2022
DOI: 10.1038/s41558-022-01349-x
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Using large ensembles of climate change mitigation scenarios for robust insights

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Cited by 46 publications
(32 citation statements)
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“…Some gases that are represented in climate emulators are not modelled for any long-term global scenario IAM considered in AR6, though these particular emissions have a relatively small projected impact on climate change. To maximise the richness and diversity of scenarios available in a given assessment (Guivarch et al, 2022), a process of infilling scenarios with missing emissions data is performed. There is, however, no unique way of infilling scenarios with missing data.…”
Section: The Inclusion Of Multiple Climate Emulatorsmentioning
confidence: 99%
See 1 more Smart Citation
“…Some gases that are represented in climate emulators are not modelled for any long-term global scenario IAM considered in AR6, though these particular emissions have a relatively small projected impact on climate change. To maximise the richness and diversity of scenarios available in a given assessment (Guivarch et al, 2022), a process of infilling scenarios with missing emissions data is performed. There is, however, no unique way of infilling scenarios with missing data.…”
Section: The Inclusion Of Multiple Climate Emulatorsmentioning
confidence: 99%
“…Describing larger scenario categories comes with further limitations, because summary statistics can conceal the underlying distribution or overemphasise outliers. Further efforts could be made to describe key scenario characteristics by developing methods that correct for potential biases in the underlying scenario database, such as overrepresentation of scenarios from one specific modelling framework or weightings based on feasibility, historical compatibility, or scenario similarity (Guivarch et al, 2022). Other topics that might be relevant for a more multidimensional categorisation could be a separation of scenarios by their temperature decline after their peak or the associated reliance on net-negative emissions to achieve this.…”
Section: Scenario Classification Approachesmentioning
confidence: 99%
“…These heterogeneous strengths and weaknesses of each model highlight the importance of diverse model ensembles, like the one employed here, in shedding light on various effects of policy and providing a robust assessment within a spectrum of uncertainty inherent in model theory and dynamics. 55…”
Section: Llmentioning
confidence: 99%
“…In this work, we have used an ethically, and methodologically transparent approach to construct emission scenarios with heterogenous regional objectives. However, as is true for any work that relies on an unstructured ensemble of opportunity, we cannot draw conclusions on whether the regional bounds assessed here are the actual lowest possible regional emissions (Guivarch et al, 2022). The scenarios (per modelling framework) constructed here should be understood to represent a potential scenario that could have been constructed by that modelling framework if: (i) regionally differentiated carbon prices were applied to match a predefined regional carbon budget, (ii) regional carbon budgets are applied.…”
Section: Limitations and Outlook For Further Workmentioning
confidence: 91%