@article{HarmsenKrieglervanVuurenetal.2021,
  author    = {Harmsen, Mathijs and Kriegler, Elmar and van Vuuren, Detlef P. and van der Wijst, Kaj-Ivar and Luderer, Gunnar and Cui, Ryna and Dessens, Olivier and Drouet, Laurent and Emmerling, Johannes and Morris, Jennifer Faye and Fosse, Florian and Fragkiadakis, Dimitris and Fragkiadakis, Kostas and Fragkos, Panagiotis and Fricko, Oliver and Fujimori, Shinichiro and Gernaat, David and Guivarch, C{\´e}line and Iyer, Gokul and Karkatsoulis, Panagiotis and Keppo, Ilkka and Keramidas, Kimon and K{\"o}berle, Alexandre and Kolp, Peter and Krey, Volker and Kr{\"u}ger, Christoph and Leblanc, Florian and Mittal, Shivika and Paltsev, Sergey and Rochedo, Pedro and van Ruijven, Bas J. and Sands, Ronald D. and Sano, Fuminori and Strefler, Jessica and Arroyo, Eveline Vasquez and Wada, Kenichi and Zakeri, Behnam},
  title     = {Integrated assessment model diagnostics},
  series = {Environmental research letters},
  volume    = {16},
  journal   = {Environmental research letters},
  number    = {5},
  publisher = {IOP Publishing},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/abf964},
  pages     = {13},
  year      = {2021},
  abstract  = {Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45-61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.},
  language  = {en}
}
@article{WietzkeMerzGerlitzetal.2020,
  author    = {Wietzke, Luzie M. and Merz, Bruno and Gerlitz, Lars and Kreibich, Heidi and Guse, Bj{\"o}rn and Castellarin, Attilio and Vorogushyn, Sergiy},
  title     = {Comparative analysis of scalar upper tail indicators},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {65},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {10},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2020.1769104},
  pages     = {1625 -- 1639},
  year      = {2020},
  abstract  = {Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution.},
  language  = {en}
}
@article{WippertArampatzisBanzeretal.2019,
  author    = {Wippert, Pia-Maria and Arampatzis, Adamantios and Banzer, Winfried and Beck, Heidrun and Hasenbring, Monika Ilona and Schiltenwolf, Marcus and Schneider, Christian and Stengel, Dirk and Platen, Petra and Mayer, Frank},
  title     = {Psychosoziale Risikofaktoren in der Entstehung von chronisch unspezifischen R{\"u}ckenschmerzen},
  series = {Zeitschrift f{\"u}r Sportpsychologie},
  volume    = {26},
  journal   = {Zeitschrift f{\"u}r Sportpsychologie},
  number    = {1},
  publisher = {Hogrefe},
  address   = {G{\"o}ttingen},
  issn      = {1612-5010},
  doi       = {10.1026/1612-5010/a000245},
  pages     = {25 -- 35},
  year      = {2019},
  abstract  = {Chronisch unspezifische R{\"u}ckenschmerzen (CURS) geh{\"o}ren international zu den h{\"a}ufigsten Schmerzph{\"a}nomenen und k{\"o}nnen f{\"u}r Athletinnen und Athleten karrierelimitierend sein. Knapp ein Drittel der j{\"a}hrlichen Trainingsausfallzeiten werden auf CURS zur{\"u}ckgef{\"u}hrt. In der Entstehung von chronischen Schmerzen ist ein multifaktorielles {\"A}tiologiemodell mit einem signifikanten Einfluss psychosozialer Risikofaktoren evident. Obwohl dies in der Allgemeinbev{\"o}lkerung bereits gut erforscht ist, gibt es in der Sportwissenschaft vergleichsweise wenige Arbeiten dar{\"u}ber. Dieses Thema wird daher in drei Multicenterstudien und zahlreichen Teilstudien des MiSpEx-Netzwerks (Medicine in Spine-Exercise-Network, F{\"o}rderzeitraum 2011 - 2018) aufgegriffen. Entsprechend der Empfehlung einer fr{\"u}hzeitigen Diagnostik von Chronifizierungsfaktoren in der „Nationalen Versorgungsleitlinie Kreuzschmerz", besch{\"a}ftigt sich das Netzwerk u. a. mit der {\"U}berpr{\"u}fung, Entwicklung und Evaluation diagnostischer M{\"o}glichkeiten. Der vorliegende Beitrag beschreibt die Entwicklung einer Diagnostik von psychosozialen Risikofaktoren, die einerseits eine Einsch{\"a}tzung des Risikos der Entwicklung von CURS und andererseits eine individuelle Zuweisung zu (Trainings)Interventionen erlaubt. Es wird die Entwicklungsrationale beschrieben und dabei verschiedene methodische Herangehensweisen und Entscheidungssequenzen reflektiert.},
  language  = {de}
}