Data-driven estimates of global litter production imply slower vegetation carbon turnover
© 2021 John Wiley & Sons Ltd.
| Publié dans: | Global change biology. - 1999. - 27(2021), 8 vom: 15. Apr., Seite 1678-1688 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2021
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| Accès à la collection: | Global change biology |
| Sujets: | Journal Article boosted regression trees land-surface models litter production vegetation carbon stock vegetation carbon turnover time Carbon 7440-44-0 |
| Résumé: | © 2021 John Wiley & Sons Ltd. Accurate quantification of vegetation carbon turnover time (τveg ) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg could only be estimated based on net primary productivity under the steady-state assumption. Here, we applied a machine-learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation-based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year-1 . By contrast, land-surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation-based τveg , modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle |
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| Description: | Date Completed 23.04.2021 Date Revised 23.04.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1365-2486 |
| DOI: | 10.1111/gcb.15515 |