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DDC: 550 × Subject: Bayesian analysis ×

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Now showing 1 - 3 of 3
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    Agents, Bayes, and Climatic Risks - a modular modelling approach
    (München : European Geopyhsical Union, 2005) Haas, A.; Jaeger, C.
    When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine
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    A complete representation of uncertainties in layer-counted paleoclimatic archives
    (München : European Geopyhsical Union, 2017) Boers, Niklas; Goswami, Bedartha; Ghil, Michael
    Accurate time series representation of paleoclimatic proxy records is challenging because such records involve dating errors in addition to proxy measurement errors. Rigorous attention is rarely given to age uncertainties in paleoclimatic research, although the latter can severely bias the results of proxy record analysis. Here, we introduce a Bayesian approach to represent layer-counted proxy records – such as ice cores, sediments, corals, or tree rings – as sequences of probability distributions on absolute, error-free time axes. The method accounts for both proxy measurement errors and uncertainties arising from layer-counting-based dating of the records. An application to oxygen isotope ratios from the North Greenland Ice Core Project (NGRIP) record reveals that the counting errors, although seemingly small, lead to substantial uncertainties in the final representation of the oxygen isotope ratios. In particular, for the older parts of the NGRIP record, our results show that the total uncertainty originating from dating errors has been seriously underestimated. Our method is next applied to deriving the overall uncertainties of the Suigetsu radiocarbon comparison curve, which was recently obtained from varved sediment cores at Lake Suigetsu, Japan. This curve provides the only terrestrial radiocarbon comparison for the time interval 12.5–52.8 kyr BP. The uncertainties derived here can be readily employed to obtain complete error estimates for arbitrary radiometrically dated proxy records of this recent part of the last glacial interval.
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    Constraining ocean diffusivity from the 8.2 ka event
    (Berlin : Springer Verlag, 2010) Lorenz, A.; Held, H.; Bauer, E.; von Deimling, T.S.
    Greenland ice-core data containing the 8.2 ka event are utilized by a model-data intercomparison within the Earth system model of intermediate complexity, CLIMBER-2.3 to investigate their potential for constraining the range of uncertain ocean diffusivity properties. Within a stochastic version of the model (Bauer et al. in Paleoceanography 19:PA3014, 2004) it has been possible to mimic the pronounced cooling of the 8.2 ka event with relatively good accuracy considering the timing of the event in comparison to other modelling exercises. When statistically inferring from the 8.2 ka event on diffusivity the technical difficulty arises to establish the related likelihood numerically per realisation of the uncertain model parameters: while mainstream uncertainty analyses can assume a quasi-Gaussian shape of likelihood, with weather fluctuating around a long term mean, the 8.2 ka event as a highly nonlinear effect precludes such an a priori assumption. As a result of this study the Bayesian Analysis leads to a sharp single-mode likelihood for ocean diffusivity parameters within CLIMBER-2.3. Depending on the prior distribution this likelihood leads to a reduction of uncertainty in ocean diffusivity parameters (e. g. for flat prior uncertainty in the vertical ocean diffusivity parameter is reduced by factor 2). These results highlight the potential of paleo data to constrain uncertain system properties and strongly suggest to make further steps with more complex models and richer data sets to harvest this potential. © The Author(s) 2009.