Constraining estimates of global soil respiration by quantifying sources of variability

Global Change Biology
Jinshi JianSteven C Hodges

Abstract

Quantifying global soil respiration (RSG ) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of RSG range from 68 to 98 Pg C year-1 , causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain RSG , we developed RSG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (RS ) and climate data to predict RSG . From the resulting RSG estimates (range = 66.62-100.72 Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43-9.46 Pg; however, RSG calculated from daily RS data was only 1.83 Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36 Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first-order exponential (93.2 Pg) was gr...Continue Reading

References

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Mar 26, 2010·Nature·Ben Bond-Lamberty, Allison Thomson
Feb 21, 2016·Global Change Biology·Lingyan ZhouShahla Hosseini Bai
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Nov 17, 2016·Proceedings of the National Academy of Sciences of the United States of America·Joanna C CareyAlbert Tietema
Jun 3, 2017·Journal of Environmental Management·Minaco AdachiWataru Takeuchi

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Citations

May 2, 2020·Scientific Reports·Na Lei, Jichang Han
Jan 17, 2021·Nature Communications·Jiesi LeiYunfeng Yang
Mar 29, 2021·The Science of the Total Environment·Emily M LacroixScott Fendorf
Aug 12, 2021·The Science of the Total Environment·Xiaolu TangJingji Li

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