Preprints
https://doi.org/10.5194/essd-2024-68
https://doi.org/10.5194/essd-2024-68
14 Mar 2024
 | 14 Mar 2024
Status: this preprint is currently under review for the journal ESSD.

Imputation of missing IPCC AR6 data on land carbon sequestration

Ruben Prütz, Sabine Fuss, and Joeri Rogelj

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.

Ruben Prütz, Sabine Fuss, and Joeri Rogelj

Status: open (until 18 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-68', Thomas Bossy, 08 Apr 2024 reply
Ruben Prütz, Sabine Fuss, and Joeri Rogelj

Data sets

Imputation of missing IPCC AR6 data on land carbon sequestration Ruben Prütz, Sabine Fuss, and Joeri Rogelj https://zenodo.org/doi/10.5281/zenodo.10696653

Model code and software

Imputation of missing IPCC AR6 data on land carbon sequestration Ruben Prütz, Sabine Fuss, and Joeri Rogelj https://zenodo.org/doi/10.5281/zenodo.10696653

Ruben Prütz, Sabine Fuss, and Joeri Rogelj

Viewed

Total article views: 331 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
277 37 17 331 14 15
  • HTML: 277
  • PDF: 37
  • XML: 17
  • Total: 331
  • BibTeX: 14
  • EndNote: 15
Views and downloads (calculated since 14 Mar 2024)
Cumulative views and downloads (calculated since 14 Mar 2024)

Viewed (geographical distribution)

Total article views: 325 (including HTML, PDF, and XML) Thereof 325 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Apr 2024
Download
Short summary
The AR6 Scenario Database, used by the IPCC, lacks data on land carbon removal for several vetted pathways, hindering secondary scenario analyses. We tested and compared four regression models, identifying gradient boosting regression as the most effective to predict this missing land carbon removal data. We provide a publicly available imputation dataset for 404 incomplete scenarios and discuss the limitations of our study, its use cases, and how our dataset compares to other recent approaches.
Altmetrics