A comprehensive quantification of global nitrous oxide sources and sinks.

Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum-maximum estimates: 12.2-23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9-17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2-11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies-particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O-climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.

A 130-year global inventory of methane emissions from livestock: Trends, patterns, and drivers.

Livestock contributes approximately one-third of global anthropogenic methane (CH4 ) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country-level information is reported regularly through national inventories and global databases, spatially explicit quantification of century-long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~9 km at the equator) during the period 1890-2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5-37.1] (mean [minimum-maximum of 95% confidence interval) Tg CH4 yr-1 in 1890 to 131.7 [109.6-153.7] Tg CH4 yr-1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial-temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.

Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO3, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.

Methane emission from global livestock sector during 1890-2014: Magnitude, trends and spatiotemporal patterns.

Human demand for livestock products has increased rapidly during the past few decades largely due to dietary transition and population growth, with significant impact on climate and the environment. The contribution of ruminant livestock to greenhouse gas (GHG) emissions has been investigated extensively at various scales from regional to global, but the long-term trend, regional variation and drivers of methane (CH4 ) emission remain unclear. In this study, we use Intergovernmental Panel on Climate Change (IPCC) Tier II guidelines to quantify the evolution of CH4 emissions from ruminant livestock during 1890-2014. We estimate that total CH4 emissions in 2014 was 97.1 million tonnes (MT) CH4 or 2.72 Gigatonnes (Gt) CO2 -eq (1 MT = 1012 g, 1 Gt = 1015 g) from ruminant livestock, which accounted for 47%-54% of all non-CO2 GHG emissions from the agricultural sector. Our estimate shows that CH4 emissions from the ruminant livestock had increased by 332% (73.6 MT CH4 or 2.06 Gt CO2 -eq) since the 1890s. Our results further indicate that livestock sector in drylands had 36% higher emission intensity (CH4 emissions/km2 ) compared to that in nondrylands in 2014, due to the combined effect of higher rate of increase in livestock population and low feed quality. We also find that the contribution of developing regions (Africa, Asia and Latin America) to the total CH4 emissions had increased from 51.7% in the 1890s to 72.5% in the 2010s. These changes were driven by increases in livestock numbers (LU units) by up to 121% in developing regions, but decreases in livestock numbers and emission intensity (emission/km2 ) by up to 47% and 32%, respectively, in developed regions. Our results indicate that future increases in livestock production would likely contribute to higher CH4 emissions, unless effective strategies to mitigate GHG emissions in livestock system are implemented.

Revisiting enteric methane emissions from domestic ruminants and their δ13CCH4 source signature.

Accurate knowledge of 13C isotopic signature (δ13C) of methane from each source is crucial for separating biogenic, fossil fuel and pyrogenic emissions in bottom-up and top-down methane budget. Livestock production is the largest anthropogenic source in the global methane budget, mostly from enteric fermentation of domestic ruminants. However, the global average, geographical distribution and temporal variations of the δ13C of enteric emissions are not well understood yet. Here, we provide a new estimation of C3-C4 diet composition of domestic ruminants (cattle, buffaloes, goats and sheep), a revised estimation of yearly enteric CH4 emissions, and a new estimation for the evolution of its δ13C during the period 1961-2012. Compared to previous estimates, our results suggest a larger contribution of ruminants' enteric emissions to the increasing trend in global methane emissions between 2000 and 2012, and also a larger contribution to the observed decrease in the δ13C of atmospheric methane.

Complex spatiotemporal responses of global terrestrial primary production to climate change and increasing atmospheric CO2 in the 21st century.

Quantitative information on the response of global terrestrial net primary production (NPP) to climate change and increasing atmospheric CO2 is essential for climate change adaptation and mitigation in the 21st century. Using a process-based ecosystem model (the Dynamic Land Ecosystem Model, DLEM), we quantified the magnitude and spatiotemporal variations of contemporary (2000s) global NPP, and projected its potential responses to climate and CO2 changes in the 21st century under the Special Report on Emission Scenarios (SRES) A2 and B1 of Intergovernmental Panel on Climate Change (IPCC). We estimated a global terrestrial NPP of 54.6 (52.8-56.4) PgC yr(-1) as a result of multiple factors during 2000-2009. Climate change would either reduce global NPP (4.6%) under the A2 scenario or slightly enhance NPP (2.2%) under the B1 scenario during 2010-2099. In response to climate change, global NPP would first increase until surface air temperature increases by 1.5 °C (until the 2030s) and then level-off or decline after it increases by more than 1.5 °C (after the 2030s). This result supports the Copenhagen Accord Acknowledgement, which states that staying below 2 °C may not be sufficient and the need to potentially aim for staying below 1.5 °C. The CO2 fertilization effect would result in a 12%-13.9% increase in global NPP during the 21st century. The relative CO2 fertilization effect, i.e. change in NPP on per CO2 (ppm) bases, is projected to first increase quickly then level off in the 2070s and even decline by the end of the 2080s, possibly due to CO2 saturation and nutrient limitation. Terrestrial NPP responses to climate change and elevated atmospheric CO2 largely varied among biomes, with the largest increases in the tundra and boreal needleleaf deciduous forest. Compared to the low emission scenario (B1), the high emission scenario (A2) would lead to larger spatiotemporal variations in NPP, and more dramatic and counteracting impacts from climate and increasing atmospheric CO2.