Global pattern of temperature sensitivity of soil heterotrophic respiration (Q10) and its implications for carbon‐climate feedback

Temperature sensitivity of soil respiration (Q 10 ) is an important parameter in modeling effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q 10 has high spatial heterogeneity. However, most biogeochemical models still use a globally constant Q 10 in projecting future climate change, partly because no spatial pattern of Q 10 values has been derived. In this study, we conducted an inverse analysis to retrieve a global pattern of spatially heterogeneous Q 10 values by assimilating data of soil organic carbon into a process‐based terrestrial carbon model (Carnegie‐Ames‐Stanford Approach model) at spatial resolution of 1° by 1°. The estimated Q 10 values were, in turn, incorporated into soil respiration models to evaluate their impacts on global respiratory carbon release from soil (i.e., total soil respiration is equal to microbial and root respiration) and from microbial decomposition (i.e., heterotrophic respiration). Our results show that the optimized Q 10 values are spatially heterogeneous and vary with environmental factors. In general, Q 10 value tends to be high in the high‐latitudinal regions. The mean Q 10 values for different biomes range from 1.43 to 2.03, with the highest value in tundra and the lowest value in deserts. When spatially heterogeneous Q 10 values were incorporated into global soil respiration models, simulated soil respiration has a feedback intensity of 3.21 Pg C °C −1 to climate warming, which is approximately 40% higher than that with a globally invariant Q 10 value. The modeled heterotrophic respiration has a feedback intensity of 2.26 Pg C °C −1 , about 25% higher than that derived from a globally invariant Q 10 value. Overall, the feedback intensity of soil carbon release to climate warming depends not only on the magnitude of a global mean of Q 10 values but also their spatial variability.