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The land and ocean act as a sink for fossil-fuel emissions, thereby slowing the rise of atmospheric carbon dioxide concentrations (1). Although the uptake of carbon by oceanic and terrestrial processes has kept pace with accelerating carbon dioxide emissions until now, atmospheric carbon dioxide concentrations exhibit a large variability on interannual timescales (2), considered to be driven primarily by terrestrial ecosystem processes dominated by tropical rainforests (3). We use a terrestrial biogeochemical model, atmospheric carbon dioxide inversion and global carbon budget accounting methods to investigate the evolution of the terrestrial carbon sink over the past 30 years, with a focus on the underlying mechanisms responsible for the exceptionally large land carbon sink reported in 2011 (ref. 2). Here we show that our three terrestrial carbon sink estimates are in good agreement and support the finding of a 2011 record land carbon sink. Surprisingly, we find that the global carbon sink anomaly was driven by growth of semi-arid vegetation in the Southern Hemisphere, with almost 60 per cent of carbon uptake attributed to Australian ecosystems, where prevalent La Nina conditions caused up to six consecutive seasons of increased precipitation. In addition, since 1981, a six per cent expansion of vegetation cover over Australia was associated with a fourfold increase in the sensitivity of continental net carbon uptake to precipitation. Our findings suggest that the higher turnover rates of carbon pools in semi-arid biomes are an increasingly important driver of global carbon cycle inter-annual variability and that tropical rainforests may become less relevant drivers in the future. More research is needed to identify to what extent the carbon stocks accumulated during wet years are vulnerable to rapid decomposition or loss through fire in subsequent years., Each year, on average, land and ocean carbon sinks absorb the equivalent of about half of the global fossil fuel emissions, thereby providing a critical service that slows the rise [...]

This study proposes a hybrid method for producing personalized travel recommendation that better meet travellers'individual needs and also improve their online booking experience. The proposed method integrates multi-attribute collaborative filtering with social network analysis within the framework of large-scale group decision-making. It includes four modules, i.e. identification of online opinion experts, construction of a social network, detection of user communities, and interactively produced of personalized travel recommendation. Specifically, the preliminary user filtering and k-means clustering approach are utilized to identify the online opinion experts for a specific travel recommendation issue. Then, social network construction and its community detection process are adopted to alleviate the sparsity problem. Finally, the travel alternatives are ranked to select recommendations, and this is done interactively with travellers to handle the cold start problem. With the proposed method, a better online booking experience can be achieved for travellers, as they are presented with a more appropriate set of recommended options and so can make better travel decisions. [ABSTRACT FROM AUTHOR]

This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used. [ABSTRACT FROM AUTHOR]