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  • 1
    Call number: PIK N 456-16-89766
    Type of Medium: Monograph available for loan
    Pages: VII, 185 S. , Ill., graph. Darst., Kt.
    Language: Undetermined
    Branch Library: PIK Library
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  • 2
    Monograph available for loan
    Monograph available for loan
    Cambridge, United Kingdom : Cambridge University Press
    Call number: PIK N 456-18-91566
    Type of Medium: Monograph available for loan
    Pages: xvi, 347 Seiten , Diagramme , 25 cm
    ISBN: 9781107066052
    Language: English
    Note: Contents: 1. Introduction ; Part I. Background and Fundamentals: 2. Regional climate ; 3. History of downscaling ; 4. Rationale of downscaling ; 5. User needs ; 6. Mathematical and statistical methods ; 7. Reference observations ; 8. Climate modelling ; 9. Uncertainties ; Part II. Statistical Downscaling Concepts and Methods: 10. Structure of statistical downscaling methods ; 11. Perfect prognosis ; 12. Model output statistics ; 13. Weather generators ; 14. Other approaches ; Part III. Downscaling in Practice and Outlook: 15. Evaluation ; 16. Performance of statistical downscaling ; 17. A regional modelling debate ; 18. Use of downscaling in practice ; 19. Outlook ; Appendix A ; Appendix B
    Branch Library: PIK Library
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  • 3
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 432 (2004), S. 290-291 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] The principal extratropical atmospheric circulation mode in the Southern Hemisphere, the Antarctic oscillation (or Southern Hemisphere annular mode), represents fluctuations in the strength of the circumpolar vortex and has shown a trend towards a positive index in austral summer in recent ...
    Type of Medium: Electronic Resource
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  • 4
    Publication Date: 2017-08-25
    Description: Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs.
    Type: Article , PeerReviewed
    Format: text
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  • 5
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    AGU / Wiley
    In:  Journal of Geophysical Research: Atmospheres, 119 (19). 11,040-11,053.
    Publication Date: 2018-02-27
    Description: In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.
    Type: Article , PeerReviewed
    Format: text
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  • 6
    Publication Date: 2019-05-24
    Description: VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 7
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    Copernicus Publications (EGU)
    In:  Hydrology and Earth System Sciences, 21 . pp. 1693-1719.
    Publication Date: 2019-05-23
    Description: Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional climate models (RCM) that have resolutions which are much coarser than typical spatial scales of precipitation, particularly extremes. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Wong et al. developed a model to jointly bias correct and downscale precipitation at daily scales. This approach, however, relied on pairwise correspondence between predictor and predictand for calibration, and thus, on nudged simulations which are rarely available. Here we present an extension of this approach that separates the downscaling from the bias correction and in principle is applicable to free running GCMs/RCMs. In a first step, we bias correct RCM-simulated precipitation against gridded observations at the same scale using a parametric quantile mapping approach. To correct the whole distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail. In a second step, we bridge the scale gap: we predict local variance employing a vector generalized linear gamma model (VGLM gamma) with the bias corrected time series as predictor. The VGLM gamma model is calibrated between gridded and point scale (station) observations. For evaluation we adopt the perfect predictor experimental setup of VALUE. Precipitation is in most cases improved by (parts of) our method across different European climates. The method generally performs better in summer than in winter and in winter best in the Mediterranean region with a mild winter climate and worst for continental winter climate in mid & eastern Europe or Scandinavia. A strength of this two-step method is that the best combination of bias correction and downscaling methods can be selected. This implies that the concept can be extended to a wide range of method combinations.
    Type: Article , PeerReviewed
    Format: text
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  • 8
    Publication Date: 2019-07-13
    Description: Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy-model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations.We subsequently explore means of using proxy-model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy-model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN50993 , Climate of the Past (e-ISSN 1814-9332); 13; 12; 1851-1900
    Format: text
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  • 9
    Publication Date: 2019-07-25
    Type: Article , NonPeerReviewed
    Format: text
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  • 10
    Publication Date: 2013-11-30
    Description: Landfill gas is composed of methane (CH 4 ) and (CO 2 ) at a ratio of about (60% – 40%), whereby the impact of methane on the greenhouse effect is about 25 times higher than that of carbon dioxide. Bacterial methane oxidation, taking place in the landfill cover layer, helps to reduce the climate active emissions from landfill sites. This contribution presents a theoretical and numerical approach to model the coupled processes of bacterial methane oxidation. An isothermal biphasic model based on the Theory of Porous Media (TPM) and Mixture Theory is introduced as well as the coupled finite element (FE) calculation concept. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)
    Electronic ISSN: 1617-7061
    Topics: Mathematics , Physics , Technology
    Published by Wiley
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