Estimates of soil heterotrophic respiration in global terrestrial ecosystems

Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of terrestrial carbon cycle, which reflects the carbon loss from soils to the atmosphere due to microbial decomposition of soil organic carbon and litter debris. However, RH estimates vary greatly using different approaches, requiring RH estimates from different angles. Therefore, in current study, we first modelled RH from 1980 to 2016 using a Random Forest (RF) algorithm with the linkage of field observations from the Global Soil Respiration Database and global environmental drivers at 0.5 degree; second, we analyzed the spati-temporal patterns of RH; and third, we compared RF-driven RH with estimates from Global Dynamic Vegetation Model (GDVM) and Data-Model Integration approaches. Results showed that RF could satisfactorily capture the spati-temporal patterns of RH with a model efficiency of 50% and root mean square error of 143 g C m^-2 a^-1. RF-driven RH showed a large spatial variability and decreased with the increasing latitude. Total RF-driven RH was 57 Pg C a^-1 (1 Pg = 10¹⁵ g) with an average increasing trend of 0.036 Pg C a^-2 from 1980 to 2016 (p < 0.001). However, the temporal trend of RH varied with climatic zones that RH increased in boreal and temperate areas, while no temporal trend in tropical regions. RH from seven GDVMs changed from 34.8 Pg C a^-1 for ISAM model to 59.9 Pg C a^-1 with an average of 47.6 Pg C a^-1, underestimating RH by 9.6 Pg C a^-1 (16%) in comparison to RF-driven RH. Furthermore, RH estimates from data-driven approach of Hashimoto et al. (2015) and Yao et al. (2021) were 51 and 47 Pg C a^-1, which were lower than our estimate. Such difference was mainly attributed to different modelling algorithms and observational datasets. Therefore, given the potential uncertainties remaining in RH products, new approaches, e.g. deep learning or better representation of soil carbon cycling processes in GDVMs, are encouraged.  

*This study was supported by the National Natural Science Foundation of China (32271856).