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ABSTRACT:Industrialization and urbanization in the developing world have boosted steel demand during the recent two decades. Reliable estimates on how much steel is required for high economic development are necessary to better understand the future challenges for employment, resource management, capacity planning, and climate change mitigation within the steel sector. During their use phase, steel-containing products provide service to people, and the size of the in-use stock of steel can serve as indicator of the total service level. We apply dynamic material flow analysis to estimate in-use stocks of steel in about 200 countries and 3 identify patterns of how stocks evolve over time. Three different models of the steel cycle are applied and a full uncertainty analysis is conducted to obtain reliable stock estimates for the period 1700-2008.Per capita in-use stocks in countries with a long industrial history, e.g., the U.S, the UK, or Germany, are between 11 and 16 tonnes, and stock accumulation is slowing down or has come to a halt. Stocks in countries that industrialized rather recently, such as South Korea or Portugal, are between 6-10 tonnes per capita and grow fast. In several countries, per capita inuse stocks of steel have saturated or are close to saturation. We identify the range of saturation to be 13±2 tonnes for the total per capita stock, which includes 10±2 tonnes for construction, 1.3±0.5 tonnes for machinery, 1.5±0.7 tonnes for transportation, and 0.6±0.2 tonnes for appliances and containers. The time series for the stocks and the saturation levels can be used to estimate future steel production and scrap supply.

The uncertainties of China's gross primary productivity (GPP) estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm

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Phenology studies the cycle of events in nature that are initiated and driven by an annually recurring environment. Plant phenology is expected to be one of the most sensitive and easily observable natural indicators of climate change. On the Tibetan Plateau (TP), an accelerated warming since the mid-1980s has resulted in significant environmental changes. These new conditions are accompanied by phenological changes that are characterized by considerable spatiotemporal heterogeneity. Satellite remote sensing observed widespread advance in the start of the plant growing season across the plateau during the 1980s and 1990s but substantial delay over 2000–2011 in the southwest although it continued to advance in the northeast regions of the TP. Both observational studies and controlled experiments have revealed, to some extent, the positive role of higher preseason temperature and even more precipitation in advancing the leaf onset and first flowering date of the TP. However, a number of rarely visited research issues that are essential for understanding the role of phenology in ecosystem responses and feedback processes to climate change remain to be solved. Our review recommends that addressing the following questions should be a high priority. How did other phenological events change, such as flowering and fruiting phenology? What are the influences from environmental changes other than temperature and precipitation, including human activities such as grazing? What are the genetic and physiological bases of plants phenological responses? How does phenological change influence ecosystem structure and function at different scales and feedback to the climate system? Investigating these research questions requires, first of all, new data of the associated environmental variables, and consistent and reliable phenological observation using different methodologies (i.e. in situ observations and remote sensing).

Abstract. Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grassbiomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5 • by 0.5 • . The grassbiomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901-2012. The grassbiomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, rising CO 2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6

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