Understanding the temperature sensitivity (Q₁₀) of soil organic matter (SOM) decomposition is important for predicting soil carbon (C) sequestration in terrestrial ecosystems under warming scenarios. Whether Q₁₀ varies predictably with ecosystem succession and the ways in which the stoichiometry of input SOM influences Q₁₀ remain largely unknown. We investigate these issues using a grassland succession series from free‐grazing to 31‐year grazing‐exclusion grasslands in Inner Mongolia, and an incubation experiment performed at six temperatures (0, 5, 10, 15, 20, and 25°C) and with four substrates: control (CK), glucose (GLU), mixed grass leaf (GRA), and Medicago falcata leaf (MED). The results showed that basal soil respiration (20°C) and microbial biomass C (MBC) logarithmically decreased with grassland succession. Q₁₀ decreased logarithmically from 1.43 in free‐grazing grasslands to 1.22 in 31‐year grazing‐exclusion grasslands. Q₁₀ increased significantly with the addition of substrates, and the Q₁₀ levels increased with increase in N:C ratios of substrate. Moreover, accumulated C mineralization was controlled by the N:C ratio of newly input SOM and by incubation temperature. Changes in Q₁₀ with grassland ecosystem succession are controlled by the stoichiometry of newly input SOM, MBC, and SOM quality, and the combined effects of which could partially explain the mechanisms underlying soil C sequestration in the long‐term grazing‐exclusion grasslands in Inner Mongolia, China. The findings highlight the effect of substrate stoichiometry on Q₁₀ which requires further study.

The high temporal resolution measurements of δD, δ¹⁸O, and deuterium excess (d) of atmospheric water vapor provide an improved understanding of atmospheric and ecohydrological processes at ecosystem to global scales. In this study, δD, δ¹⁸O, and d of water vapor and their flux ratios were continuously measured from May to September 2012 using an in situ technique above an arid artificial oasis in the Heihe River Basin, which has a typical continental arid climate. The monthly δD and δ¹⁸O increased slowly and then decreased, whereas the monthly d showed a steady decrease. δD, δ¹⁸O, and d exhibited a marked diurnal cycle, indicating the influence of the entrainment, local evapotranspiration (ET), and dewfall. The departures of δD, δ¹⁸O, and d from equilibrium prediction were significantly correlated with rain amount, relative humidity (RH), and air temperature (T). The “amount effect” was observed during one precipitation event. δD and δ¹⁸O were log linear dependent on water vapor mixing ratio with respective R² of 17% and 14%, whereas d was significantly correlated with local RH and T, suggesting the less influence of air mass advection and more important contribution of the local source of moisture to atmospheric water vapor. Throughout the experiment, the local ET acted to increase δD and δ¹⁸O, with isofluxes of 102.5 and 23.50 mmol m⁻² s⁻¹‰, respectively. However, the dominated effect of entrainment still decreased δD and δ¹⁸O by 10.1 and 2.24‰, respectively. Both of the local ET and entrainment exerted a positive forcing on the diurnal variability in d.

Isotope ratio infrared spectroscopy (IRIS) provides an in situ technique for measuring δ¹³C in atmospheric CO₂. A number of methods have been proposed for calibrating the IRIS measurements, but few studies have systematically evaluated their accuracy for atmospheric applications. In this study, we carried out laboratory and ambient measurements with two commercial IRIS analyzers and compared the accuracy of four calibration strategies. We found that calibration based on the ¹²C and ¹³C mixing ratios (Bowling et al., 2003) and on linear interpolation of the measured delta using the mixing ratio of the major isotopologue (Lee et al., 2005) yielded accuracy better than 0.06‰. Over a 7-day atmospheric measurement in Beijing, the two analyzers agreed to within −0.02 ± 0.18‰ after proper calibration. However, even after calibration the difference between the two analyzers showed a slight correlation with concentration, and this concentration dependence propagated through the Keeling analysis, resulting in a much larger difference of 2.44‰ for the Keeling intercept. The high sensitivity of the Keeling analysis to the concentration dependence underscores the challenge of IRIS for atmospheric research.