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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-g3' 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.

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-g3.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-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.

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about whether drought affects alien plant invasion indirectly via interactions of plants with other groups of organisms such as soil mesofauna. To test for such indirect effects, we grew single plants of nine naturalized alien target species in pot-mesocosms with a community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-mesofauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive strength in the native community. Drought also decreased the abundance of soil mesofauna, particularly soil mites, but did not affect the abundance and richness of soil herbivores. Soil-fauna inoculation did not affect biomass of the alien plants but increased biomass of the native plant community, and thereby decreased the relative biomass production of the alien plants. This increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Synthesis. Our multispecies experiment thus shows that soil fauna might help native communities to resist alien plant invasions, but that this effect might be weakened under drought. In other words, soil mesofauna may buffer the negative effects of drought on alien plant invasions. The file archives 'SoilFauna_Drought_PlantInvasion_Date_YJL.tar' include three dataset, one named 'SoilFauna_Drought_PlantInvasion.csv' (Biomass data), one named 'SoilFaunaData.csv' (Soil Fauna data), and one named 'SoilNitrogenData.csv' (soil nitrogen data). The file 'SoilFauna_Drought_PlantInvasion.Rmd' is the R script, and its output is 'SoilFauna_Drought_PlantInvasion.html'. All data were collected from a greenhouse expeirment at the Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences.

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-g3.ssp119' 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.

Microcystis development in most temperate lakes shows an annual cycle that is mainly triggered by water temperature and includes four stages. This study aims to identify the optimum growth temperature and the temperature thresholds for recruitment and overwintering in Microcystis in Lake Taihu, based on field data and experiments at the cellular and genetic level on Microcystis under a simulated temperature condition. The field investigation showed that the cyanobacterial biomass began to increase at 11-15 °C in spring, reached a peak at 20-30 °C and remained at a low level after the water temperature declined below 6 °C. The simulation experiment found that the recovery of gene expression, photosynthesis and growth in Microcystis cells occurred at 11-14 °C and increased to an appreciable level after the temperature exceeded 20 °C. Microcystis cells stopped growing and maintained low photosynthetic activity and gene expression when the temperature declined to 10 °C or lower. These results suggest that Microcystis in Lake Taihu begin recruitment at 11-14 °C in spring and grow vigorously at 20-30 °C, then overwinter at 10 °C or lower in winter.

Abstract Methane is responsible for 20% of the global warming resulting from greenhouse gas emissions. Municipal solid waste (MSW) landfills are the third largest anthropogenic source of methane and are thus important to estimating the global methane budget and evaluating its contribution to global greenhouse gas emissions. Based on the greenhouse gas inventory guidelines from the Intergovernmental Panel on Climate Change (IPCC) and the first-order decay method used to estimate emissions from MSW landfills – and in line with MSW management in various regions – we calculated methane emissions from MSW landfills in various Chinese provinces from 2003 to 2013. During this period, methane emissions from MSW landfills increased from 1141.10 Gg to 1858.98 Gg, representing a mean annual increase of 71.79 Gg. MSW emissions tended to increase more in the northern and western provinces than in the southern and eastern provinces, as methane emissions strongly and positively correlated with population and socioeconomic demographics. MSW decontamination is growing rapidly in China, and landfills predominate in all MSW treatments; moreover, incineration has also dramatically increased in recent years.

Using a population dataset of China, this study analyzes the spatial pattern of rural migration and urbanization and their coupling coordination relationship and investigates the causes of their spatial heterogeneity. Results show that rural migration and urbanization from 1978 to 2017 can be divided into three stages, i.e., the recovery and development stage, the stable and rapid development stage, and the stage of promoting the citizenization of the rural migrant population. From 2000 to 2010, counties with average annual growth rates of the ratio of rural migration (GRM) ranging from 0 to 5.00% showed a spatial pattern of ubiquitous distribution, while there were significant spatial inequalities in the average annual growth rates of the urbanization rate of the residential population (GUR) and hukou-registered population (GUH). Since urbanization and rural migration are two synergistic processes, coupling coordination degrees (CCDs) between GRM and GRU as well as GRM and GUH were generally between 0.60 and 0.80. Due to the gaps in socioeconomic development, spatial distance, and the policy system, they also showed regional heterogeneity, and there were notable differences in CCD between rural migration and urbanization of residential and hukou-registered populations. Finally, we propose that China should implement targeted and people-oriented measures to guide rural migration, promote new-type urbanization, and achieve integrated urban–rural development.

Rural household livelihood research of ethnic minorities is urgent to the development of ethnic areas in China and achieve the world poverty reduction goal. To improve ethnic rural household livelihood, it is fundamental to figure out what are the types, characteristics as well as impact factors of their livelihood strategies. In the study, we explored the household livelihood strategy choices and livelihood diversity of the main ethnic minorities (Tujia and Miao) in Chongqing, as well as how livelihood capitals impact livelihood strategy through methods of clustering, livelihood diversity index and multiple logistic regression under the framework of sustainable livelihood approach. The results show that: (1) Full-time job, both full-time and part-time job, part-time agriculture, part-time job and subsidized livelihood strategy are livelihood strategies adopted by Tujia and Miao rural households in Chongqing, China. (2) The characteristics of the identified livelihood strategies are diversified and various in natural, financial, human and finance capital. (3) A number of livelihood capitals impact the way that household choose their livelihood strategies, but the livelihood capitals have no significant impact on the livelihood diversity. By detailed analysis of the characteristic of rural ethnic household livelihood strategy, especial livelihood diversity, the research enriched sustainable livelihood literature and provided useful information for policymakers and practitioners in designing effective programs for regional sustainable development and ecological protection.

Short-term air exposure to biofilm effectively inhibited methanogenesis in a MEC bioanode during 24 h batch operation. Compared to the anaerobic control, biofilm aeration increased H2 yield but presented little damage to the Coulombic efficiency.