T he impacts of climate change and anthropogenic activities on Earth's vegetation and ecosystems have been in the spotlight of science in the past decades [1][2][3][4][5][6] . With increasing climate variability and more frequent occurrences of extreme events expected in the future 7 , research has targeted the sensitivity of ecosystems 8,9 . At the same time, recent studies have shown globally increasing leaf area index (LAI; a proxy for green vegetation cover) 10 , and aboveground biomass carbon (ABC) 11 , also known as the greening Earth 10,12 . Dynamic vegetation models and Earth observation studies reveal climatic and atmospheric changes as the main drivers of large scale increases in LAI 10,13 . On the contrary, the anthropogenic footprint is usually found to cause land degradation and deforestation 13,14 , and only a few studies find a direct positive effect of management on vegetation cover and biomass trends 2,11,15 . Although ecological conservation projects aim at increasing biodiversity, carbon sequestration and vegetation cover 16,17 , the success of such conservation efforts is not easily quantifiable, and the spatial footprint of projects is not always commensurable with contemporary satellite-and modelling-based monitoring methods. Adaptation and mitigation strategies to climate change should be anchored in knowledge on how ecosystems respond to climatic and anthropogenic disturbances, but at present it is not known whether conservation projects impact on the ability of vegetation to alleviate the effects of climate change at large scales.China's ecological restoration projects (for example, the Natural Forest Protection Project, the Grain to Green Project, and the Karst Rocky Desertification Restoration Project) are considered 'megaengineering' activities and the most ambitious afforestation and conservation projects in human history [16][17][18][19] . The highly sensitive and vulnerable karst ecosystem in southwest China is one of the largest exposed carbonate rock areas (more than 0.54 million km 2 ) in the world. This area hosts 220 million people 20,21 and has been selected as a major target of restoration projects. Descriptions as early as the seventeenth century reported the rocky karst mountains as an area of sparse forest or vegetation cover 22 , and accelerating desertification has been reported during the past half century, caused by the increasing intensity of human exploitation of natural resources [21][22][23][24] . As a result, approximately 0.13 million km 2 of karst areas previously covered by vegetation and soil were turned into a rocky landscape. To combat this severe form of land degradation and to relieve poverty, more than 130 billion yuan (~19 billion USD) have been invested in mitigation initiatives since the end of the 1990s 24 . The largest programme implemented, the Grain to Green Project, offers grain, cash and free seedlings as compensation for rural households to re-establish forests, shrub and/or grassland 24 . The costs of ecological engineering projects as a clima...

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitation changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. We used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. This highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes.

Abstract. Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is timedependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times.Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a threedimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationPublished by Copernicus Publications on behalf of the European Geosciences Union. 146Y. Luo et al.: Land carbon storage dynamics ally feasible so that both C flux-and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

Crowther et al. 1 reported that the best predictor of surface soil carbon (C; top 10 cm) losses in response to warming is the size of the surface C stock in the soil (i.e. C stocks in unwarmed plots), with soils high in soil C also losing more C. This relationship was based on a linear regression of soil C losses and soil C stocks in field warming studies, which was then used to project C losses over time and to generate a map of soil C vulnerability. However, a few extreme data points can strongly influence the slope of a regression line (i.e. high leverage points) 2. Of the 49 sites in Crowther et al, only five are in the upper half of the C stock range. This paucity of high-soil C data calls into question the robustness of the overall relationship and raises the possibility that this relationship could be substantially altered by new data from sites with relatively high surface C stocks. We obtained information on soil C losses from published and unpublished data from 94 additional field warming studies worldwide, and thereby tripled the data set used by Crowther and colleagues to a total of 143 studies (Table S1). We performed the same mixed-model regression analyses as used by Crowther et al. to examine spatial patterns of soil carbon responses to warming, by linking these to standing soil C stocks, climate data and soil properties (see Methods for details, Table S2 for study-specific data on soil properties and climate, and Table S3 for Akaike Information Criterion results). We chose the same predictors in our models to compare our results directly to theirs. Our new

ABSTRACTg-aminobutyric acid (GABA) is a four-carbon non-protein amino acid presented in a wide range of organisms. In this study, a suppression subtractive hybridization (SSH) library was constructed using roots of a legume shrub, Caragana intermedia, with the combined treatment of 300 mM NaCl and 300 mM NaCl + 10 mM GABA. We obtained 224 GABA-regulated unique expressed sequence tags (ESTs) including signal transduction, transcriptional regulation, hormone biosynthesis, reactive oxygen species (ROS) and polyamine metabolism, etc. The key H2O2-generated genes, NADPH oxidase (CaGR60), peroxidase (CaGR61) and amine oxidase (CaGR62), were regulated at the mRNA level by 10 mM GABA, which clearly inhibited H2O2 accumulation brought about by NaCl stress in roots and leaves with the observation of 3,3Ј-diaminobenzidine (DAB) staining. Similarly, 10 mM GABA also regulated the expression of 1-aminocyclopropane-1-carboxylic acid (ACC) oxidase (ACO) genes (CaGR30 and CaGR31) and ethylene production in NaCl-treated roots. Surprisingly, these H2O2-generated genes were enhanced at the mRNA level by a lower concentration of GABA, at 0.25 mM, but not other alternative nitrogen sources, and endogenous GABA accumulated largely just by the application of GABA at either concentration. Our results further proved that GABA, as a signal molecule, participates in regulating the expression of genes in plants under salt stress.

a b s t r a c tGlobal warming is anticipated to have profound effects on terrestrial carbon fluxes and thus feed backs to future climate change. Ecosystem respiration (R eco ) is one of the dominant components of biosphere CO 2 fluxes, but the effects of warming on R eco are still unclear. A field warming experiment using open top chambers (OTCs) was conducted in a meadow grassland on the Tibetan Plateau to study the effects of warming on the components of R eco . Warming significantly enhanced above-ground plant respiration (R agb ) and total autotrophic plant respiration (R plant ) by 28.7% and 19.9%, respectively, but reduced heterotrophic respiration (R h ) by 10.4%. These different responses resulted in the insensitive responses of R eco and soil respiration (R s ) to the experimental warming. The warming treatment also increased R plant /R eco and R agb /R eco by 8.4% and 17.3%, respectively, while decreasing R h /R eco by 19.0%, suggesting that warming could eventually cause R eco to be dominated by R plant . Enhancements in R plant and R agb were related to the warming-induced increases in aboveground biomass (AGB) while reduced R h was closely coupled with warming-induced decrease of microbial biomass carbon. Our results highlight that the differential responses of the components of R eco to different environmental physics under warming scenarios should be taken into consideration to project the future carbon-climate feed backs.

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Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.