Comment on essd-2024-68

Abstract

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The AR6 Scenario Database is a vital repository of climate change mitigation pathways used in the latest IPCC assessment cycle. In its current version, several scenarios in the database lack information about the level of gross carbon removal on land, as net and gross removals on land are not always separated and consistently reported across models. This makes scenario analyses focusing on carbon removals challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration from available data on net CO2 emissions in agriculture, forestry, and other land use. We find that a gradient boosting regression performs best among the tested regression models and provide a publicly available imputation dataset [https://doi.org/10.5281/zenodo.10696654] (Prütz et al., 2024) on carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. We discuss the limitations of our approach, its use cases, and how this approach compares to other recent AR6 data re-analyses. Climate change mitigation pathways, created with integrated assessment models (IAMs), have come to take up a critical role in the assessment work of Working Group III of the Intergovernmental Panel on Climate Change (IPCC) (Riahi et al., 2022; Guivarch et al., 2022) . The AR6 Scenario Database hosted by the International Institute for Applied Systems Analysis (IIASA) contains climate change mitigation pathways compiled for and considered in the Working Group III Contribution to the IPCC