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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.

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.MIROC.MIROC6.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 MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Dataset containing meiobenthos data for samples collected during the September 2008 Sesame Cruise in the North-West Black Sea on board of the Romanian R/V Mare Nigrum. Meiobenthos samples were collected in 5 stations, using a multicorer MARK II-400. The dataset includes 5 samples analysed for meiobenthos species composition, abundance and biomass.The entire washed sample was analyzed under the binocular stereomicroscope. Meiobenthic species were identified and enumerated; some meiobenthic species were identified and enumerated only at higher taxonomic level. Taxonomic identification was done at GEOECOMAR.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

Refrigerated road transport (RRT) vehicles are large users of energy, and reportedly have relatively high leakage of hydrofluorocarbon refrigerant gases, both of which contribute to global warming. The experience obtained from widespread research in leak reduction in stationary refrigeration systems can be instructive in combatting leakage in RRT systems, which has received less focus to date. This paper will take an integrated approach to develop and describe a preliminary model for sustainable RRT systems. It will first review lessons learned about refrigerant leakage in stationary systems in an effort to identify problematic/leak prone components common to transport refrigeration systems. This will then be followed by a survey of recent studies conducted in modelling transport refrigeration systems to advance energy efficiency. Initial results from the model illustrate the need to improve the efficiency of the refrigeration system, together with preventative maintenance of the box structure and refrigeration system as a whole.

This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)

The HEU MODERATE Building Stock Data provides information regarding the building stock for all EU27 member states at the national level (i.e., NUTS 0) considering 2020 as the reference year. Regarding the Service Sector, the data distinguishes the following subsectors: single-family houses, multifamily houses, and apartment blocks. Regarding the Service Sector, the data distinguishes the following subsectors: offices, trade, education, health, hotels and restaurants, and other non-residential buildings. Moreover, for each subsector, the data distinguishes the following construction periods: before 1945, 1945-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2010, and 2011-2020. For each building stock subsector and construction period, the data provide information regarding total values at the national level for: - Number of buildings - Number of dwellings - Number of dwellings according to ownership (i.e., owner occupied, rented, social housing) - Number of dwellings according to occupation (i.e., occupied, vacant, secondary houses) - Total constructed area - Total heated area - Total cooled area - Total final energy consumption for space heating and domestic hot water - Total final energy consumption for space cooling Moreover, the following average values for single building characteristics are provided: - Number of floors - Volume-to-surface ratio - Vertical area - Ground area - Window surface - U-values for the different building elements (roof, walls, windows, and floors) - Useful energy demand (ued) differentiating between space heating, domestic hot water, and space cooling - Final energy consumption (fed) differentiating between space heating, domestic hot water, and space cooling Finally, the data provide information about the prevalence of: - Building materials and methodology for the different building elements (roof, walls, windows, and floors) - Different systems used for space heating, domestic hot water, and space cooling The data is provided as a `csv` file (long format with all details and data source) and as an excel file (wide format with separate sheets for each country). Data and a complete description of the available fields can be found at https://github.com/MODERATE-Project/building-stock-analysis/tree/main/T3.2-static-analysis The dataset was obtained by combining information from European and national resources and the review of scientific literature. Data gaps were subsequently filled via statistical modeling.

Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC.Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

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.