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This is the dataset for our research on assessing CO2 emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and Reservoirs_1980s, Part 2: CO2 Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these data can be found from the 'README' text file.

Supplementary Information is available for this paper.

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.

Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software

Background and Aims: This study aims at evaluating the ease of use of the new calorimeter for the measurement of energy expenditure (EE) in intensive care unit (ICU) patients. EE in ICU patients is highly variable depending on the severity of the disease and treatments. Clinicians need to measure EE by indirect calorimetry (IC) to optimize nutritional support for the better clinical outcome. However, indirect calorimeters available on the market have insufficient accuracy for clinical and research use. Difficulties of handling and interpretation of results often limit IC in ICU patients. An accurate, easy-to-use calorimeter has been developed to meet these needs. The Study Device: The new calorimeter (Quark RMR 2.0, COSMED) is capable of IC measurements in mechanically ventilated patients without warm-up and limited calibration. The disposable in-line pneumotach flow meter and direct sampling of respiratory gas from the ventilator circuit enables the accurate measurement of oxygen consumption volume (VO2) and CO2 production volume (VCO2) to derive the energy expenditure. The software interface to manage the device and the collected data provides easy-to-use, user-friendly interface. This calorimeter bears an European Commission (EC) Conformity Mark, and will be used in the way it is intended to be used as described in the instruction manual. Currently used indirect calorimeters at each study center will be used as the comparator. This study will evaluate the ease of use of the new calorimeter (Quark RMR 2.0 (COSMED, Italy)) in intensive care unit (ICU) patients compared to currently used calorimeters (i.e. Quark RMR 1.0(COSMED, Italy) or Deltatrac Metabolic Monitor (Datex, Finland)), as well as the stability and the feasibility of the measurements in various clinically relevant situations. Time needed to prepare and start indirect calorimetry (IC) measurement will be compared as the measure of the ease of use of the calorimeter.

The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.

Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 0 degree inflow wind direction angle (Casename PDk 0) Included Python files for loading and visualizing the data. Use the plot_*.py files. Further information, including description of the case and dataset can be found in the deliverable report at: https://cordis.europa.eu/project/id/727680/results "Database for reference wind farms part 2: windfarm simulations"