Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models
The CO _2 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 CO _2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrop...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/abf526 |
| _version_ | 1797747748590583808 |
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| author | Haibo Lu Shihua Li Minna Ma Vladislav Bastrikov Xiuzhi Chen Philippe Ciais Yongjiu Dai Akihiko Ito Weimin Ju Sebastian Lienert Danica Lombardozzi Xingjie Lu Fabienne Maignan Mahdi Nakhavali Timothy Quine Andreas Schindlbacher Jun Wang Yingping Wang David Wårlind Shupeng Zhang Wenping Yuan |
| author_facet | Haibo Lu Shihua Li Minna Ma Vladislav Bastrikov Xiuzhi Chen Philippe Ciais Yongjiu Dai Akihiko Ito Weimin Ju Sebastian Lienert Danica Lombardozzi Xingjie Lu Fabienne Maignan Mahdi Nakhavali Timothy Quine Andreas Schindlbacher Jun Wang Yingping Wang David Wårlind Shupeng Zhang Wenping Yuan |
| author_sort | Haibo Lu |
| collection | DOAJ |
| description | The CO _2 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 CO _2 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. |
| first_indexed | 2024-03-12T15:55:04Z |
| format | Article |
| id | doaj.art-bda83b9da2694598a2ba9141b7570c18 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:55:04Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-bda83b9da2694598a2ba9141b7570c182023-08-09T14:57:24ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-0116505404810.1088/1748-9326/abf526Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem modelsHaibo Lu0https://orcid.org/0000-0002-2461-4870Shihua Li1https://orcid.org/0000-0001-6796-0856Minna Ma2Vladislav Bastrikov3Xiuzhi Chen4Philippe Ciais5https://orcid.org/0000-0001-8560-4943Yongjiu Dai6Akihiko Ito7https://orcid.org/0000-0001-5265-0791Weimin Ju8Sebastian Lienert9Danica Lombardozzi10https://orcid.org/0000-0003-3557-7929Xingjie Lu11Fabienne Maignan12https://orcid.org/0000-0001-5024-5928Mahdi Nakhavali13Timothy Quine14Andreas Schindlbacher15Jun Wang16https://orcid.org/0000-0001-7359-1647Yingping Wang17David Wårlind18Shupeng Zhang19Wenping Yuan20School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaLaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ , 91191 Gif Sur Yvette, FranceSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaLaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ , 91191 Gif Sur Yvette, FranceSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaNational Institute for Environmental Studies , Tsukuba, Ibaraki 305-8506, JapanJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University , Nanjing 210023, People’s Republic of ChinaClimate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern , Bern, SwitzerlandClimate and Global Dynamics Laboratory, National Center for Atmospheric Research , Boulder, CO, United States of AmericaSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaLaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ , 91191 Gif Sur Yvette, FranceCollege of Engineering, Mathematics and Physical Sciences, University of Exeter , EX4 4QE Exeter, United KingdomDepartment of Geography, College of Life and Environmental Sciences, University of Exeter , EX4 4RJ Exeter, United KingdomDepartment of Forest Ecology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape-BFW , A-1131 Vienna, AustriaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University , Nanjing 210023, People’s Republic of China; Department of Atmospheric and Oceanic Science, University of Maryland, College Park , MD 20742, United States of AmericaCSIRO, Oceans and Atmosphere, Private Bag 1 , Aspendale, Victoria 3195, Australia; South China Botanical Garden, Chinese Academy of Sciences , Guangzhou 510650, People’s Republic of ChinaDepartment of Physical Geography and Ecosystem Science, Lund University , Lund, SwedenSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaSchool of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University , Zhuhai, Guangdong 519082, People’s Republic of ChinaThe CO _2 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 CO _2 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.https://doi.org/10.1088/1748-9326/abf526benchmarkcarbon cyclingglobal soil respirationmachine learningterrestrial ecosystem models |
| spellingShingle | Haibo Lu Shihua Li Minna Ma Vladislav Bastrikov Xiuzhi Chen Philippe Ciais Yongjiu Dai Akihiko Ito Weimin Ju Sebastian Lienert Danica Lombardozzi Xingjie Lu Fabienne Maignan Mahdi Nakhavali Timothy Quine Andreas Schindlbacher Jun Wang Yingping Wang David Wårlind Shupeng Zhang Wenping Yuan Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models Environmental Research Letters benchmark carbon cycling global soil respiration machine learning terrestrial ecosystem models |
| title | Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| title_full | Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| title_fullStr | Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| title_full_unstemmed | Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| title_short | Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| title_sort | comparing machine learning derived global estimates of soil respiration and its components with those from terrestrial ecosystem models |
| topic | benchmark carbon cycling global soil respiration machine learning terrestrial ecosystem models |
| url | https://doi.org/10.1088/1748-9326/abf526 |
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