Abstract. 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.

中文翻译：

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对土地碳固存缺失 IPCC AR6 数据的估算

摘要。AR6 情景数据库是最新 IPCC 评估周期中使用的气候变化减缓路径的重要存储库。在当前版本中，数据库中的几种情景缺乏有关土地上总碳清除水平的信息，因为土地上的净碳清除量和总碳清除量并不总是分开的，并且在不同模型中一致报告。这使得关注碳清除的情景分析具有挑战性。我们测试并比较了不同回归模型的性能，以根据农业、​​林业和其他土地利用中的净 CO ₂排放量的可用数据来估算土地碳固存的缺失数据。我们发现梯度增强回归在测试的回归模型中表现最好，并提供了一个公开可用的估算数据集 [https://doi.org/10.5281/zenodo.10696654]（Prütz 等人，2024） AR6 场景数据库中有 404 个不完整的场景。我们讨论了我们的方法的局限性、其用例，以及该方法与其他最近的 AR6 数据重新分析的比较。