BackgroundSoil microbiomes play an important role in the services and functioning of terrestrial ecosystems. However, little is known of their vertical responses to restoration process and their contributions to soil nutrient cycling in the subsurface profiles. Here, we investigated the community assembly of soil bacteria, archaea, and fungi along vertical (i.e., soil depths of 0–300 cm) and horizontal (i.e., distance from trees of 30–90 cm) profiles in a chronosequence of reforestation sites that represent over 30 years of restoration.ResultsIn the superficial layers (0–80 cm), bacterial and fungal diversity decreased, whereas archaeal diversity increased with increasing soil depth. As reforestation proceeded over time, the vertical spatial variation in bacterial communities decreased, while that in archaeal and fungal communities increased. Vertical distributions of the soil microbiomes were more related to the variation in soil properties, while their horizontal distributions may be driven by a gradient effect of roots extending from the tree. Bacterial and archaeal beta-diversity were strongly related to multi-nutrient cycling in the soil, respectively, playing major roles in deep and superficial layers.ConclusionsTaken together, these results reveal a new perspective on the vertical and horizontal spatial variation in soil microbiomes at the fine scale of single trees. Distinct response patterns underpinned the contributions of soil bacteria, archaea, and fungi as a function of subsurface nutrient cycling during the reforestation of ex-arable land.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0526-0) contains supplementary material, which is available to authorized users.

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[1] The paper examines terrestrial and oceanic carbon budgets from preindustrial time to present day in the version of Beijing Climate Center Climate System Model (BCC_CSM1.1) which is a global fully coupled climate-carbon cycle model. Atmospheric CO 2 concentration is calculated from a prognostic equation taking into account global anthropogenic CO 2 emissions and the interactive CO 2 exchanges of land-atmosphere and ocean-atmosphere. When forced by prescribed historical emissions of CO 2 from combustion of fossil fuels and land use change, BCC_CSM1.1 can reproduce the trends of observed atmospheric CO 2 concentration and global surface air temperature from 1850 to 2005. Simulated interannual variability and long-term trend of global carbon sources and sinks and their spatial patterns generally agree with other model estimates and observations, which shows the following: (1) Both land and ocean in the last century act as net carbon sinks. The ability of carbon uptake by land and ocean is enhanced at the end of last century. (2) Interannual variability of the global atmospheric CO 2 concentration is closely correlated with the El Niño-Southern Oscillation cycle, in agreement with observations. (3) Interannual variation of the land-to-atmosphere net carbon flux is positively correlated with surface air temperature while negatively correlated with soil moisture over low and midlatitudes. The relative contribution of soil moisture to the interannual variation of land-atmosphere CO 2 exchange is more important than that of air temperature over tropical regions, while surface air temperature is more important than soil moisture over other regions of the globe.Citation: Wu, T., et al. (2013), Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century,

influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21-22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere-western Indian Ocean area and across the eastern Indian Ocean-Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the wellknown Maritime Continent predictability barrier.

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts.Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.