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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.CMIP.CMCC.CMCC-CM2-SR5.historical' 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 CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.

This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, and N_FEATURES number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED, REFERENCE, FID with the feature ID, and geom in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.

Contrasting physiological mortality with predator-induced mortality is of tremendous importance for the population dynamics of many organisms but is difficult to assess. I performed a meta-analysis using planktonic ciliates as model organisms to estimate the maximum physiological mortality rates (δmax) across pelagic ecosystems in relation to environmental and biotic factors. Data were compiled from published numerical response (NR) experiments and experimentally determined rates of decline (ROD). Variables reported are ciliate species and order, ciliate specific growth rates (rmax), prey species, temperature, habitat (marine vs freshwater), the coefficients of the numerical response experiments, and reported or calculated ciliate mortality rates. The median δmax of planktonic ciliates was 0.62 d−1 and did not differ between marine and freshwater species. Maximum ciliate mortality rates were species-specific and affected by their rmax, cell volume, and ability to encyst. Cyst-forming species had, on average, higher δmax than species unable to encyst. Maximum mortality rates of ciliates were positively related to rmax but appeared unaffected by temperature. I conclude that (i) in the ocean, physiological mortality is more critical for controlling ciliate population size than ciliate losses imposed by microcrustacean predation, but (ii) in many lakes, the opposite holds; (iii) cyst-formation is an effective ciliate trait to cope with the high mortality of motile cells upon starvation. The lack of a temperature effect on δmax deserves further study; if correct, planktonic ciliates may take advantage of rising ocean and lake temperatures, with important implications for the pelagic food web. I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The search terms were “growth (rate)” or “numerical response” in combination with “ciliate*” to search for numerical response experiments and “starvation” or “starved” in combination with “ciliate*” to search for mortality experiments. In addition, I searched the literature cited in these publications for further datasets. I considered only planktonic ciliates. When studies did not report all parameters of the NR curve, the data were extracted from figures with DataThief III or WebPlotDigitizer (Version 4.6) and fitted with a modified Michaelis-Menten equation that included the threshold prey concentration (P’) as an additional parameter. Mortality rates obtained by ROD experiments used the δmax reported in the respective study or calculated δmax from the maximum rate of decline after digitizing the data from the original curves, as described above. The literature search yielded δmax reported from 41 studies investigating 56 species or strains in 81 NR experiments and 19 ROD experiments. The final dataset (n = 77) included 37 studies and 48 species. I analyzed the dataset using the R Statistical Software using the packages lme4, lmerTest, AICcmodavg, and MuMIn. # Physiological mortality rates of planktonic ciliates ## Description of the Data and file structure I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The main dataset containing available experimental studies reporting ciliate species, experimental temperature, prey species, ciliate maximum growth rates, ciliate cell volumes, habitat of ciliate isolation, method of study and reported or calculated ciliate mortality rates are reported in the 'Dataset_v2.xlsx' file. This is the main document. Missing data codes: N.A. = not available; n/a = not applicable. More details about each column of the main document can be found in the 'Units_table.xlsx' file. Details on the references - i.e. authors, publication year, title, journal/book, volume and page/article numbers - used to compile this dataset can be found in 'References.xlsx'. ## Sharing/access Information The individual data were derived mainly from the ISI Web of Science. The data compilation is novel. Excel, R

Editorial Complex Optimization and Simulation in Power Systems

This paper evaluated the environmental effects of socially responsible investments (SRIs) in European countries and analyzed the differentiation between them in terms of SRIs and selected features in the environmental dimension. The first section of the paper discusses contemporary trends in Europe and in certain European countries, whilst the second compares SR environmental investments and environmental factors in selected European countries from a multidimensional perspective. The aim of the study was to identify and evaluate these trends as well as to find similarities and differences between European countries, and subsequently to indicate groups of countries with similar approaches to pro-ecological investments. In order to solve the problem, descriptive and multidimensional statistical methods were used, namely correspondence analysis (CA). Although the research results clearly revealed upward tendencies in the volume of SR environmental investments in the analyzed period, they nonetheless represent a relatively low share in the total number of socially responsible investments. The overall growth in SRIs in Europe may have resulted from the more intense activities of policymakers in some countries as a consequence of concluding agreements reached during the 21st Conference of the Parties (COP21) in 2015. The results of the study also revealed no significant correlations between SR environmental investments and environmental variables among the European countries analyzed; hence, there is no substantial evidence that investors’ assets contribute to the improvement of the environment.

The complex interaction between city and climate crisis is converting design-based disciplines from deterministic to flexible approaches. In this regard, Decision-Making Under Deep Uncertainty (DMDU) methods and operational strategies can be valuable support mechanisms to cope with the emerging climate fragilities of urban systems. In light of recent advances in the field of adaptive approaches, this paper discusses key concepts, current limitations and the potential to introduce the DMDU in the method and practices of regenerative design. Our critical discussion aims to restore the designer’s role within the DMDU and to reduce current and future climate fragilities in European cities.

Periods of no activity between 02.00 and 10.00 and lasting for up 7 h 40 min are reported for two captive Crocidura flavescens (I. Geoffroy, 1827). During on of the these periods, the weight specific energy requirement of the shrew dropped to 25.1% of the usual, indicating a period of spontaneous torpor. Nest-box temperatures also drop during the periods of prolonged inactivity suggesting a lowered body temperature. Data for a younger animal suggest shorter periods of torpor. The timing of the torpor spans the coldest period of the day when the animal would expend most energy maintaining a constans body temperature.