*Partitioning soil respiration into autotrophic (R(A)) and heterotrophic (R(H)) components is critical for understanding their differential responses to climate warming. *Here, we used a deconvolution analysis to partition soil respiration in a pulse warming experiment. We first conducted a sensitivity analysis to determine which parameters can be identified by soil respiration data. A Markov chain Monte Carlo technique was then used to optimize those identifiable parameters in a terrestrial ecosystem model. Finally, the optimized parameters were employed to quantify R(A) and R(H) in a forward analysis. *Our results displayed that more than one-half of parameters were constrained by daily soil respiration data. The optimized model simulation showed that warming stimulated R(H) and had little effect on R(A) in the first 2 months, but decreased both R(H) and R(A) during the remainder of the treatment and post-treatment years. Clipping of above-ground biomass stimulated the warming effect on R(H) but not on R(A). Overall, warming decreased R(A) and R(H) significantly, by 28.9% and 24.9%, respectively, during the treatment year and by 27.3% and 33.3%, respectively, during the post-treatment year, largely as a result of decreased canopy greenness and biomass. *Lagged effects of climate anomalies on soil respiration and its components are important in assessing terrestrial carbon cycle feedbacks to climate warming.