Uncertainty about the sensitivity of the climate system to changes in the Earth’s radiative balance constitutes a primary source of uncertainty for climate projections. Given the continuous increase in atmospheric greenhouse gas concentrations, constraining the uncertainty range in such type of sensitivity is of vital importance. A common measure for expressing this key characteristic for climate models is the climate sensitivity, defined as the simulated change in global-mean equilibrium temperature resulting from a doubling of atmospheric CO2 concentration. The broad range of climate sensitivity estimates (1.5-4.5°C as given in the last Assessment Report of the Intergovernmental Panel on Climate Change, 2001), inferred from comprehensive climate models, illustrates that the strength of simulated feedback mechanisms varies strongly among different models. The central goal of this thesis is to constrain uncertainty in climate sensitivity. For this objective we first generate a large ensemble of model simulations, covering different feedback strengths, and then request their consistency with present-day observational data and proxy-data from the Last Glacial Maximum (LGM). Our analyses are based on an ensemble of fully-coupled simulations, that were realized with a climate model of intermediate complexity (CLIMBER-2). These model versions cover a broad range of different climate sensitivities, ranging from 1.3 to 5.5°C, and have been generated by simultaneously perturbing a set of 11 model parameters. The analysis of the simulated model feedbacks reveals that the spread in climate sensitivity results from different realizations of the feedback strengths in water vapour, clouds, lapse rate and albedo. The calculated spread in the sum of all feedbacks spans almost the entire plausible range inferred from a sampling of more complex models. We show that the requirement for consistency between simulated pre-industrial climate and a set of seven global-mean data constraints represents a comparatively weak test for model sensitivity (the data constrain climate sensitivity to 1.3-4.9°C). Analyses of the simulated latitudinal profile and of the seasonal cycle suggest that additional present-day data constraints, based on these characteristics, do not further constrain uncertainty in climate sensitivity. The novel approach presented in this thesis consists in systematically combining a large set of LGM simulations with data information from reconstructed regional glacial cooling. Irrespective of uncertainties in model parameters and feedback strengths, the set of our model versions reveals a close link between the simulated warming due to a doubling of CO2, and the cooling obtained for the LGM. Based on this close relationship between past and future temperature evolution, we define a method (based on linear regression) that allows us to estimate robust 5-95% quantiles for climate sensitivity. We thus constrain the range of climate sensitivity to 1.3-3.5°C using proxy-data from the LGM at low and high latitudes. Uncertainties in glacial radiative forcing enlarge this estimate to 1.2-4.3°C, whereas the assumption of large structural uncertainties may increase the upper limit by an additional degree. Using proxy-based data constraints for tropical and Antarctic cooling we show that very different absolute temperature changes in high and low latitudes all yield very similar estimates of climate sensitivity. On the whole, this thesis highlights that LGM proxy-data information can offer an effective means of constraining the uncertainty range in climate sensitivity and thus underlines the potential of paleo-climatic data to reduce uncertainty in future climate projections. / Eine der entscheidenden Hauptquellen für Unsicherheiten von Klimaprojektionen ist, wie sensitiv das Klimasystem auf Änderungen der Strahlungsbilanz der Erde reagiert. Angesichts des kontinuierlichen Anstiegs der atmosphärischen Treibhausgaskonzentrationen ist die Einschränkung des Unsicherheitsbereichs dieser Sensitivität von entscheidender Bedeutung. Ein häufig verwendetes Maß zur Beschreibung dieser charakteristischen Kenngröße von Klimamodellen ist die sogenannte Klimasensitivität, definiert als die Gleichgewichtsänderung der simulierten globalen Mitteltemperatur, welche sich aus einer Verdoppelung des atmosphärischen CO2-Gehalts ergibt. Die breite Spanne der geschätzten Klimasensitivität (1.5-4.5°C), welche ein Vergleich verschiedener komplexer Klimamodelle nahe legt (IPCC, 2001), verdeutlicht, wie groß die Unsicherheit in der Klimasensitivität ist. Diese Unsicherheit resultiert in erster Linie aus Unterschieden in der Simulation der entscheidenden Rückkopplungs-mechanismen in den verschiedenen Modellen. Das zentrale Ziel dieser Dissertation ist die Einschränkung des breiten Unsicherheitsbereichs der Klimasensitivität. Zunächst wird hierzu ein großes Ensemble an Modellsimulationen erzeugt, in welchem gezielt spezifische Modellparameter variiert, und somit unterschiedliche Rückkopplungsstärken der einzelnen Modellversionen realisiert werden. Diese Simulationen werden dann auf ihre Konsistenz mit sowohl heutigen Beobachtungsdaten, als auch Proxy-Daten des Letzten Glazialen Maximums (LGM) überprüft. Unsere Analysen basieren dabei auf einem Ensemble voll gekoppelter Modellläufe, welche mit einem Klimamodell intermediärer Komplexität (CLIMBER-2) realisiert wurden. Die betrachteten Modellversionen decken eine breite Spanne verschiedener Klimasensitivitäten (1.3-5.5°C) ab und wurden durch gleichzeitiges Variieren von 11 Modellparametern erzeugt. Die Analyse der simulierten Rückkopplungs-mechanismen offenbart, dass unterschiedliche Werte der Klimasensitivität in unserem Modellensemble durch verschiedene Realisierungen der Rückkopplungsstärken von Wasserdampf, Wolken, Temperatur-Vertikalprofil und Albedo zu erklären sind. Die berechneten Gesamt-Rückkopplungsstärken unser Modellversionen decken hierbei fast den gesamten möglichen Bereich von komplexeren Modellen ab. Wir zeigen, dass sich die Forderung nach Konsistenz zwischen simuliertem vorindustriellem Klima und Messdaten, die auf einer Wahl von sieben global gemittelten Datensätzen basieren, als vergleichsweise schwacher Test der Modellsensitivität erweist: Die Daten schränken den plausiblen Bereich der Klimasensitivität lediglich auf 1.3-4.9°C ein. Zieht man neben den genannten global gemittelten Messdaten außerdem klimatische Informationen aus Jahreszeit und geografischer Breite hinzu, lässt sich die Unsicherheit in der Klimasensitivität nicht weiter einschränken. Der neue Ansatz dieser Dissertation besteht darin, in systematischer Weise einen großen Satz an LGM-Simulationen mit Dateninformationen über die rekonstruierte glaziale Abkühlung bestimmter Regionen zu kombinieren. Unabhängig von den Unsicherheiten in Modellparametern und Rückkopplungsstärken offenbaren unsere Modellversionen eine ausgeprägte Beziehung zwischen der simulierten Erwärmung aufgrund der CO2-Verdoppelung und der Abkühlung im LGM. Basierend auf dieser engen Beziehung zwischen vergangener und zukünftiger Temperaturentwicklung definieren wir eine Methode (basierend auf linearer Regression), welche es uns erlaubt, robuste 5-95%-Quantile der Klimasensitivität abzuschätzen. Indem wir Proxy-Daten des LGM von niederen und hohen Breiten heranziehen, können wir die Unsicherheitsspanne der Klimasensitivität auf 1.3-3.5°C beschränken. Unsicherheiten im glazialen Strahlungsantrieb vergrößern diese Abschätzung auf 1.2-4.3°C, wobei die Annahme von großen strukturellen Unsicherheiten die obere Grenze um ein weiteres Grad erhöhen kann. Indem wir Proxy-Daten über tropische und antarktische Abkühlung betrachten, können wir zeigen, dass sehr unterschiedliche absolute Temperatur-Änderungen in hohen und niederen Breiten zu sehr ähnlichen Abschätzungen der Klimasensitivität führen. Vor dem Hintergrund unserer Ergebnisse zeigt diese Dissertation, dass LGM-Proxy-Daten ein effektives Mittel zur Einschränkung des Unsicherheitsbereichs der Klimasensitivität sein können und betont somit das Potenzial von Paläoklimadaten, den großen Unsicherheitsbereich von Klimaprojektionen zu reduzieren.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T16:55:56Z No. of bitstreams: 1 Evert Elvis Batista Almeida.pdf: 8214568 bytes, checksum: 34db767d9a38f53b7b60aaf92ca37a20 (MD5) / Made available in DSpace on 2016-07-05T16:55:56Z (GMT). No. of bitstreams: 1 Evert Elvis Batista Almeida.pdf: 8214568 bytes, checksum: 34db767d9a38f53b7b60aaf92ca37a20 (MD5) Previous issue date: 2009-02-26 / We applied a non-supervisioned data clustering technique based on a map of the problem into an inhomogeneous granular magnet problem. The physical behavior of the magnet is studied through the usual Monte Carlo method. Each data item is described by a set of numerical attributes, interpreted as points in a multiple-dimensional Euclidian space. The mapping consists in associating a Potts spin to each data point. The physical system is described by a disordered Potts Hamiltonian with several states with an exponentially decaying interaction among spins. The magnet reaches a superparamagnetic state at high temperatures in which the spins in certain grains are strongly correlated whereas the grains are loosely linked. In this way, each grain corresponds to a group or cluster. We implemented the method in a microcanonical ensemble where the conserved total energy is the control parameter. The temperature is calculated during the simulation and, besides thermodynamic stable states, it is possible to sample unstable and metastable state as well. We work with three artificial multiple-dimensional data set and a four-dimensional real data set. We obtained good results in all cases and discuss some issues concerning the microcanonical implementation of the superparamagnetic data clustering. / Aplicamos um método não supervisionado de agrupamento de dados para identificar padrões em vários conjuntos dados. A técnica baseia-se em um mapeamento do problema em um sistema magnético granular heterogêneo, cujo comportamento é investigado através de métodos Monte Carlo comumente empregado no campo da física estatística. Cada objeto é descrito por um conjunto de atributos de valores numéricos, interpretados como um ponto em um espaço euclidiano de dimensão apropriada. O mapeamento consiste em associar a cada item do conjunto, um ponto no espaço, um spin de Potts. O sistema físico é descrito por um hamiltoniano de Potts de muitos estados, no qual a interação entre os spins decai exponencialmente com a distância entre eles. Itens semelhantes, próximos, interagem fortemente enquanto que aqueles mais distantes entre si interagem apenas fracamente. O magneto atinge um estado superparamagnético para temperaturas suficientemente altas, no qual os spins de alguns grãos permanecem fortemente correlacionados, porém, os grãos estão fracamente ligados entre si. Cada grão corresponde a um grupo. Implementamos o método no ensemble microcanônico, no qual a energia total é conservada e constitui o parâmetro de controle. Nesse caso, a temperatura é calculada ao longo do processo e podemos acessar estados termodinamicamente estáveis, metaestáveis, bem como, instáveis. Trabalhamos com três conjuntos artificiais de dados, em duas e três dimensões, e um conjunto de dados reais com quatro dimensões. O desempenho do método foi satisfatório em todos os casos investigados.

En dépit de progrès croissants durant la dernière décennie, la complexité des écosystèmes marins est encore imparfaitement simulée par les modèles.Les formulations des processus biogéochimiques sont en général établies de manière empirique et contraintes par une multitude de paramètres.Il est ainsi généralement admis que leurs incertitudes impactent fortement l'estimation de la production primaire, dont le rôle dans le cycle du carbone est primordial.Analyser les impacts de l'incertitude des modèles est donc nécessaire pour améliorer la représentation des caractéristiques biogéochimiques de l'océan.Dans le contexte d'assimilation de données de la couleur de l'océan, la définition des erreurs de prévision représente de plus un important verrou aux performances des systèmes.Ces points seront analysés dans cette thèse. L'objectif sera d'examiner, dans un contexte de modélisation/assimilation, la pertinence d'utiliser une approche probabiliste basée sur une simulation explicite des incertitudes biogéochimiques du modèle couplé au 1/4° NEMO/PISCES sur l'océan Atlantique Nord.A partir d'une simulation déterministe du modèle PISCES, nous proposerons une méthode pour générer des processus aléatoires, AR(1), permettant d'inclure des structures spatiales et temporelles de corrélations.A chaque pas de temps, ces perturbations aléatoires seront ensuite introduites dans le modèle par l'intermédiaire de paramétrisations stochastiques.Elles simuleront 2 différentes classes d'incertitudes: les incertitudes sur les paramètres biogéochimiques du modèle et les incertitudes dues aux échelles non résolues dans le cas d'équations non linéaires. L'utilisation de paramétrisations stochastiques permettra ainsi d'élaborer une version probabiliste du modèle PISCES, à partir de laquelle nous pourrons réaliser une simulation d'ensemble de 60 membres.La pertinence de cette simulation d'ensemble sera évaluée par comparaison avec les observations de la couleur de l'océan SeaWIFS. Nous montrerons en particulier que la simulation d'ensemble conserve les structures de grande échelle présentes dans la simulation déterministe.En utilisant les distributions de probabilité définies par les membres de l'ensemble, nous montrerons que l'ensemble capture l'information des observations avec une bonne estimation de leurs statistiques d'erreur (fiabilité statistique). L'intérêt de l'approche probabiliste sera ainsi d'abord évalué dans un contexte de modélisation biogéochimique. / In spite of recent advances, biogeochemical models are still unable to represent the full complexity of marine ecosystems.Since mathematical formulations are still based on empirical laws involving many parameters, it is now well established that the uncertainties inherent to the biogeochemical complexity strongly impact the model response.Improving model representation therefore requires to properly describe model uncertainties and their consequences.Moreover, in the context of ocean color data assimilation, one of the major issue rely on our ability to characterize the model uncertainty (or equivalently the model error) in order to maximize the efficiency of the assimilation system.This is exactly the purpose of this PhD which investigates the potential of using random processes to simulate some biogeochemical uncertaintiesof the 1/4° coupled physical–biogeochemical NEMO/PISCES model of the North Atlantic ocean.Starting from a deterministic simulation performed with the original PISCES formulation, we propose a genericmethod based on AR(1) random processes to generate perturbations with temporal and spatial correlations.These perturbations are introduced into the model formulations to simulate 2 classes of uncertainties: theuncertainties on biogeochemical parameters and the uncertainties induced by unresolved scales in the presenceof non-linear processes. Using these stochastic parameterizations, a probabilistic version of PISCES is designedand a 60-member ensemble simulation is performed.The implications of this probabilistic approach is assessed using the information of the probability distributions given of this ensemble simulationThe relevance and the impacts of the stochastic parameterizations are assessed from a comparison with SeaWIFS satellite data.In particular, it is shown that the ensemble simulation is able to produce a better estimate of the surface chlorophyll concentration than the first guess deterministic simulation.Using SeaWIFS ocean color data observations, the statistical consistency (reliability) of this prior ensemble is demonstrated using rank histograms.Finally, the relevance of our approach in the prospect of ocean color data assimilation is demonstrated by considering a 3D optimal analysis of the ensemble (one updateat one time step) performed from the statistic errors of the stochastic ensemble simulation previously stated.During this experiment, the high resolution SeaWIFS ocean color data are assimilated using a Ensemble Transform Kalman Filter (ETKF) analysis scheme and the non gaussian behaviour and non linear relationshipbetween variables are taken into account using anamorphic transformations.More specifically, we show that the analysis of SeaWIFS data improves the representation and the ensemble statistics of chlorophyll concentrations.

Ce travail porte sur la quantification d'incertitude dans la modélisation des émissions de polluants atmosphériques dues au trafic routier d'une aire urbaine. Une chaîne de modélisations des émissions de polluants atmosphériques est construite, en couplant un modèle d’affectation dynamique du trafic (ADT) avec un modèle de facteurs d’émission. Cette chaîne est appliquée à l’agglomération de Clermont-Ferrand (France) à la résolution de la rue. Un métamodèle de l’ADT est construit pour réduire le temps d’évaluation du modèle. Une analyse de sensibilité globale est ensuite effectuée sur cette chaîne, afin d’identifier les entrées les plus influentes sur les sorties. Enfin, pour la quantification d’incertitude, deux ensembles sont construits avec l’approche de Monte Carlo, l’un pour l’ADT et l’autre pour les émissions. L’ensemble d’ADT est évalué et amélioré grâce à la comparaison avec les débits du trafic observés, afin de mieux échantillonner les incertitudes / This work focuses on the uncertainty quantification in the modeling of road traffic emissions in a metropolitan area. The first step is to estimate the time-dependent traffic flow at street-resolution for a full agglomeration area, using a dynamic traffic assignment (DTA) model. Then, a metamodel is built for the DTA model set up for the agglomeration, in order to reduce the computational cost of the DTA simulation. Then the road traffic emissions of atmospheric pollutants are estimated at street resolution, based on a modeling chain that couples the DTA metamodel with an emission factor model. This modeling chain is then used to conduct a global sensitivity analysis to identify the most influential inputs in computed traffic flows, speeds and emissions. At last, the uncertainty quantification is carried out based on ensemble simulations using Monte Carlo approach. The ensemble is evaluated with observations in order to check and optimize its reliability

Electric double layer capacitors (EDLCs) are an important class of electrical energy storage devices which store energy in the form of electric double layers. The charging mechanism is highly reversible physical adsorption of ions into the porous electrodes, which empower these devices to show a remarkable power performance (15kW/kg) and greater life expectancy (> 1 million cycles). However, they store a small amount of energy (5Wh/kg) when compared with batteries. Optimization of the performance of EDLCs based on porous activated carbons is highly challenging due to complex charging process prevailing in the Nano pores of electrodes. Molecular simulations provide information at the molecular scale which in turn can be used to develop insights that can explain experimental results and design improved EDLCs. The conventional approach to simulate EDLCs places both the electrodes and electrolyte region in a single simulation box. With present day computers, however, this one-box method limits us to system sizes of the order of nanometres whereas the size of a typical EDLC is at least of the order of micrometres. To overcome this system size limitation, a Gibbs-ensemble based Monte Carlo (MC) method was recently developed, where the electrodes are simulated in a separate simulation boxes and each box is subjected to periodic boundary conditions in all the three directions. This allows us to eliminate the electrode-electrolyte interface. The simulation of the bulk electrolyte is avoided through the use of the grand canonical ensemble. The electrode atoms in the electrode are maintained at an equal constant electric potential likewise the case in a pure conductor with the use of the constant voltage ensemble. In this thesis, the Gibbs-ensemble based MC simulations are performed for an EDLC consisting of porous electrodes. The simulations are performed with aqueous electrolytes of type MX and DX2 (where M=Na+, K+; D=Ca+2; X=Cl , F ) for a wide variety of operating conditions. The water is modelled as a continuum background with a dielectric constant value of 30. The electrodes are silicon carbide-derived carbon, whose microstructure generated from reverse MC technique, is used in the simulations. The results from these simulations help us understand the charge storage mechanism, the effect of size and valence of ions on the performance of nonporous carbon based EDLCs when the hydration effects are indignant. The thesis first demonstrates the presence of finite size effects in the simulations performed with the one-box method for KCl electrolyte. The capacitance (ratio of the charged stored on the positive electrode to the voltage applied) values obtained for KCl electrolyte with the one-box method are significantly higher than the corresponding values obtained from the Gibbs-ensemble method. This shows the presence of finite size effects in the one-box method simulations and justices the use of the Gibbs-ensemble based method in our simulations. The fundamental characteristics of aqueous electrolytes in the EDLC are analyzed with the simulation results for KCl electrolyte. In agreement with experiments and modern mean held theory, the capacitance monotonically decreases with voltage (bell-shaped curve) due to overcrowding of ions near the electrode surface. The charge storage mechanism in both the electrodes is mainly a combination of countering (ions oppositely charged to that of the electrode) adsorption and ion exchange, where coins (ions identically charged to that of the electrode) are replaced with countering. However, at higher voltages, the mechanism is predominantly counter ion adsorption because of the scarcity of coins in the electrodes. The mechanism is preferentially more ion exchange for the positive electrode because of its relatively bulky countering, Cl . The shifting of mechanism towards counter ion adsorption at higher voltages and preferential ion exchange process for the positive electrode are in qualitative agreement with the recent experimental results. The constraint of equal electric potential on all the electrode atoms of the amorphous electrode in the simulations resulted in a non-uniform average charge distribution on the electrodes. It shows that the Gibbs-ensemble simulation approach can account for the polarization effects which arises due to a complex topology of the electrodes. In agreement with earlier experiments and simulation studies, the local structure analyses of the electrodes shows that the highly conned ions store charge more efficiently. On the application of voltage difference between the electrodes, the electrolyte ions move towards higher degree of con ned regions of the electrodes indicating the charging process involves local rearrangement and rescuing of electrolyte ions. The thesis also discusses the effect of temperature and bulk concentration on the performance of EDLCs. The Gibbs-ensemble based simulations are performed for the EDLC with varying temperature and bulk concentration for the KCl electrolyte independently. In agreement with the Guo -Chapman theory and experiments, the capacitance decreases with the temperature and increases with the bulk concentration. This is because the concentration of countering in the electrodes decreases with an increase in the temperature but increases with an increase in the bulk concentration. Lastly, the effect of ion size and valency on the performance of EDLCs is analyzed. The capacitance monotonically decreases with voltage (bell-shaped curve) for all the electrolytes, except for NaF, where a maximum is observed at a non-zero finite voltage (camel-shaped curve). The capacitances of NaCl and NaF are greater than that for KCl and KF, respectively. This is because the smaller Na+ ions have more accessibility to narrow con ned regions, where the charge storage efficiency is high. As expected, the capacitance for CaCl2 and CaF2 are highest among their monovalent counterparts, NaCl and KCl; NaF and KF, respectively. This is attributed to the relatively smaller double layer thickness of the bivalent Ca+2 ions. Interestingly, at higher voltages, the capacitance for the bivalent electrolytes approaches the capacitance for the monovalent electrolytes because the concentration of Ca+2 ions in the negative electrode increases sluggishly with voltage due to a strong electrostatic repulsion between Ca+2 ions.

Ensemble simulation is a commonly used technique in operational forecasting of weather and floods. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists and hydrologists are interested in understanding the uncertainties associated with the simulation; specifically variability between the ensemble members. The visualization of ensemble members is currently accomplished through spaghetti plots or hydrographs. To improve visualization techniques and tools for forecasters, we conducted a userstudy to evaluate the effectiveness of existing uncertainty visualization techniques on 1D and 2D synthetic datasets. We designed an uncertainty evaluation framework to enable easier design of such studies for scientific visualization. The techniques evaluated are errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. Although we did not find a consistent order among the four techniques for all tasks, we found that the efficiency of techniques used highly depended on the tasks being performed. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. With results from the user-study, we iteratively developed a tool named ‘Noodles’ to interactively explore the ensemble uncertainty in weather simulations. Uncertainty was quantified using standard deviation, inter-quartile range, width of the 95% confidence interval, and by bootstrapping the data. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers and avoiding the parametrizations leading to these outliers. Additionally, they could identify spatial regions with high uncertainty thereby determining poorly simulated storm environments and deriving physical interpretation of these model issues. We also describe uncertainty visualization capabilities developed for a tool named ‘FloodViz’ for visualization and analysis of flood simulation ensembles. Simple member and trend plots and composited inundation maps with uncertainty are described along with different types of glyph based uncertainty representations. We also provide feedback from a hydrologist using various features of the tool from an operational perspective.