The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Atmospheric dryness reduces photosynthesis along a large range of soil water deficits
Open accessAbstract
Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates.
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Cites
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Training feedforward networks with the Marquardt algorithm
Hagan, M. T., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,701
Investigating soil moisture–climate interactions in a changing climate: A review
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010) · Earth-Science Reviews · doi:10.1016/j.earscirev.2010.02.004 · Cited by 5,638
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., et al. (2005) · Global Change Biology · doi:10.1111/j.1365-2486.2005.001002.x · Cited by 3,810
Plant responses to rising vapor pressure deficit
Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B., Siegwolf, R. T. W., Sperry, J. S., & McDowell, N. G. (2020) · New Phytologist · doi:10.1111/nph.16485 · Cited by 2,302
Climate extremes and the carbon cycle
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M. D., Seneviratne, S. I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D. C., et al. (2013) · Nature · doi:10.1038/nature12350 · Cited by 2,010
Self-citationIncreased atmospheric vapor pressure deficit reduces global vegetation growth
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., Ryu, Y., Chen, G., Dong, W., Hu, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aax1396 · Cited by 1,762
Self-citationThe FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-0534-3 · Cited by 1,728
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., et al. (2006) · Biogeosciences · doi:10.5194/bg-3-571-2006 · Cited by 1,590
The increasing importance of atmospheric demand for ecosystem water and carbon fluxes
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate3114 · Cited by 1,508
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Metadata
- Authors
- Fu, Z., Ciais, P., Prentice, I. C., Gentine, P., Makowski, D., Bastos, A., Luo, X., Green, J. K., Stoy, P. C., Yang, H., & Hajima, T.
- Venue
- Nature Communications
- Published
- 2022-02-21
- Volume
- 13
- Issue
- 1
- Pages
- 989-989
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4213274904
- Cited by
- 409
- References
- 60
- FWCI
- 30.88
- Type
- journal article
- Language
- en
- Open access
- gold
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