Our understanding and quantification of global soil nitrous oxide (N2O) emissions and the underlying processes remain largely uncertain. Here, we assessed the effects of multiple anthropogenic and ...natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change, and rising atmospheric CO2 concentration, on global soil N2O emissions for the period 1861–2016 using a standard simulation protocol with seven process‐based terrestrial biosphere models. Results suggest global soil N2O emissions have increased from 6.3 ± 1.1 Tg N2O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N2O‐N/year to 3.3 Tg N2O‐N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N2O emissions since the 1970s. However, US cropland N2O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N2O emissions appear to have decreased by 14%. Soil N2O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N2O‐N/year (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application, and climate change contributed 54%, 26%, 15%, and 24%, respectively, to the total increase. Rising atmospheric CO2 concentration reduced soil N2O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N2O emissions, this study recommends several critical strategies for improving the process‐based simulations.
The ensemble of terrestrial biosphere models indicates that global soil N2O emissions have increased from 6.3 ± 1.1 Tg N2O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N2O‐N/year to 3.3 Tg N2O‐N/year over the same period, accounting for 82% of the total increase, among which 54% attributes to nitrogen fertilizer application. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N2O emissions since the 1970s. However, European cropland N2O emissions appear to have decreased by 14%.
Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global ...crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.
Rapid deforestation is a major sustainability challenge, partly as the loss of carbon sinks exacerbates global climate change. In Cambodia, more than 13% of the total land area has been contracted ...out to foreign and domestic agribusinesses in the form of economic land concessions, causing rapid large-scale land use change and deforestation. Additionally, the distant drivers of local and global environmental change often remain invisible. Here, we identify hotspots of carbon loss between 1987-2017 using the dynamic global vegetation model LPJ-GUESS and by comparing past and present land use and land cover. We also link global consumption and production patterns to their environmental effects in Cambodia by mapping the countries to which land-use embedded carbon are exported. We find that natural forests have decreased from 54%-21% between 1987 and 2017, mainly for the expansion of farmland and orchards, translating into 300 million tons of carbon lost, with loss rates over twice as high within economic land concessions. China is the largest importer of embedded carbon, mainly for rubber and sugarcane from Chinese agribusinesses. Cambodian investors have also negatively affected carbon pools through export-oriented products like rubber. The combined understanding of environmental change and trade flows makes it possible to identify distant drivers of deforestation, which is important for crafting more environmentally and socially responsible policies on national and transnational scales.
Concerns over climate change are motivated in large part because of their impact on human
society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it ...requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
Using biomass for charcoal production in sub‐Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether ...these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO2 and 1.3 million tonnes of CH4. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off‐balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ‐GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi‐desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m2 to 2.46 ± 3.43 kg C/m2 and −2.73 ± 2.80 kg C/m2 to 1.87 ± 4.94 kg C/m2 respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi‐desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks.
Biomes in Africa with high biomass are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. The finding of this study suggests that charcoal production in sub‐Saharan Africa is not sustainable, especially in tropical rain forests and montane forests, where we observe more carbon losses and longer recovery for vegetation and litter carbon stocks than in other biomes.
THE GLOBAL N₂O MODEL INTERCOMPARISON PROJECT Tian, Hanqin; Yang, Jia; Lu, Chaoqun ...
Bulletin of the American Meteorological Society,
06/2018, Volume:
99, Issue:
6
Journal Article
Peer reviewed
Open access
Nitrous oxide (N₂O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and ...spatiotemporal patterns of N₂O fluxes and the key drivers of N₂O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N₂O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N₂O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N₂O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N₂O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N₂O dynamics; and 3) provide a benchmarking estimate of N₂O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N₂O estimation derived from the NMIP is a key component of the global N₂O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.
Anthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, ...using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO
fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO
fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink.
Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global ...scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
Abstract
The ongoing climate change can modulate the behavior of global vegetation and influence the terrestrial biosphere carbon sink. Past observation-based studies have mainly focused on the ...linear trend or interannual variability of the vegetation greenness, but could not explicitly deal with the effect of natural decadal variability due to the short length of observations. Here we put the variabilities revealed by remote sensing-based global leaf area index (LAI) from 1982 to 2015 into a long-term perspective with the help of ensemble Earth system model simulations of the historical period 1850–2014, with a focus on the low-frequency variability in the global LAI during the growing season. Robust decadal variability in the observed and modelled LAI was revealed across global terrestrial ecosystems, and it became stronger toward higher latitudes, accounting for over 50% of the total variability north of 40°N. The linkage of LAI decadal variability to major natural decadal climate modes, such as the El Niño–Southern Oscillation decadal variability (ENSO-d), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO), was analyzed. ENSO-d affects LAI by altering precipitation over large parts of tropical land. The PDO exerts opposite impacts on LAI in the tropics and extra-tropics due to the compensation between the effects of temperature and growing season length. The AMO effects are mainly associated with anomalous precipitation in North America and Europe but are mixed with long-term climate change impacts due to the coincident phase shift of the AMO which also induces North Atlantic basin warming. Our results suggest that the natural decadal variability of LAI can be largely explained by these decadal climate modes (on average 20% of the variance, comparable to linear changes, and over 40% in some ecosystems) which also can be potentially important in inducing the greening of the Earth of the past decades.
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