Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO2 and photosynthesis and in-situ flux tower measures. We show the 2010–11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010–11 net CO2 uptake was highly transient with rapid dissipation through drought. The size of the 2010–11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011–12, and was nearly eliminated in 2012–13 (0.08 Pg). We further report evidence of an earlier 2000–01 large net CO2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle.

Key Points miR-146a may be involved in the pathogenesis of ALPS by targeting Fas. Sustained expression of miR-146a in B cells is the major factor leading to the enhanced homeostatic expansion of B and T cells.

No abstract

Big data reveals new, stark pictures of the state of our environments. It also reveals 'bright spots' amongst the broad pattern of decline and-crucially-the key conditions for these cases. Big data analyses could benefit the planet if tightly coupled with ongoing sustainability efforts. This big data revolution, which encompasses techniques to capture, process, analyse and visualize large datasets in a rapid timeframe, has led to an explosion in data variety over the last five decades (Fig. 1a). Significant advances in data growth in the bio-geophysical sciences have allowed scientists to discover, analyse and understand environmental changes at micro to global scales, and separate out what is human-driven. As a consequence, the state and trends of the environment is increasingly becoming a focus of big data applications (Fig. 1b). Here, we discuss the trends emerging from these environmental analyses (including the derived data products) and propose a way forward to harness these technologies to mitigate global environmental declines. Environmental changes revealed by big data Almost invariably, the advances in big data analyses confirm planetary declines and, in most cases, reveal that declines are worse than previously indicated (Fig. 1b). For example, a landmark collaboration between NASA and the European Space Agency integrated the Antarctic ice sheet's changing volume, flow and gravitational attraction to model its surface mass balance, which revealed Antarctica lost 2720 ± 1390 Mg of ice between 1992 and 2017 (equating to a sea-level rise of 7.6 ± 3.9 mm) 2. Yet only a few years prior, the IPCC stated that 'we have medium confidence in model projections of a future Antarctic SMB [surface mass balance] increase, implying a negative contribution to GMSL [global mean sea level] rise' (p.1155 10). Similarly, efforts to create a Red List of Ecosystems-an emerging methodology using multiple lines of evidence to assess the threat status of ecosystems 11-has already revealed some alarming results. For instance, in the Americas and the Caribbean, 85% of the forest area and 80% of forest types are potentially threatened 12. While these methodical developments are impressive and urgently needed, they reveal a stark picture for the environment. However, analyses of big data have also revealed 'bright spots' amongst the broad pattern of decline and-crucially-identify the key drivers, including deliberate policy interventions. For instance, while Hansen et al. 5 revealed dramatic declines in forest extent across the globe, forest loss in Brazil was decreasing by 1318 km 2 y −1 through the 12 year period to 2012, primarily due to a progressive legal framework covering forests during the study period (although the change