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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAS.FGOALS-f3-L.ssp370' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.CAS.FGOALS-g3.hist-GHG' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Xuanwei City (formerly known as Xuanwei County) locates in the northeastern of Yunnan Province and is rich in coal, iron, copper and other mines, especially the smoky (bituminous) coal. Unfortunately, the lung cancer morbidity and mortality rates in this region are among China's highest, with a clear upward trend from the mid-1970s to mid-2000s. In 2004-2005, the crude death rate of lung cancer was 91.3 per 100,000 in the whole Xuanwei City, while that for Laibin Town in this city was 241.14 per 100,000. The epidemiologic distribution (clustering patterns by population, time, and space) of lung cancer in Xuanwei has some special features, e.g., high incidence in rural areas, high incidence in females, and an early age peak in lung cancer deaths. The main factor that associates with a high rate of lung cancer incidence was found to be indoor air pollution caused by the indoor burning of smoky coal. To a certain extent, genetic defects are also associated with the high incidence of lung cancer in Xuanwei. Taken together, lung cancer in this smoky coal combustion region is a unique model for environmental factor-related human cancer, and the current studies indicate that abandoning the use of smoky coal is the key to diminish lung cancer morbidity and mortality.

A pot experiment was undertaken to investigate the effects of Cd and Cu mixtures to growth and nutrients (sugar, carotene or vitamin C) of carrot and pakchoi under greenhouse cultivation condition. The study included: (a) physical-chemical properties of soil and soil animals in response to Cd and Cu stress; (b) bioaccumulation of heavy metals, length, biomass, contents of sugar and carotene (vitamin C) of carrot and pakchoi; (c) estimation the effects of Cd and Cu mixtures by multivariate regression analysis. The results implied that heavy metals impacted negative influence on soil animals' abundance. The metals contents in plants increased obviously with Cd and Cu contamination in soil. The biomass production and nutrients declined with Cd and Cu contents increasing. Cd (20 mg kg-1) treatment caused maximum reduction of sugar content (45.29%) in carrot root; maximum reduction in carotene content (75.73%) in carrot, 75.1% sugar content reduction and 70.58% vitamin C content reduction in pakchoi shoots were observed with addition of Cd (20 mg kg-1) and Cu (400 mg kg-1) mixture. The results of multivariate regression analysis indicated that combination of Cd and Cu exerts negative effects to both carrot and pakchoi, and both growth and nutrients were negatively correlated with metals concentrations. It is concluded that the Cd and Cu mixtures caused toxic damage to vegetable plants as Cd and Cu gradient concentrations increased.

Exploring the sensitivity of rural households’ livelihood strategies to livelihood capital is of great significance for improving rural households’ livelihood levels. This paper selects 23 livelihood capital measurement indicators and conducts an in-depth survey of rural households. In addition, the entropy method and a weighted comprehensive model are used to explore the basic characteristics of rural households’ livelihood capital in the upper reaches of the Min River, China, in 2017. Furthermore, econometric models are used to analyze the sensitivity of rural households’ livelihood strategies to livelihood capital. As indicated from the research, the livelihood capital levels of different types of rural households in the study area are not equivalent. The types of rural households with different livelihood strategies can be ordered in terms of quantity as follows: non-agricultural type > non-agricultural dominant type > agricultural dominant type > pure agricultural type. Livelihood strategies have different sensitivities to different livelihood capital measurement indicators. Among these indicators, cash income, the number of relatives and friends available for financial assistance, and the number of civil servants have positive effects on the livelihood strategy selection of non-agricultural dominant rural households and non-agricultural rural households. However, the average age of laborers, area of cultivated land and gardens, number of livestock and poultry, and present value of production tools have negative effects. These evaluation results can provide a scientific decision-making basis for the formulation of poverty alleviation policies by relevant government departments.

In this work, the impact of exogenous aerobic bacteria mixture (EABM) on municipal solid waste (MSW) is well evaluated in the following aspects: biogas production, leachate analysis, organic waste degradation, EABM population, and the composition of microbial communities. The study was designed and performed as follows: the control bioreactor (R1) was filled up with MSW and the culture medium of EABM and the experimental bioreactor (R2) was filled up with MSW and EABM. The data suggests that the composition of microbial communities (bacterial and methanogenic) in R1 and R2 were similar at day 0, while the addition of EABM in R2 led to a differential abundance of Bacillus cereus, Bacillus subtilis, Staphylococcus saprophyticus, Staphlyoccus xylosus, and Pantoea agglomerans in two bioreactors. The population of exogenous aerobic bacteria in R2 greatly increased during hydrolysis and acidogenesis stages, and subsequently increased the degradation of volatile solid (VS), protein, lipid, and lignin by 59.25%, 25.68%, 60.47%, and 197.62%, respectively, compared to R1. The duration of hydrolysis and acidogenesis in R2 was 33.33% shorter than that in R1. At the end of the study, the accumulative methane yield in R2 (494.4 L) was almost three times more than that in R1 (187.4 L). In addition, the abundance of acetoclasic methanogens increased at acetogenesis and methanogenesis stages in both bioreactors, which indicates that acetoclasic methanogens (especially Methanoseata) could contribute to methane production. This study demonstrates that EABM can accelerate organic waste degradation to promote MSW biodegradation and methane production. Moreover, the operational parameters helped EABM to generate 20.85% more in accumulative methane yield. With a better understanding of how EABM affects MSW and the composition of bacterial community, this study offers a potential practical approach to MSW disposal and cleaner energy generation worldwide.

Coal will continue to be the main energy source in China for the immediate future, although the environmental pollution and ecological impacts of each stage in the full life cycle of coal mining, transportation, and combustion generate large quantities of external costs. The Late Permian coals in southwestern (SW) China usually contain high amounts of fluorine (F), arsenic (As), and ash, which together with high-F clays cause abnormally high levels of endemic fluorosis, As poisoning, and lung cancer in areas where coal is mined and burned. In this paper, we estimate the external costs of the life cycle of coal. The results show that the externalities of coal in SW China are estimated at USD 73.5 billion or 284.3 USD/t, which would have accounted for 6.5 % of the provincial GDP in this area in 2018. The external cost of human health accounts for 87.2% of the total external costs, of which endemic skeletal fluorosis diseases and related lung cancers have the most important impact. Our study provides a more precise estimate of externalities compared with its counterparts in other provinces in China. Therefore, several policy recommendations would be proposed to internalize the external cost.

The issue of achieving sustainable livelihoods (SL) is a persistent problem that has gained significant interest for all countries. Even though contexts of vulnerability have been highlighted to be critical to SL, the difference of SL under vulnerability contexts, particularly disaster, has been ignored. As one disaster-prone area, there is an urgent need to conduct studies on SL in Shenzha, within the context of the construction of a national park. This paper proposes to address this research gap by evaluating SL under various disaster contexts in Shenzha, China. According to the frequency of natural disasters, towns in Shenzha can be divided into three groups: Snowstorm and windstorm-dominated towns (SWT), mixed towns (MT) and drought-dominated towns (DT). The results showed that (1) a great disparity of SL can be observed among the three vulnerability groups. The scores of these SL were sorted into descending order as: DT > SWT > MT. (2) In detail, herdsmen in DT have a high value of SL because they have high livelihood assets, livelihood strategies and disaster management capabilities. (3) Herdsmen in SWT have high livelihood assets, particularly human and financial assets, and livelihood strategies. (4) The low livelihood assets and livelihood strategies have restricted the SL of herdsmen in MT. An analysis of SL under various disaster contexts helped to depict the characteristics of SL. Accordingly, targeted policies were developed for the development of SL under various disaster contexts.