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Published April 1, 2021 | Supplemental Material + Published
Journal Article Open

Soil moisture–atmosphere feedback dominates land carbon uptake variability

Abstract

Year-to-year changes in carbon uptake by terrestrial ecosystems have an essential role in determining atmospheric carbon dioxide concentrations. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale. Here we use factorial climate model simulations and show that variability in soil moisture drives 90 per cent of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedback amplifies temperature and humidity anomalies and enhances the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate that soil moisture has a dominant role, which is not readily apparent from land surface model simulations and observational analyses. These findings highlight the need to account for feedback between soil and atmospheric dryness when estimating the response of the carbon cycle to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to droughts. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects that are controlled by soil moisture.

Additional Information

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 05 February 2020; Accepted 02 February 2021; Published 31 March 2021. This research was funded by a Postdoc.Mobility fellowship of the Swiss National Science Foundation (P400P2_180784). C.F. acknowledges funding through NASA IDS grant 80NSSC17K0687. P.G. acknowledges funding from NASA 80NSSC18K0998 and European Research Council synergy grant USMILE ERC CU18-3746. P.C. acknowledges funding from the ANR CLAND convergence institute. S.I.S. acknowledges partial support from the European Union's Horizon 2020 Research and Innovation Program (grant agreement 821003 (4C)). We thank all modelling groups who participated in the GLACE-CMIP5 experiments and conducted the model runs, in particular F. Cheruy, S. Hagemann and D. Lawrence. We also thank G. Bonan, J. K. Green, M. Hirschi, D. Lawrence, D. Miralles, U. Weber and Y. Yin for comments on the analyses, data availability or the manuscript. Data availability: GLACE-CMIP5 model outputs can be obtained from S.I.S. (sonia.seneviratne@ethz.ch). FluxCom data are available at http://www.fluxcom.org/CF-Download/. CAMS data are available from the Atmosphere Data Store at https://atmosphere.copernicus.eu/data. ERA5 and ERA5Land data are available from the Climate Data Store at https://cds.climate.copernicus.eu. VPM-GPP is available at https://doi.org/10.6084/m9.figshare.c.3789814. ESA CCI Soil Moisture is available at https://www.esa-soilmoisture-cci.org. CRU TS data are available at https://crudata.uea.ac.uk/cru/data/hrg/. GSWP3 data are available at https://doi.org/10.20783/DIAS.501. The corresponding author can also be contacted at vincent.humphrey@bluewin.ch. Source data are provided with this paper. Code availability: Code and documentation for CCSM4 is publicly available at https://www.cesm.ucar.edu/models/ccsm4.0/. Code and documentation for ECHAM6 (MPI-ESM) is available for scientific users at https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability. Code and documentation for the GFDL model is publicly available at https://www.gfdl.noaa.gov/modeling-systems-group-public-releases/. Code and documentation for the IPSL model is publicly available at https://cmc.ipsl.fr/ipsl-climate-models/ipsl-cm5/. Model outputs were processed using the software Matlab 2019a. Open access funding provided by Max Planck Society. Author Contributions: V.H. designed and conducted the study. S.I.S. designed and coordinated the GLACE-CMIP5 climate model experiment. A.B., P.C., P.G., M.J., M.R., S.I.S. and C.F. provided feedback on the analyses, the figures and the manuscript. The authors declare no competing interests. Peer review information: Nature thanks Belinda Medlyn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Attached Files

Published - s41586-021-03325-5.pdf

Supplemental Material - 41586_2021_3325_Fig10_ESM.webp

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023