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Comparing machine l...
Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models
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- Lu, Haibo (author)
- Sun Yat-sen University
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- Li, Shihua (author)
- Sun Yat-sen University
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- Ma, Minna (author)
- Sun Yat-sen University
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- Bastrikov, Vladislav (author)
- French Alternative Energies and Atomic Energy Commission (CEA)
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- Chen, Xiuzhi (author)
- Sun Yat-sen University
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- Ciais, Philippe (author)
- French Alternative Energies and Atomic Energy Commission (CEA)
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- Dai, Yongjiu (author)
- Sun Yat-sen University
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- Ito, Akihiko (author)
- National Institute for Environmental Studies of Japan
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- Ju, Weimin (author)
- Nanjing University
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- Lienert, Sebastian (author)
- University of Bern
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- Lombardozzi, Danica (author)
- National Center for Atmospheric Research
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- Lu, Xingjie (author)
- Sun Yat-sen University
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- Maignan, Fabienne (author)
- French Alternative Energies and Atomic Energy Commission (CEA)
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- Nakhavali, Mahdi (author)
- University of Exeter
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- Quine, Timothy (author)
- University of Exeter
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- Schindlbacher, Andreas (author)
- Federal Research And Training Centre For Forests, Natural Hazards And Landscape
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- Wang, Jun (author)
- University of Maryland,Nanjing University
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- Wang, Yingping (author)
- CSIRO Oceans and Atmosphere, Canberra,University of the Chinese Academy of Sciences
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- W rlind, David (author)
- Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science
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- Zhang, Shupeng (author)
- Sun Yat-sen University
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- Yuan, Wenping (author)
- Sun Yat-sen University
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(creator_code:org_t)
- 2021-05-05
- 2021
- English.
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In: Environmental Research Letters. - : IOP Publishing. - 1748-9318 .- 1748-9326. ; 16:5
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
Keyword
- benchmark
- carbon cycling
- global soil respiration
- machine learning
- terrestrial ecosystem models
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Lu, Haibo
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Li, Shihua
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Ma, Minna
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Bastrikov, Vladi ...
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Chen, Xiuzhi
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Ciais, Philippe
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show more...
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Dai, Yongjiu
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Ito, Akihiko
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Ju, Weimin
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Lienert, Sebasti ...
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Lombardozzi, Dan ...
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Lu, Xingjie
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Maignan, Fabienn ...
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Nakhavali, Mahdi
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Quine, Timothy
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Schindlbacher, A ...
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Wang, Jun
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Wang, Yingping
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W rlind, David
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Zhang, Shupeng
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Yuan, Wenping
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show less...
- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Earth and Relate ...
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and Physical Geograp ...
- Articles in the publication
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Environmental Re ...
- By the university
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Lund University