The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections.

This study aimed to investigate the effects of wind turbulence on CO₂ transport within a medium and the extent of measurement errors in a closed chamber. Therefore, in a laboratory with controllable environmental conditions, the measurement performance of the closed chamber at various wind speeds was assessed using a soil respiration calibration apparatus and four types of porous media. The experimental results indicated that the closed chamber under the influence of wind turbulence exhibited varying degrees of underestimation, ranging from −51 to −6%. The effects of wind turbulence were more pronounced in sandy soils. As wind turbulence enhanced gas transport within the medium, the flux measurements of the closed chamber were biased, and this phenomenon was closely related to the medium’s particle size and surface wind speed. To address this issue, it is recommended to conduct long-term monitoring and eliminate errors by averaging repeated measurements, which will improve the accuracy of the ecosystem carbon budget.