资源环境科技发展态势分析平台

Quantifying global soil respiration (R-SG) 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 R-SG range from 68 to 98PgCyear(-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 R-SG, we developed R-SG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (R-S) and climate data to predict R-SG. From the resulting R-SG estimates (range=66.62-100.72Pg), we calculated variability associated with each assumption. Among model formats, using monthly R-S data rather than annual data decreased R-SG by 7.43-9.46Pg; however, R-SG calculated from daily R-S data was only 1.83Pg lower than the R-SG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36Pg C differences, respectively. If the timescale of R-S data is constant, R-SG estimated by the first-order exponential (93.2Pg) was greater than the Random Forest (78.76Pg) or second-order exponential (76.18Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to R-SG. The results indicated R-SG is lower than in recent papers and the current benchmark for land models (98PgCyear(-1)), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.