Open AccessIntegrated assessment model diagnostics: key indicators and model evolution
Author(s) -
Mathijs Harmsen,
Elmar Kriegler,
Detlef P. van Vuuren,
Kaj-Ivar van der Wijst,
Gunnar Luderer,
Ryna Cui,
Olivier Dessens,
Laurent Drouet,
Johannes Emmerling,
Jennifer Morris,
Florian Fosse,
Dimitris Fragkiadakis,
Kostas Fragkiadakis,
Panagiotis Fragkos,
Oliver Fricko,
Shinichiro Fujimori,
David Gernaat,
Céline Guivarch,
Gokul Iyer,
Panagiotis Karkatsoulis,
Ilkka Keppo,
Kimon Keramidas,
Alexandre C. Köberle,
Peter Kolp,
Volker Krey,
Christoph Krüger,
Florian Leblanc,
Shivika Mittal,
Sergey Paltsev,
Pedro Rochedo,
Bas van Ruijven,
Ronald D. Sands,
Fuminori Sano,
Jessica Strefler,
Eveline Vasquez-Arroyo,
Kazumi Wada,
Behnam Zakeri
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abf964
Subject(s) - comparability , computer science , index (typography) , econometrics , set (abstract data type) , climate change , key (lock) , transparency (behavior) , operations research , mathematics , ecology , computer security , combinatorics , world wide web , biology , programming language
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.