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Partitioning global land evapotranspiration using CMIP5 models constrained by observations

Author

Listed:
  • Xu Lian

    (Peking University)

  • Shilong Piao

    (Peking University
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Chris Huntingford

    (Centre for Ecology and Hydrology)

  • Yue Li

    (Peking University)

  • Zhenzhong Zeng

    (Peking University)

  • Xuhui Wang

    (Peking University)

  • Philippe Ciais

    (Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ)

  • Tim R. McVicar

    (CSIRO Land and Water
    Australian Research Council Centre of Excellence for Climate System Science)

  • Shushi Peng

    (Peking University)

  • Catherine Ottlé

    (Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ)

  • Hui Yang

    (Peking University)

  • Yuting Yang

    (Tsinghua University)

  • Yongqiang Zhang

    (CSIRO Land and Water)

  • Tao Wang

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.

Suggested Citation

  • Xu Lian & Shilong Piao & Chris Huntingford & Yue Li & Zhenzhong Zeng & Xuhui Wang & Philippe Ciais & Tim R. McVicar & Shushi Peng & Catherine Ottlé & Hui Yang & Yuting Yang & Yongqiang Zhang & Tao Wan, 2018. "Partitioning global land evapotranspiration using CMIP5 models constrained by observations," Nature Climate Change, Nature, vol. 8(7), pages 640-646, July.
  • Handle: RePEc:nat:natcli:v:8:y:2018:i:7:d:10.1038_s41558-018-0207-9
    DOI: 10.1038/s41558-018-0207-9
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    Citations

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    Cited by:

    1. Yan Yu & Jiafu Mao & Stan D. Wullschleger & Anping Chen & Xiaoying Shi & Yaoping Wang & Forrest M. Hoffman & Yulong Zhang & Eric Pierce, 2022. "Machine learning–based observation-constrained projections reveal elevated global socioeconomic risks from wildfire," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Wu, Genan & Lu, Xinchen & Zhao, Wei & Cao, Ruochen & Xie, Wenqi & Wang, Liyun & Wang, Qiuhong & Song, Jiexuan & Gao, Shaobo & Li, Shenggong & Hu, Zhongmin, 2023. "The increasing contribution of greening to the terrestrial evapotranspiration in China," Ecological Modelling, Elsevier, vol. 477(C).
    3. Xu Lian & Sujong Jeong & Chang-Eui Park & Hao Xu & Laurent Z. X. Li & Tao Wang & Pierre Gentine & Josep Peñuelas & Shilong Piao, 2022. "Biophysical impacts of northern vegetation changes on seasonal warming patterns," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Xu Lian & Wenli Zhao & Pierre Gentine, 2022. "Recent global decline in rainfall interception loss due to altered rainfall regimes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Zhu, Shihua & Fang, Xia & Cao, Liangzhong & Hang, Xin & Xie, Xiaoping & Sun, Liangxiao & Li, Yachun, 2023. "Multivariate drives and their interactive effects on the ratio of transpiration to evapotranspiration over Central Asia ecosystems," Ecological Modelling, Elsevier, vol. 478(C).
    6. Zhao, Haigen & Ma, Yanfei, 2021. "Effects of various driving factors on potential evapotranspiration trends over the main grain-production area of China while accounting for vegetation dynamics," Agricultural Water Management, Elsevier, vol. 250(C).
    7. Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
    8. Yu, Haichao & Li, Sien & Ding, Jie & Yang, Tianyi & Wang, Yuexin, 2023. "Water use efficiency and its drivers of two typical cash crops in an arid area of Northwest China," Agricultural Water Management, Elsevier, vol. 287(C).
    9. Cui, Ningbo & He, Ziling & Jiang, Shouzheng & Wang, Mingjun & Yu, Xiuyun & Zhao, Lu & Qiu, Rangjian & Gong, Daozhi & Wang, Yaosheng & Feng, Yu, 2023. "Inter-comparison of the Penman-Monteith type model in modeling the evapotranspiration and its components in an orchard plantation of Southwest China," Agricultural Water Management, Elsevier, vol. 289(C).
    10. Amin, M.G. Mostofa & Mahbub, S.M. Mubtasim & Hasan, Md. Moudud & Pervin, Wafa & Sharmin, Jinat & Hossain, Md. Delwar, 2023. "Plant–water relations in subtropical maize fields under mulching and organic fertilization," Agricultural Water Management, Elsevier, vol. 286(C).

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