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Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

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.CMIP.CAS.FGOALS-f3-L' 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.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Survey of Timisoara City residents conducted by the West University of Timisoara for the SMARTEES project between March and August 2020 (n=439). The survey was aimed at (1) understanding individual behaviours related to the environment and energy in general, and (2) assessing how people make decisions about energy efficiency measures in particular (i.e., perceptions about existing regional or national programmes aiming to improve the energy efficiency of homes through upgrades to the building fabric with a neighbourhood-scale heat network retrofit). It includes data about citizens' attitudes, behaviours and social networks. Files include the dataset in two formats: .csv and .sav. The questionnaire, a data dictionary and background and sampling details are also included.

By using a liquid desiccant ventilation system for dehumidification and an air-handling unit for cooling, the liquid desiccant cooling system (LDCS) system became a promising alternative for traditional technology. Solar thermal energy is suitable to deal with the heat requirement of LDCS in buildings, especially in the areas with abundant solar radiation. The energy saving of solar-assisted liquid desiccant air-conditioning system is significantly affected by various operation conditions, and multi-parameter optimization was necessary to improve the system applicability. In this paper, we investigated the impact of five main parameters on the system performance via self-developed system modelling, including the solution mass flow rate, concentration, cooling tower flow rate, and solar water flow rate and installation area of solar collector. A typical commercial building in Hong Kong was selected as a case study, which air-conditioning load was obtained by Energy-plus. The results indicated that the installation area of solar collector showed the greatest impact, and the effect of heating water flow rate was also important. The effect of desiccant flow rate was significant, but the influence of solution concentration was slight. Then, the multi-parameter optimization was conducted for obtaining a maximum annual electricity saving rate based on the Multi-Population Genetic Algorithm. The optimized installation area of solar collector was 72 m2, and the heating water flow rate was 0.66 kg/s. The optimized solution flow rate was 0.17 kg/s. The required cooling water flow rate was around 0.8 kg/s.

Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Dataset supporting publication: “A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol” (publication available for download: GEOFIT Zenodo) Datasets resultant from simulation of the integrated system into buildings. Conference paper presented in IBPSA-England Building Simulation and Optimisation Conference 2020 This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a Resistor-Capacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.

This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO2 and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO2) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport. The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions. The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr. A detailed description of the Monte Carlo simulation is presented in: Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019. N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care. NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.