Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño

The forest biome of Amazonia is one of Earth's greatest biological treasures and a major component of the Earth system. This century, it faces the dual threats of deforestation and stress from climate change. Here, we summarize some of the latest findings and thinking on these threats, explore the consequences for the forest ecosystem and its human residents, and outline options for the future of Amazonia. We also discuss the implications of new proposals to finance preservation of Amazonian forests.

Plants, with an estimated 300,000 species, provide crucial primary production and ecosystem structure. To date, our quantitative understanding of diversity gradients of megadiverse clades such as plants has been hampered by the paucity of distribution data. Here, we investigate the global-scale species-richness pattern of vascular plants and examine its environmental and potential historical determinants. Across 1,032 geographic regions worldwide, potential evapotranspiration, the number of wet days per year, and measurements of topographical and habitat heterogeneity emerge as core predictors of species richness. After accounting for environmental effects, the residual differences across the major floristic kingdoms are minor, with the exception of the uniquely diverse Cape Region, highlighting the important role of historical contingencies. Notably, the South African Cape region contains more than twice as many species as expected by the global environmental model, confirming its uniquely evolved flora. A combined multipredictor model explains approximately 70% of the global variation in species richness and fully accounts for the enigmatic latitudinal gradient in species richness. The models illustrate the geographic interplay of different environmental predictors of species richness. Our findings highlight that different hypotheses about the causes of diversity gradients are not mutually exclusive, but likely act synergistically with water-energy dynamics playing a dominant role. The presented geostatistical approach is likely to prove instrumental for identifying richness patterns of the many other taxa without single-species distribution data that still escape our understanding.

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1 σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), E FF was 9.3 ± 0.5 GtC yr −1 , E LUC 1.0 ± 0.5 GtC yr −1 , G ATM 4.5 ± 0.1 GtC yr −1 , S OCEAN 2.6 ± 0.5 GtC yr −1 , and S LAND 3.1 ± 0.9 GtC yr −1 . For year 2015 alone, the growth in E FF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr −1 , showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr −1 that took place during 2006–2015. Also, for 2015, E LUC was 1.3 ± 0.5 GtC yr −1 , G ATM was 6.3 ± 0.2 GtC yr −1 , S OCEAN was 3.0 ± 0.5 GtC yr −1 , and S LAND was 1.9 ± 0.9 GtC yr −1 . G ATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller S LAND for that year. The global atmospheric CO 2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in E FF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of E FF in 2016, the growth rate in atmospheric CO 2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink ( S LAND ) in response to El Niño conditions of 2015–2016. From this projection of E FF and assumed constant E LUC for 2016, cumulative emissions of CO 2 will reach 565 ± 55 GtC (2075 ± 205 GtCO 2 ) for 1870–2016, about 75 % from E FF and 25 % from E LUC . This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2016 ).