干旱区生态环境知识资源中心(Arid): A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability

Arid

DOI	10.1029/2020GB006918
	A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability
	Yao, Yitong; Ciais, Philippe; Viovy, Nicolas; Li, Wei; Yang, Hui; Joetzjer, Emilie; Ben Bond-Lamberty
通讯作者	Yao, YT (corresponding author), Univ Paris Saclay, Lab Sci Climat & Environm, CEA CNRS UVSQ, LSCE IPSL, Gif Sur Yvette, France.
来源期刊	GLOBAL BIOGEOCHEMICAL CYCLES
ISSN	0886-6236
EISSN	1944-9224
出版年	2021
卷号	35 期号:8
英文摘要	Soil heterotrophic respiration (SHR) is important for carbon-climate feedbacks because of its sensitivity to soil carbon, climatic conditions and nutrient availability. However, available global SHR estimates have either a coarse spatial resolution or rely on simple upscaling formulations. To better quantify the global distribution of SHR and its response to climate variability, we produced a new global SHR data set using Random Forest, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity. We estimated a global total SHR of 46.838.656.3 Pg C yr(-1) over 1985-2013 with a significant increasing trend of 0.03 Pg C yr(-2). Among the inputs to generate SHR products, the choice of soil moisture datasets contributes more to the difference among SHR ensemble. Water availability dominates SHR inter-annual variability (IAV) at the global scale; more precisely, temperature strongly controls the SHR IAV in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning SHR ensemble of data-driven gridded estimates and outputs from process-based models (TRENDYv6) shows agreement for a strong association between water variability and SHR IAV at the global scale, but ensemble members exhibit different ecosystem-level SHR IAV controllers. The important role of water availability in driving SHR suggests both a direct effect limiting decomposition and an indirect effect on litter available from productivity. Considering potential uncertainties remaining in our data-driven SHR datasets, we call for more scientifically designed SHR observation network and deep-learning methods making maximum use of observation data.
英文关键词	soil heterotrophic respiration random forest inter-annual variability precipitation soil moisture
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000690773700005
WOS关键词	CARBON-DIOXIDE EMISSIONS ; LAND-SURFACE MODEL ; NITROGEN ADDITION ; AUTOTROPHIC RESPIRATION ; MICROBIAL RESPIRATION ; ECOSYSTEM RESPIRATION ; CLIMATE-CHANGE ; TEMPERATURE SENSITIVITY ; WHEAT YIELD ; FOREST
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
来源机构	清华大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363453
作者单位	[Yao, Yitong; Ciais, Philippe; Viovy, Nicolas; Yang, Hui] Univ Paris Saclay, Lab Sci Climat & Environm, CEA CNRS UVSQ, LSCE IPSL, Gif Sur Yvette, France; [Li, Wei] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China; [Joetzjer, Emilie] Univ Toulouse, Meteo France, CNRM, CNRS, Toulouse, France; [Ben Bond-Lamberty] Univ Maryland, Joint Global Change Res Inst, Pacific Northwest Natl Lab, College Pk, MD 20742 USA
推荐引用方式 GB/T 7714	Yao, Yitong,Ciais, Philippe,Viovy, Nicolas,et al. A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability[J]. 清华大学,2021,35(8).
APA	Yao, Yitong.,Ciais, Philippe.,Viovy, Nicolas.,Li, Wei.,Yang, Hui.,...&Ben Bond-Lamberty.(2021).A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability.GLOBAL BIOGEOCHEMICAL CYCLES,35(8).
MLA	Yao, Yitong,et al."A Data-Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter-Annual Variability".GLOBAL BIOGEOCHEMICAL CYCLES 35.8(2021).