@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}
}