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Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models

Lu, Haibo ; Li, Shihua ; Ma, Minna ; Bastrikov, Vladislav ; Chen, Xiuzhi ; Ciais, Philippe ; Dai, Yongjiu ; Ito, Akihiko ; Ju, Weimin and Lienert, Sebastian , et al. (2021) In Environmental Research Letters 16(5).
Abstract

The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared... (More)

The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr-1 for SR, 50.3 ± 25.0 (SD) Pg C yr-1 for HR and 35.2 Pg C yr-1 for AR during 1982-2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr-1 and 39.8 to 61.7 Pg C yr-1, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0-42.3 Pg C yr-1) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
benchmark, carbon cycling, global soil respiration, machine learning, terrestrial ecosystem models
in
Environmental Research Letters
volume
16
issue
5
article number
054048
publisher
IOP Publishing
external identifiers
  • scopus:85105747256
ISSN
1748-9318
DOI
10.1088/1748-9326/abf526
language
English
LU publication?
yes
id
4910fc2c-cccc-414f-b7c9-aad29460c4e7
date added to LUP
2021-06-09 15:16:28
date last changed
2023-03-07 23:01:17
@article{4910fc2c-cccc-414f-b7c9-aad29460c4e7,
  abstract     = {{<p>The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr-1 for SR, 50.3 ± 25.0 (SD) Pg C yr-1 for HR and 35.2 Pg C yr-1 for AR during 1982-2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr-1 and 39.8 to 61.7 Pg C yr-1, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0-42.3 Pg C yr-1) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.</p>}},
  author       = {{Lu, Haibo and Li, Shihua and Ma, Minna and Bastrikov, Vladislav and Chen, Xiuzhi and Ciais, Philippe and Dai, Yongjiu and Ito, Akihiko and Ju, Weimin and Lienert, Sebastian and Lombardozzi, Danica and Lu, Xingjie and Maignan, Fabienne and Nakhavali, Mahdi and Quine, Timothy and Schindlbacher, Andreas and Wang, Jun and Wang, Yingping and W rlind, David and Zhang, Shupeng and Yuan, Wenping}},
  issn         = {{1748-9318}},
  keywords     = {{benchmark; carbon cycling; global soil respiration; machine learning; terrestrial ecosystem models}},
  language     = {{eng}},
  number       = {{5}},
  publisher    = {{IOP Publishing}},
  series       = {{Environmental Research Letters}},
  title        = {{Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models}},
  url          = {{http://dx.doi.org/10.1088/1748-9326/abf526}},
  doi          = {{10.1088/1748-9326/abf526}},
  volume       = {{16}},
  year         = {{2021}},
}