Accurate assessment of anthropogenic carbon dioxide (CO₂) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere 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 a 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 as well as consistency within and among components, alongside methodology and data limitations. CO₂ emissions from fossil fuels and industry (E_FF) are based on energy statistics and cement production data, 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 CO₂ concentration is measured directly and its rate of growth (G_ATM) is computed from the annual changes in concentration. The mean ocean CO₂ 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 CO₂ measurements. The global residual terrestrial CO₂ 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 forced by observed climate, CO₂, and land-cover change (some including nitrogen–carbon interactions). 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 ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), E_FF was 9.0 ± 0.5 GtC yr⁻¹, E_LUC was 0.9 ± 0.5 GtC yr⁻¹, G_ATM was 4.4 ± 0.1 GtC yr⁻¹, S_OCEAN was 2.6 ± 0.5 GtC yr⁻¹, and S_LAND was 3.0 ± 0.8 GtC yr⁻¹. For the year 2014 alone, E_FF grew to 9.8 ± 0.5 GtC yr⁻¹, 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr⁻¹ that took place during 2005–2014. Also, for 2014, E_LUC was 1.1 ± 0.5 GtC yr⁻¹, G_ATM was 3.9 ± 0.2 GtC yr⁻¹, S_OCEAN was 2.9 ± 0.5 GtC yr⁻¹, and S_LAND was 4.1 ± 0.9 GtC yr⁻¹. G_ATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger S_LAND for that year. The global atmospheric CO₂ concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E_FF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of E_FF and assumed constant E_LUC for 2015, cumulative emissions of CO₂ will reach about 555 ± 55 GtC (2035 ± 205 GtCO₂) for 1870–2015, 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., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2015).

Grasslands absorb and release carbon dioxide (CO₂), emit methane (CH₄) from grazing livestock, and emit nitrous oxide (N₂O) from soils. Little is known about how the fluxes of these three greenhouse gases, from managed and natural grasslands worldwide, have contributed to past climate change, or the roles of managed pastures versus natural grasslands. Here, global trends and regional patterns of the full greenhouse gas balance of grasslands are estimated for the period 1750 to 2012. A new spatially explicit land surface model is applied, to separate the direct effects of human activities from land management and the indirect effects from climate change, increasing CO₂ and regional changes in nitrogen deposition. Direct human management activities are simulated to have caused grasslands to switch from a sink to a source of greenhouse gas, because of increased livestock numbers and accelerated conversion of natural lands to pasture. However, climate change drivers contributed a net carbon sink in soil organic matter, mainly from the increased productivity of grasslands due to increased CO₂ and nitrogen deposition. The net radiative forcing of all grasslands is currently close to neutral, but has been increasing since the 1960s. Here, we show that the net global climate warming caused by managed grassland cancels the net climate cooling from carbon sinks in sparsely grazed and natural grasslands. In the face of future climate change and increased demand for livestock products, these findings highlight the need to use sustainable management to preserve and enhance soil carbon storage in grasslands and to reduce greenhouse gas emissions from managed grasslands.

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.