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Discussion papers
https://doi.org/10.5194/essd-2019-18
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-18
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 27 Mar 2019

Submitted as: data description paper | 27 Mar 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Earth System Science Data (ESSD).

Global variability of belowground autotrophic respiration in terrestrial ecosystems

Xiaolu Tang1,2, Shaohui Fan3, Wenjie Zhang4,5, Sicong Gao5, Guo Chen1, and Leilei Shi6 Xiaolu Tang et al.
  • 1College of Earth Science, Chengdu University of Technology, Chengdu, P. R. China
  • 2State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, P. R. China
  • 3Key laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing, P. R. China
  • 4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, P. R. China
  • 5School of Life Science, University of Technology Sydney, NSW, Australia
  • 6Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Jinming Avenue, Kaifeng, P. R. China

Abstract. Belowground autotrophic respiration (RA) is one of the largest, but highly uncertain carbon flux components in terrestrial ecosystems. It has not been explored globally before and still acted as a “black box” in global carbon cycling. Such progress and uncertainty motivate a development of global RA dataset and understand its spatial and temporal pattern, causes and responses to future climate change. This study used Random Forest to study RA's spatial and temporal pattern at the global scale by linking the updated field observations from Global Soil Respiration Database (v4) with global grid temperature, precipitation and other environmental variables. Globally, mean RA was 43.8 ± 0.4 Pg C a−1 with a temporally increasing trend of 0.025 ± 0.006 Pg C a−1 over 1980–2012. Such increment trend was widely spread with 58 % global land areas. For each 1 °C increase in annual mean temperature, global RA increased by 0.85 ± 0.13 Pg C a−1, and it was 0.17 ± 0.03 Pg C a−1 for 10 mm increase in annual mean precipitation, indicating a positive feedback of RA to future climate change. At a global scale, precipitation was the main dominant climatic drivers of the spatial pattern of RA, accounting for 56 % of global land areas with widely spread globally, particularly in dry or semi-arid areas, followed by shortwave radiation (25 %) and temperature (19 %). Different temporal patterns for varying climate zones and biomes indicated uneven response of RA to future climate change, challenging the perspective that the parameters of global carbon stimulation independent on climate zones and biomes. The developed RA database, the missing carbon flux component that is not constrained and validated in terrestrial ecosystem models and earth system models, will provide insights into understanding mechanisms underlying the spatial and temporal variability of belowground carbon dynamics. RA database also has great potentials to serve as a benchmark for future data-model comparisons. The RA product is freely available at https://doi.org/10.6084/m9.figshare.7636193.

Xiaolu Tang et al.
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Short summary
We estimated the missing global carbon flux component – global belowground autotrophic respiration (RA), using global published observations with the linkage of global variables with random forest. Globally, annual mean RA was 43.8 ± 0.4 Pg C a−1 with a temporally increasing trend of 0.025 ± 0.006 Pg C a−1 over 1980–2012. Precipitation dominated 56 % of global areas of the spatial pattern of RA. RA database also has great potentials to serve as a benchmark for future data-model comparisons.
We estimated the missing global carbon flux component – global belowground autotrophic...
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