TY  - JOUR
A1  - Harmsen, Mathijs
A1  - Kriegler, Elmar
A1  - van Vuuren, Detlef P.
A1  - van der Wijst, Kaj-Ivar
A1  - Luderer, Gunnar
A1  - Cui, Ryna
A1  - Dessens, Olivier
A1  - Drouet, Laurent
A1  - Emmerling, Johannes
A1  - Morris, Jennifer Faye
A1  - Fosse, Florian
A1  - Fragkiadakis, Dimitris
A1  - Fragkiadakis, Kostas
A1  - Fragkos, Panagiotis
A1  - Fricko, Oliver
A1  - Fujimori, Shinichiro
A1  - Gernaat, David
A1  - Guivarch, Céline
A1  - Iyer, Gokul
A1  - Karkatsoulis, Panagiotis
A1  - Keppo, Ilkka
A1  - Keramidas, Kimon
A1  - Köberle, Alexandre
A1  - Kolp, Peter
A1  - Krey, Volker
A1  - Krüger, Christoph
A1  - Leblanc, Florian
A1  - Mittal, Shivika
A1  - Paltsev, Sergey
A1  - Rochedo, Pedro
A1  - van Ruijven, Bas J.
A1  - Sands, Ronald D.
A1  - Sano, Fuminori
A1  - Strefler, Jessica
A1  - Arroyo, Eveline Vasquez
A1  - Wada, Kenichi
A1  - Zakeri, Behnam
T1  - Integrated assessment model diagnostics
BT  - key indicators and model evolution
JF  - Environmental research letters
N2  - 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.
KW  - diagnostics
KW  - integrated assessment models
KW  - climate policy
KW  - Assessment Report IPCC
KW  - renewable energy
KW  - migration
KW  - AR6
Y1  - 2021
U6  - https://doi.org/10.1088/1748-9326/abf964
SN  - 1748-9326
VL  - 16
IS  - 5
PB  - IOP Publishing
CY  - Bristol
ER  - 
TY  - JOUR
A1  - Wietzke, Luzie M.
A1  - Merz, Bruno
A1  - Gerlitz, Lars
A1  - Kreibich, Heidi
A1  - Guse, Björn
A1  - Castellarin, Attilio
A1  - Vorogushyn, Sergiy
T1  - Comparative analysis of scalar upper tail indicators
JF  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - 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.
KW  - upper tail behaviour
KW  - heavy-tailed distributions
KW  - extremes
KW  - diagnostics
KW  - surprise
Y1  - 2020
U6  - https://doi.org/10.1080/02626667.2020.1769104
SN  - 0262-6667
SN  - 2150-3435
VL  - 65
IS  - 10
SP  - 1625
EP  - 1639
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -