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Abstract Bioenergy with carbon capture and storage (BECCS) is a key negative emission technology for climate mitigation. Some countries have made no commitment to carbon neutrality but are viewed as potential BECCS candidates (hereafter, non-CN countries). Here we analyze contributions of these countries to global climate mitigation with respect to BECCS using an Earth system model with explicit representations of bioenergy crops. Switchgrass cultivation in these non-CN countries can further remove atmospheric CO2 by 9.1 ± 2.8 and 19.9 ± 5.2 PgC in the low-warming and overshot scenarios, resulting in an extra biogeochemical cooling effect of 0.01 ± 0.04 to 0.02 ± 0.06 °C. This cooling is largely counterbalanced by the biophysical warming, but the net effect is still an extra cooling. The non-CN countries play a more important role in the low-warming scenario than in the overshoot scenario, despite the inequality of temperature change among countries. Our study highlights the importance of a global system for climate mitigation.

Abstract Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth’s water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended.

Abstract Biochar has been proposed as a promising negative CO2 emission technology to mitigate future climate change with the additional benefit of increasing agricultural production. However, the spatial responses of soil organic carbon (SOC) to biochar addition in cropland are still uncertain, and the economic feasibility of large‐scale biochar implementation remains unclear. Here, we analyzed the response of SOC to biochar addition using 389 paired field measurements. The results show that biochar addition significantly increased SOC by 45.8% on average with large regional variations. Using a random forest model trained with soil, climate, biotic, biochar, and management factors, we found that the response of SOC to biochar addition was mainly dependent on biochar application rates, initial SOC, edaphic (e.g., pH), and climatic (e.g., mean annual precipitation) variables. Combined with the predicted SOC changes to biochar addition on the global cropland, we assessed the revenue of the biochar system based on the current and potential pyrolysis plants in the world using the life‐cycle analysis. Net revenue of the currently existing 144 pyrolysis plants increases with larger plant capacity and higher carbon price. Potential revenue of building new plants is high in regions like America and Europe but low in regions with infertile soil, low crop residues availability, and inconvenient transportation. The global CO2 removal of biochar application is 6.6 Tg CO2e (CO2 equivalent) year−1 with a net revenue of $ 177 million dollars at a carbon price of $ 50 t−1 CO2 for current pyrolysis plants with a biomass‐processing capacity of 20,000 t year−1. Our study provides a full economic assessment of idealized biochar addition scenarios and identifies the locations with maximal potential revenues with new pyrolysis plants.

Abstract Background Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled. Results Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation. Conclusions The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.

Bioenergy crops has been proposed as a climate mitigation measure, but how the biophysical effects of large-scale cultivation would influence the climate is not well known. Here, the authors use models to show that large-scale cultivation could cool the global land by 0.03 to 0.08 °C.

Abstract Tropical forests store about 70% of the total living biomass on land and yet very little is known about changes in this vital carbon reservoir. Changes in their biomass stock, determined by changes in carbon input (i.e., net primary production [NPP]) and carbon turnover time (τ), are critical to the global carbon sink. In this study, we calculated transient τ in tropical forest biomass using satellite‐based biomass and moderate‐resolution imaging spectroradiometer (MODIS) NPP and analyzed the trends of τ and NPP from 2001 to 2012. Results show that τ and NPP generally have opposite trends across the tropics. Increasing NPP and decreasing τ (“N+T−”) mainly distribute in central Africa and the northeast region of South America, while decreasing NPP and increasing τ (“N−T+”) prevail in Southeast Asia and western Amazon forests. Most of the N+T− tropical forest areas are associated with mean annual precipitation (MAP) below 2,000 mm·y−1 and most N−T+ tropical forests with MAP above 2,000 mm·y−1. The τ and NPP trends in the N+T− region are statistically associated with radiation, precipitation and vapor pressure deficit (VPD), while the τ and NPP trends in the N−T+ region are mainly associated with temperature and VPD. Our results inherit the uncertainties from the satellite‐based datasets and largely depend on the carbon use efficiency from MODIS. We thus systematically assessed the robustness of the findings. Our study reveals regional patterns and potential drivers of biomass turnover time and NPP changes and provides valuable insights into the tropical forest carbon dynamics.

Due to various geological problems and geological materials of the slope, there is a kind of non-continuous and uncertain natural geological body. Because of the complexity of various external factors, slope stability is not easy to be determined, which leads to the ambiguity of human’s judgments between stability and instability. Therefore, it is crucial that a simple evaluation method for judging the slope stability with uncertain information is established in slope stability analysis. This study selects nine impact factors: the lithology type, the slope structure, the development degree of discontinuity, the relationship between inclination and slope of discontinuities, the slope height, the slope angle, the mean annual precipitation, the weathering degree of rock, and the degree of human action, which can be expressed as the fuzzy cubic information (the hybrid information of both a fuzzy value and an interval-valued fuzzy number). Then, a tangent similarity measure between fuzzy cube sets (FCSs) is developed for the slope stability evaluation, where the tangent similarity measure values between FCSs of the slope sample and FCSs of slope stability grades/patterns (stability, slight stability, slight instability, and instability) are used for the assessment of the slope stability in FCS environment. Lastly, eight slope samples are provided as the actual cases to show that the eight evaluation results of slope stability using the proposed similarity measure of FCSs are in accordance with the actual results of the eight actual cases, which indicate the effectiveness of the proposed method under FCS environment.

Most scenarios from Integrated Assessment Models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieving negative emissions (together with CCS). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random forest algorithm is used to upscale a bioenergy yield dataset of 3,963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5° × 0.5° spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting the one of the five crop types with the highest yield in each of the grid cell, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha-1 yr-1. High yields mainly occur in the Amazon region and Southeast Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models, or to identify the most suitable lands for future bioenergy crop plantations. [ABSTRACT FROM AUTHOR]