-6_3.

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Gottinger, Hans W. "Global Climate Change Models." In Encyclopedia of Operations Research and Management Science, 645–49. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_388.

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Majumder, Mrinmoy, and Apu K. Saha. "Climate Change and Models." In Impact of Climate Change on Hydro-Energy Potential, 9–11. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-305-7_3.

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Bacmeister, Julio T. "Weather Prediction Models." In Climate Change Modeling Methodology, 89–114. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5767-1_5.

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Sanderson, Ben, and Reto Knutti. "Climate Change climate change Projections climate change projections : Characterizing Uncertainty Using Climate Models." In Encyclopedia of Sustainability Science and Technology, 2097–114. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_369.

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Wibig, Joanna, Douglas Maraun, Rasmus Benestad, Erik Kjellström, Philip Lorenz, and Ole Bøssing Christensen. "Projected Change—Models and Methodology." In Regional Climate Studies, 189–215. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16006-1_10.

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Yoon, Jin-Ho, and Po-Lun Ma. "Oceanic General Circulation Models." In Climate Change Modeling Methodology, 63–87. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5767-1_4.

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Conference papers on the topic "190501 Climate change models"

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WASHINGTON, WARREN M. "THE STATUS OF CLIMATE MODELS AND CLIMATE CHANGE SIMULATIONS." In International Seminar on Nuclear War and Planetary Emergencies 25th Session. Singapore: World Scientific Publishing Co. Pte. Ltd., 2001. http://dx.doi.org/10.1142/9789812797001_0039.

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Herath, H. M. R. C., and I. M. S. P. Jayawardena. "EVALUATION OF DOWNSCALED CMIP5 CLIMATE MODELS TO SELECT THE BEST MODELS TO DEVELOP FUTURE CLIMATE SCENARIOS FOR SRI LANKA." In The International Conference on Climate Change. The International Institute of Knowledge Management (TIIKM), 2018. http://dx.doi.org/10.17501/iccc.2017.1204.

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Darshika, Thanuja. "Future Climate Projections for Annual and Seasonal Rainfall in Sri Lanka using CMIP5 Models." In International Conference on Climate Change. The International Institute of Knowledge Management (TIIKM), 2017. http://dx.doi.org/10.17501/iccc.2017.1108.

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Steinschneider, S., C. Brown, R. N. Palmer, and D. Ahlfeld. "Hydrology Models for Climate Change Assessment: Inter-Decadal Climate Variability and Parameter Calibration." In World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)428.

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Ejeta, Messele, Francis Chung, Sushil Arora, and Armin Munévar. "Incorporating Climate Change into Hydrological Data for Planning Models." In World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)521.

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Chaudhary, Junaid Rafi, Husain, and Tahir. "Uncertainty Analysis of Humidity and Precipitation Changes using Data from Global Climatic Models with a Case Study." In 2006 IEEE EIC Climate Change Conference. IEEE, 2006. http://dx.doi.org/10.1109/eicccc.2006.277180.

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"Linking regional climate simulations and hydrologic models for climate-change impact studies: a data processing framework." In ASABE 1st Climate Change Symposium: Adaptation and Mitigation. American Society of Agricultural and Biological Engineers, 2015. http://dx.doi.org/10.13031/cc.20152123495.

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"Reconciling surface and groundwater models in a climate change context." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l5.woods.

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Bean, Jessica R., Kathleen Zoehfeld, Kristen Mitchell, Aleeza Oshry, Anthony Joseph Menicucci, Trish Roque, Lisa D. White, and Charles R. Marshall. "UNDERSTANDING GLOBAL CHANGE: FRAMEWORKS AND SYSTEM MODELS FOR TEACHING, LEARNING, AND COMMUNICATING CLIMATE CHANGE." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-305585.

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Stone, Peter H. "Forecast cloudy: The limits of global warming models." In The world at risk: Natural hazards and climate change. AIP, 1992. http://dx.doi.org/10.1063/1.43901.

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Reports on the topic "190501 Climate change models"

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Goody, R., and M. Gerstell. Physical basis for climate change models. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/10107441.

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Pindyck, Robert. Climate Change Policy: What Do the Models Tell Us? Cambridge, MA: National Bureau of Economic Research, July 2013. http://dx.doi.org/10.3386/w19244.

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Hofmockel, Kirsten, and Erik Hobbie. Can Microbial Ecology and Mycorrhizal Functioning Inform Climate Change Models? Office of Scientific and Technical Information (OSTI), July 2017. http://dx.doi.org/10.2172/1427520.

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Constantine, Paul, Richard V. ,. Jr Field, and Mark Bruce Elrick Boslough. Statistical surrogate models for prediction of high-consequence climate change. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1029816.

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Ríos Flores, Ramiro Alberto, Alejandro Pablo Taddia, Alfred Grunwaldt, Russel Jones, and Richard Streeter. Climate Change Projections in Latin America and the Caribbean: Review of Existing Regional Climate Models' Outputs. Inter-American Development Bank, July 2016. http://dx.doi.org/10.18235/0000375.

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Wang, S. Assessment of climate change impact on ecosystem through developing advanced ecosystem models. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2012. http://dx.doi.org/10.4095/290190.

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Melillo, J. M., Terese (T C. ). Richmond, and G. W. Yohe, eds. Appendix 5: Scenarios and Models. Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 2014. http://dx.doi.org/10.7930/j0b85625.

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Kandlikar, Milind. Reconciling uncertainties in integrated science and policy models: Applications to global climate change. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/464182.

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Burtis, M. D., V. N. Razuvaev, and S. G. Sivachok. Selected translated abstracts of Russian-language climate-change publications. 4: General circulation models. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/676892.

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Geng, S., R. Plant, and R. Loomis. Analysis and synthesis of models for effects of climate change on agricultural systems. Office of Scientific and Technical Information (OSTI), July 1992. http://dx.doi.org/10.2172/6787392.

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