Junjiong Shao

Interactive effects of global change factors on soil respiration and its components: a meta‐analysis

As the second largest carbon (C) flux between the atmosphere and terrestrial ecosystems, soil respiration (Rs) plays vital roles in regulating atmospheric CO2 concentration ([CO2 ]) and climatic dynamics in the earth system. Although numerous manipulative studies and a few meta-analyses have been conducted to determine the responses of Rs and its two components [i.e., autotrophic (Ra) and heterotrophic (Rh) respiration] to single global change factors, the interactive effects of the multiple factors are still unclear. In this study, we performed a meta-analysis of 150 multiple-factor (≥2) studies to examine the main and interactive effects of global change factors on Rs and its two components. Our results showed that elevated [CO2 ] (E), nitrogen addition (N), irrigation (I), and warming (W) induced significant increases in Rs by 28.6%, 8.8%, 9.7%, and 7.1%, respectively. The combined effects of the multiple factors, EN, EW, DE, IE, IN, IW, IEW, and DEW, were also significantly positive on Rs to a greater extent than those of the single-factor ones. For all the individual studies, the additive interactions were predominant on Rs (90.6%) and its components (≈70.0%) relative to synergistic and antagonistic ones. However, the different combinations of global change factors (e.g., EN, NW, EW, IW) indicated that the three types of interactions were all important, with two combinations for synergistic effects, two for antagonistic, and five for additive when at least eight independent experiments were considered. In addition, the interactions of elevated [CO2 ] and warming had opposite effects on Ra and Rh, suggesting that different processes may influence their responses to the multifactor interactions. Our study highlights the crucial importance of the interactive effects among the multiple factors on Rs and its components, which could inform regional and global models to assess the climate-biosphere feedbacks and improve predictions of the future states of the ecological and climate systems.

Effects of biochar application on soil greenhouse gas fluxes: a meta-analysis

Biochar application to soils may increase carbon (C) sequestration due to the inputs of recalcitrant organic C. However, the effects of biochar application on the soil greenhouse gas (GHG) fluxes appear variable among many case studies; therefore, the efficacy of biochar as a carbon sequestration agent for climate change mitigation remains uncertain. We performed a meta‐analysis of 91 published papers with 552 paired comparisons to obtain a central tendency of three main GHG fluxes (i.e., CO2, CH4, and N2O) in response to biochar application. Our results showed that biochar application significantly increased soil CO2 fluxes by 22.14%, but decreased N2O fluxes by 30.92% and did not affect CH4 fluxes. As a consequence, biochar application may significantly contribute to an increased global warming potential (GWP) of total soil GHG fluxes due to the large stimulation of CO2 fluxes. However, soil CO2 fluxes were suppressed when biochar was added to fertilized soils, indicating that biochar application is unlikely to stimulate CO2 fluxes in the agriculture sector, in which N fertilizer inputs are common. Responses of soil GHG fluxes mainly varied with biochar feedstock source and soil texture and the pyrolysis temperature of biochar. Soil and biochar pH, biochar applied rate, and latitude also influence soil GHG fluxes, but to a more limited extent. Our findings provide a scientific basis for developing more rational strategies toward widespread adoption of biochar as a soil amendment for climate change mitigation.

Direct and indirect effects of climatic variations on the interannual variability in net ecosystem exchange across terrestrial ecosystems

Climatic variables not only directly affect the interannual variability (IAV) in net ecosystem exchange of CO2 (NEE) but also indirectly drive it by changing the physiological parameters. Identifying these direct and indirect paths can reveal the underlying mechanisms of carbon (C) dynamics. In this study, we applied a path analysis using flux data from 65 sites to quantify the direct and indirect climatic effects on IAV in NEE and to evaluate the potential relationships among the climatic variables and physiological parameters that represent physiology and phenology of ecosystems. We found that the maximum photosynthetic rate was the most important factor for the IAV in gross primary productivity (GPP), which was mainly induced by the variation in vapour pressure deficit. For ecosystem respiration (RE), the most important drivers were GPP and the reference respiratory rate. The biome type regulated the direct and indirect paths, with distinctive differences between forests and non-forests, evergreen needleleaf forests and deciduous broadleaf forests, and between grasslands and croplands. Different paths were also found among wet, moist and dry ecosystems. However, the climatic variables can only partly explain the IAV in physiological parameters, suggesting that the latter may also result from other biotic and disturbance factors. In addition, the climatic variables related to NEE were not necessarily the same as those related to GPP and RE, indicating the emerging difficulty encountered when studying the IAV in NEE. Overall, our results highlight the contribution of certain physiological parameters to the IAV in C fluxes and the importance of biome type and multi-year water conditions, which should receive more attention in future experimental and modelling research.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr−1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr−1 during 1981–2000. In addition, high uncertainty in Chinas NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion

Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO-C-C (carbon (C)-only model), TECO-CN-C (TECO-CN coupled model with only C measurements as assimilating data), and TECO-CN-CN (TECO-CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long-term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long-term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process-induced disequilibrium in the initial value of C pools. However, in the long-term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C-N coupling in ecosystem models and highlight critical observational needs for future studies.

Drought-induced changes in root biomass largely result from altered root morphological traits: Evidence from a synthesis of global field trials: Effects of drought on root traits

Extreme drought is likely to become more frequent and intense as a result of global climate change, which may significantly impact plant root traits and responses (i.e., morphology, production, turnover, and biomass). However, a comprehensive understanding of how drought affects root traits and responses remains elusive. Here, we synthesized data from 128 published studies under field conditions to examine the responses of 17 variables associated with root traits to drought. Our results showed that drought significantly decreased root length and root length density by 38.29% and 11.12%, respectively, but increased root diameter by 3.49%. However, drought significantly increased root:shoot mass ratio and root cortical aerenchyma by 13.54% and 90.7%, respectively. Our results suggest that drought significantly modified root morphological traits and increased root mortality, and the drought-induced decrease in root biomass was less than shoot biomass, causing higher root:shoot mass ratio. The cascading effects of drought on root traits and responses may need to be incorporated into terrestrial biosphere models to improve prediction of the climate-biosphere feedback.