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This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019 Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de). Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries. File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019). Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability of CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of solar and wind power limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability of CCS in combination with bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1 0$/tCO2 (baseline) xx2 10$/tCO2 xx3 30$/tCO2 xx4 50$/tCO2 xx5 100$/tCO2 xx6 200$/tCO2 xx7 500$/tCO2 xx8 40$/tCO2 xx9 20$/tCO2 xx0 5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price). [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

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.CAMS.CAMS-CSM1-0.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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting" Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting"

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.DKRZ.MPI-ESM1-2-HR.ssp126' 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 MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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.