Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO 2 and nitrogen across major terrestrial vegetation types of the world. Most experiments manipulated single rather than multiple global change drivers in temperate ecosystems of the USA, Europe and China. The magnitudes of warming and elevated CO 2 treatments were consistent with the ranges of future projections, whereas those of precipitation changes and nitrogen inputs often exceeded the projected ranges. Increases in global change drivers consistently accelerated, but decreased precipitation slowed down carbon-cycle processes. Nonlinear (including synergistic and antagonistic) effects among global change drivers were rare. Belowground carbon allocation responded negatively to increased precipitation and nitrogen addition and positively to decreased precipitation and elevated CO 2. The sensitivities of carbon variables to multiple global change drivers depended on the background climate and ecosystem condition, suggesting that Earth system models should be evaluated using site-specific conditions for best uses of this large dataset. Together, this synthesis underscores an urgent need to explore the interactions among multiple global change drivers in underrepresented regions such as semi-arid ecosystems, forests in the tropics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback.

Utilizing winter (November-March) accumulated snow depth data at 60 stations over the Tibetan Plateau (TP) for the period 1960-98, three typical patterns of the TP snow anomaly's spatial distribution were objectively classified by means of empirical orthogonal function (EOF) analysis. They are characterized by light snow over the entire Tibet region (LS pattern), by an eastern Tibet heavy snow (ETHS pattern), and by a southwestern Tibet heavy snow (SWTHS pattern), respectively. The possible relations between various patterns of the Tibet winter snow anomaly and subsequent summer monsoon and rainfall over south, southeast, and east Asia are investigated using composite analysis. In ETHS and SWTHS years, the south and southeast Asian summer monsoon becomes weak and there is less summer rainfall over south and southeast Asia than in normal years. In LS years, the anomalies of the subsequent summer monsoon and rainfall are opposite to those in ETHS and SWTHS years. The physical mechanism is, in part, attributed to the impact of heavy snow on Tibet's atmospheric temperature, on the land-sea meridional thermal contrast, and also on the strength of the summer monsoon. The variation of summer rainfall over China associated with the preceding winter TP snow anomaly is also analyzed. There is a clear positive correlation between the Tibetan winter snow and the subsequent summer rainfall over the middle and lower reaches of the Yangtze River valley (central China). In contrast to the previous studies that use snow cover averaged over all of the Tibetan Plateau as a single number, the association between the winter snow and the subsequent summer precipitation over east China is much clearer.

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Construction supply chain management is a unique and problematic issue within the construction industry due to its inevitable external risks and variations. The resilience capability of a supplier is of significance in supplier selection; a supplier selected in the context of a resilient construction supply chain (RCSC) is referred to in this research as a "resilient construction supplier". This paper proposes a supplier selection framework tailored to effective information integration for supply chain management. The proposed framework works by integrating building information modeling (BIM) and a geographic information system (GIS) in a RCSC. BIM and GIS together provide highly transparent construction material information, enhanced supply chain status visualization, and workable access information for supplier selection. Supplier performance is evaluated via seventeen resilient criteria under a combined methodology consisting of the analytic hierarchy process (AHP) and grey relational analysis (GRA); AHP and GRA weigh the criteria and rank the suppliers respectively. By varying the weightings given to each criterion, sensitivity analysis was conducted to identify the criteria of resilience which impact the selection priorities of suppliers. An illustrative example is also provided to show the overall process of the proposed framework.

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Drought events could have profound influence on plant community structure and ecosystem function, and have subsequent impacts on community stability, but we know little about how different climate warming scenarios affect community resistance and resilience to drought. Combining a daytime and nighttime warming experiment in the temperate steppe of north China with a natural drought event during the study period, we tested how daytime and nighttime warming influences drought resistance and resilience. Our results showed that the semi-arid steppe in north China was resistant to both daytime and nighttime warming, but vulnerable to drought. Nighttime warming, but not daytime warming, enhanced community resistance to drought via stimulating carbon sequestration, whereas neither daytime nor nighttime warming affected community resilience to drought. Large decline in plant community cover, primarily caused by the reduction in the cover of dominant and rare species rather than subordinate species during drought, did not preclude rapid ecosystem recovery. These findings suggest that nighttime warming may facilitate ecosystem sustainability and highlight the need to assess the effects of climate extremes on ecosystem functions at finer temporal resolutions than based on diurnal mean temperature.

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