Integrated assessment model diagnostics: key indicators and model evolution

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.

A multi-model analysis of long-term emissions and warming implications of current mitigation efforts

“…We focus on global energy CO 2 emissions to 2050 as all our IAMs represent these emissions sources as a minimum. Current policy constrained scenarios reach levels of emissions between [32][33][34][35][36] in 2030 and 26-40 GtCO 2 in 2050 (Figure 1a) and NDC constrained scenarios reach levels of emissions between 30-34 GtCO 2 in 2030 and 23-38 GtCO 2 in 2050 (Figure 1b). Global differences in emissions between current policy and NDC constrained scenarios arise because not all regions are on track to meet their NDC targets.…”

“…These multiple and complex interactions are precisely why it is difficult to map individual model characteristics and assumptions to emissions outcomes. Efforts have emerged to create diagnostic indicators for IAMs 32,33 to help describe how a model responds to climate policy, but these indicators…”

Using large ensembles of climate change mitigation scenarios for robust insights

“…Third, potential avenues include developing weighting schemes for models or scenarios, following some practices implemented with climate scenarios 48,49 . Such weighting schemes can be based on inter-model distance of output, inputs or model characteristics, as experienced through hierarchical clustering in climate science 50 , and could be based on recently developed models diagnostics 51 . The use of a scenario ensemble for analysis or decision support may require prioritizing a smaller number of scenarios aligned with the specific purpose.…”

“…We call upon the Integrated Assessment Modelling Consortium, or a similar organization, to coordinate scenario ensembles, and associated meta-data, compilation activities into a common place. It would benefit from building on the templates developed for IPCC databases and the practice of harmonized models documentation (https://www.iamcdocumentation.eu/) and diagnostics 51 .…”

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

“…This highlights the difficult but important task of designing suitably encompassing scenarios from which such hybrids can be drawn. Multi‐model comparisons often find large structural (Duan et al., 2019) and parametric (Krey et al., 2019) differences across IAMs that propagate into simulated outcomes (Harmsen et al., 2021; von Lampe et al., 2014). Behind any given quantitative projection in the SSP framework is an assumption that the underlying IAM has produced a plausible real‐world trajectory, but this has been increasingly challenged, particularly with respect to energy mixes (Burgess et al., 2021; Hausfather & Peters, 2020; Ritchie & Dowlatabadi, 2017a, 2017b).…”