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 greater than the Random Forest (78.76 Pg) or second-order exponential (76.18 Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to RSG. The results indicated RSG is lower than in recent papers and the current benchmark for land models (98 Pg C year-1 ), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.
Keywords: benchmark; modeling; random forest; soil carbon cycle; soil respiration; timescale; upscaling; variability.
© 2018 John Wiley & Sons Ltd.