@phdthesis{Kleinen2005,
  author    = {Kleinen, Thomas Christopher},
  title     = {Stochastic information in the assessment of climate change},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5382},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {Stochastic information, to be understood as \"information gained by the application of stochastic methods\", is proposed as a tool in the assessment of changes in climate. This thesis aims at demonstrating that stochastic information can improve the consideration and reduction of uncertainty in the assessment of changes in climate. The thesis consists of three parts. In part one, an indicator is developed that allows the determination of the proximity to a critical threshold. In part two, the tolerable windows approach (TWA) is extended to a probabilistic TWA. In part three, an integrated assessment of changes in flooding probability due to climate change is conducted within the TWA. The thermohaline circulation (THC) is a circulation system in the North Atlantic, where the circulation may break down in a saddle-node bifurcation under the influence of climate change. Due to uncertainty in ocean models, it is currently very difficult to determine the distance of the THC to the bifurcation point. We propose a new indicator to determine the system's proximity to the bifurcation point by considering the THC as a stochastic system and using the information contained in the fluctuations of the circulation around the mean state. As the system is moved closer to the bifurcation point, the power spectrum of the overturning becomes \"redder\", i.e. more energy is contained in the low frequencies. Since the spectral changes are a generic property of the saddle-node bifurcation, the method is not limited to the THC, but it could also be applicable to other systems, e.g. transitions in ecosystems. In part two, a probabilistic extension to the tolerable windows approach (TWA) is developed. In the TWA, the aim is to determine the complete set of emission strategies that are compatible with so-called guardrails. Guardrails are limits to impacts of climate change or to climate change itself. Therefore, the TWA determines the \"maneuvering space\" humanity has, if certain impacts of climate change are to be avoided. Due to uncertainty it is not possible to definitely exclude the impacts of climate change considered, but there will always be a certain probability of violating a guardrail. Therefore the TWA is extended to a probabilistic TWA that is able to consider \"probabilistic uncertainty\", i.e. uncertainty that can be expressed as a probability distribution or uncertainty that arises through natural variability. As a first application, temperature guardrails are imposed, and the dependence of emission reduction strategies on probability distributions for climate sensitivities is investigated. The analysis suggests that it will be difficult to observe a temperature guardrail of 2\°C with high probabilities of actually meeting the target. In part three, an integrated assessment of changes in flooding probability due to climate change is conducted. A simple hydrological model is presented, as well as a downscaling scheme that allows the reconstruction of the spatio-temporal natural variability of temperature and precipitation. These are used to determine a probabilistic climate impact response function (CIRF), a function that allows the assessment of changes in probability of certain flood events under conditions of a changed climate. The assessment of changes in flooding probability is conducted in 83 major river basins. Not all floods can be considered: Events that either happen very fast, or affect only a very small area can not be considered, but large-scale flooding due to strong longer-lasting precipitation events can be considered. Finally, the probabilistic CIRFs obtained are used to determine emission corridors, where the guardrail is a limit to the fraction of world population that is affected by a predefined shift in probability of the 50-year flood event. This latter analysis has two main results. The uncertainty about regional changes in climate is still very high, and even small amounts of further climate change may lead to large changes in flooding probability in some river systems.},
  subject      = {Anthropogene Klima{\"a}nderung},
  language  = {en}
}
@phdthesis{Zickfeld2003,
  author    = {Zickfeld, Kirsten},
  title     = {Modeling large-scale singular climate events for integrated assessment},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0001176},
  school      = {Universit{\"a}t Potsdam},
  year      = {2003},
  abstract  = {Erkenntnisse aus pal{\"a}oklimatologischen Studien, theoretischen Betrachtungen und Modellsimulationen deuten darauf hin, dass anthropogene Emissionen von Treibhausgasen und Aerosolen zu großskaligen, singul{\"a}ren Klimaereignissen f{\"u}hren k{\"o}nnten. Diese bezeichnen stark nichtlineare, abrupte Klima{\"a}nderungen, mit regionalen bis hin zu globalen Auswirkungen. Ziel dieser Arbeit ist die Entwicklung von Modellen zweier maßgeblicher Komponenten des Klimasystems, die singul{\"a}res Verhalten aufweisen k{\"o}nnten: die atlantische thermohaline Zirkulation (THC) und der indische Monsun. Diese Modelle sind so konzipiert, dass sie den Anforderungen der "Integrated Assessment"-Modellierung gen{\"u}gen, d.h., sie sind realistisch, recheneffizient, transparent und flexibel. Das THC-Modell ist ein einfaches, interhemisph{\"a}risches Boxmodell, das anhand von Daten kalibriert wird, die mit einem gekoppelten Klimamodell mittlerer Komplexit{\"a}t erzeugt wurden. Das Modell wird durch die globale Mitteltemperatur angetrieben, die mit Hilfe eines linearen Downscaling-Verfahrens in regionale W{\"a}rme- und S{\"u}ßwasserfl{\"u}sse {\"u}bersetzt wird. Die Ergebnisse einer Vielzahl von zeitabh{\"a}ngigen Simulationen zeigen, dass das Modell in der Lage ist, maßgebliche Eigenschaften des Verhaltens komplexer Klimamodelle wiederzugeben, wie die Sensitivit{\"a}t bez{\"u}glich des Ausmaßes, der regionalen Verteilung und der Rate der Klima{\"a}nderung. Der indische Monsun wird anhand eines neuartigen eindimensionalen Boxmodells der tropischen Atmosph{\"a}re beschrieben. Dieses enth{\"a}lt Parmetrisierungen der Oberfl{\"a}chen- und Strahlungsfl{\"u}sse, des hydrologischen Kreislaufs und derHydrologie der Landoberfl{\"a}che. Trotz des hohen Idealisierungsgrades ist das Modell in der Lage, relevante Aspekte der beobachteten Monsundynamik, wie z.B. den Jahresgang des Niederschlags und das Eintritts- sowie R{\"u}ckzugsdatum des Sommermonsuns, zufrieden stellend zu simulieren. Außerdem erfasst das Modell die Sensitivit{\"a}tdes Monsuns bez{\"u}glich {\"A}nderungen der Treibhausgas- und Aerosolkonzentrationen, die aus komplexeren Modellen bekannt sind. Eine vereinfachte Version des Monsunmodells wird f{\"u}r die Untersuchung des qualitativen Systemverhaltens in Abh{\"a}ngigkeit von {\"A}nderungen der Randbedingungen eingesetzt. Das bemerkenswerteste Ergebnis ist das Auftreten einer Sattelknotenbifurkation des Sommermonsuns f{\"u}r kritische Werte der Albedo oder der Sonneneinstrahlung. Dar{\"u}ber hinaus weist das Modell zwei stabile Zust{\"a}nde auf: neben dem niederschlagsreichen Sommermonsun besteht ein Zustand, der sich durch einen schwachen hydrologischen Kreislauf auszeichnet. Das Beachtliche an diesen Ergebnissen ist, dass anthropogene St{\"o}rungen der plantetaren Albedo, wie Schwefelemissionen und/oder Landnutzungs{\"a}nderungen, zu einer Destabilisierung des indischen Monsuns f{\"u}hren k{\"o}nnten. Das THC-Boxmodell findet exemplarische Anwendung in einem "Integrated Assessment" von Klimaschutzstrategien. Basierend auf dem konzeptionellen und methodischen Ger{\"u}st des Leitplankenansatzes werden Emissionskorridore (d.h. zul{\"a}ssige Spannen an CO2-Emissionen) berechnet, die das Risiko eines THC-Zusammenbruchs begrenzen sowie sozio{\"o}konomische Randbedingungen ber{\"u}cksichtigen. Die Ergebnisse zeigen u.a. eine starke Abh{\"a}ngigkeit der Breite der Emissionskorridore von der Klima- und hydrologischen Sensitivit{\"a}t. F{\"u}r kleine Werte einer oder beider Sensitivit{\"a}ten liegt der obere Korridorrand bei weit h{\"o}heren Emissionswerten als jene, die von plausiblen Emissionsszenarien f{\"u}r das 21. Jahrhundert erreicht werden. F{\"u}r große Werte der Sensitivit{\"a}ten hingegen, verlassen schon niedrige Emissionsszenarien den Korridor in den fr{\"u}hen Jahrzehnten des 21. Jahrhunderts. Dies impliziert eine Abkehr von den gegenw{\"a}rtigen Emissionstrends innherhalb der kommenden Jahrzehnte, wenn das Risko eines THC Zusammenbruchs gering gehalten werden soll. Anhand einer Vielzahl von Anwendungen - von Sensitivit{\"a}ts- {\"u}ber Bifurkationsanalysen hin zu integrierter Modellierung - zeigt diese Arbeit den Wert reduzierter Modelle auf. Die Ergebnisse und die daraus zu ziehenden Schlussfolgerungen liefern einen wertvollen Beitrag zu der wissenschaftlichen und politischen Diskussion bez{\"u}glich der Folgen des anthropogenen Klimawandels und der langfristigen Klimaschutzziele.},
  language  = {en}
}
@phdthesis{Kriegler2005,
  author    = {Kriegler, Elmar},
  title     = {Imprecise probability analysis for integrated assessment of climate change},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5611},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {We present an application of imprecise probability theory to the quantification of uncertainty in the integrated assessment of climate change. Our work is motivated by the fact that uncertainty about climate change is pervasive, and therefore requires a thorough treatment in the integrated assessment process. Classical probability theory faces some severe difficulties in this respect, since it cannot capture very poor states of information in a satisfactory manner. A more general framework is provided by imprecise probability theory, which offers a similarly firm evidential and behavioural foundation, while at the same time allowing to capture more diverse states of information. An imprecise probability describes the information in terms of lower and upper bounds on probability. For the purpose of our imprecise probability analysis, we construct a diffusion ocean energy balance climate model that parameterises the global mean temperature response to secular trends in the radiative forcing in terms of climate sensitivity and effective vertical ocean heat diffusivity. We compare the model behaviour to the 20th century temperature record in order to derive a likelihood function for these two parameters and the forcing strength of anthropogenic sulphate aerosols. Results show a strong positive correlation between climate sensitivity and ocean heat diffusivity, and between climate sensitivity and absolute strength of the sulphate forcing. We identify two suitable imprecise probability classes for an efficient representation of the uncertainty about the climate model parameters and provide an algorithm to construct a belief function for the prior parameter uncertainty from a set of probability constraints that can be deduced from the literature or observational data. For the purpose of updating the prior with the likelihood function, we establish a methodological framework that allows us to perform the updating procedure efficiently for two different updating rules: Dempster's rule of conditioning and the Generalised Bayes' rule. Dempster's rule yields a posterior belief function in good qualitative agreement with previous studies that tried to constrain climate sensitivity and sulphate aerosol cooling. In contrast, we are not able to produce meaningful imprecise posterior probability bounds from the application of the Generalised Bayes' Rule. We can attribute this result mainly to our choice of representing the prior uncertainty by a belief function. We project the Dempster-updated belief function for the climate model parameters onto estimates of future global mean temperature change under several emissions scenarios for the 21st century, and several long-term stabilisation policies. Within the limitations of our analysis we find that it requires a stringent stabilisation level of around 450 ppm carbon dioxide equivalent concentration to obtain a non-negligible lower probability of limiting the warming to 2 degrees Celsius. We discuss several frameworks of decision-making under ambiguity and show that they can lead to a variety of, possibly imprecise, climate policy recommendations. We find, however, that poor states of information do not necessarily impede a useful policy advice. We conclude that imprecise probabilities constitute indeed a promising candidate for the adequate treatment of uncertainty in the integrated assessment of climate change. We have constructed prior belief functions that allow much weaker assumptions on the prior state of information than a prior probability would require and, nevertheless, can be propagated through the entire assessment process. As a caveat, the updating issue needs further investigation. Belief functions constitute only a sensible choice for the prior uncertainty representation if more restrictive updating rules than the Generalised Bayes'Rule are available.},
  subject      = {Anthropogene Klima{\"a}nderung},
  language  = {en}
}