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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/acp-2020-1088
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-1088
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Oct 2020

30 Oct 2020

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This preprint is currently under review for the journal ACP.

Aboveground biomass in Inner Mongolian temperate grasslands decreases under climate warming

Guocheng Wang1, Zhongkui Luo2, Yao Huang3, Wenjuan Sun3, Yurong Wei4, Xi Deng5, Jinhuan Zhu6, and Wen Zhang1 Guocheng Wang et al.
  • 1LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
  • 2College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
  • 3State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
  • 4Inner Mongolia Ecology and Agrometeorology Centre, Hohhot, Inner Mongolia 100051, China
  • 5School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai 519000, China
  • 6LAOR, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

Abstract. Grassland aboveground biomass (AGB) is a critical component of the global carbon cycle and reflects ecosystem productivity. Although it is widely acknowledged that dynamics of grassland biomass are significantly regulated by climate change, in situ evidence at large spatiotemporal scales is limited. Here, we combine biomass measurements from six long-term (> 30 years) experiments and data in existing literatures to explore the spatiotemporal changes in AGB in Inner Mongolian temperate grasslands. We show that, on average, annual AGB over the past four decades is 2,561 ka ha−1, 1,496 kg ha−1 and 835 kg ha−1, respectively, in meadow steppe, typical steppe and desert steppe in Inner Mongolia. The spatiotemporal changes of AGB are regulated by interactions of climatic attributes, edaphic properties, grazing intensity and grassland type. Using a machine learning-based approach, we map annual AGB (from 1981 to 2100) across the Inner Mongolian grassland at the spatial resolution of 1 km. We find that on the regional scale, meadow steppe has the highest annual AGB, followed by typical and desert steppe. During 1981–2019, the average annual AGB generally exhibited a declining trend across all the three types of grassland. Under future climate warming, AGB in the study region could continue to decrease. On average, compared with the historical AGB (i.e., average of 1981–2019), the AGB at the end of this century (i.e., average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5, respectively. The decreases in AGB under warming show large disparities across different grassland types and future climate change scenarios. Our results demonstrate the accuracy of predictions in AGB using a machine learning-based approach driven by several readily obtainable environmental variables; and provide new data at large scale and fine resolution extrapolated from field measurements.

Guocheng Wang et al.

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Data S1 Guocheng Wang et al. https://doi.org/10.6084/m9.figshare.13108430

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Data S1 Guocheng Wang et al. https://doi.org/10.6084/m9.figshare.13108430

Guocheng Wang et al.

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Short summary
We combine aboveground biomass (AGB) measurements from six long-term experiments and data synthesized from literatures to explore spatiotemporal changes in AGB in Inner Mongolian grasslands. By adopting a machine learning-based approach, we map annual AGB at the spatial resolution of 1 km and find an overall decline in AGB over the past four decades. Under the ongoing temperature rising, AGB will decrease by 14 %–28 % at the end of this century, compared with the average AGB during 1981–2019.
We combine aboveground biomass (AGB) measurements from six long-term experiments and data...
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