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86 results for “atmospheric CO2”
Average Annual Atmospheric CO2 (ppm) with Average Regional Surface Downward Mass Flux of Carbon Dioxide Expressed as Carbon (molC_m²_yr)
<p>The fluctuation in both atmospheric CO2 and regional surface downward mass flux of carbon dioxide expressed as carbon. The annual data for both variable is taken from average monthly measured data.</p>
Northern and Southern Hemispheric regulation of atmospheric CO2 at millennial timescales
<p><span>Changes in deep-water ventilation and surface biogenic pump <span> </span>may have played a prominent role in the atmospheric C</span><span>O</span><sub><span>2</span></sub><span> variability. However, how these circulations and the biological pump have varied remains poorly understood, and further detailed analysis is needed, especially for interhemispheric comparison. Here, we present high-resolution proxy data on the variability of biological productivity, bottom water current speed, and upwelling changes for the abyssal northwestern Pacific Ocean spanning 20 to 60 kyr ago. We found that <span>enhanced surface productivity correlates with the timing of North Atlantic Heinrich Stadial and </span></span><span><span><span>interpret this as a result of a combination of increased nutrient supply to the euphotic zone and intensified westerly winds causing dust fertilization. </span></span></span><span>Based on our proxy data-model comparison, we infer enhanced deep-water ventilation in the Southern Ocean to drive <em>p</em>C</span><span>O<sub>2</sub></span><span> variability on millennial timescales in support of a bipolar carbon cycle seesaw between the Northern and Southern Hemisphere. Overall, our findings demonstrate that deep ocean carbon storage and degassing are modulated by atmosphere and ocean ventilation through the nonlinear interplay of the two hemispheres.</span></p>
Tropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability
<p>This repository includes the code and the processed climate data used to examine the long-term change in atmospheric CO<sub>2</sub> growth rate. The code will generate the main figures (Figure 1, 2 and 3) in the paper - Luo, X. & Keenan, T. F. (2022) Tropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability. Nature Communications, doi: 10.1038/s41467-022-28824-5.</p> <p>Please refer to the "Data Availability Statement" in the paper and cite the original sources if you need to use one or more datasets published here.</p> <p>There are three files in the repository: 1) CGR_analysis_input.mat is the processed data in matlab format to support the study; 2) master_figures_CGR_publish_v1.m is the main matlab routine that used to generate figures; 3) supporting code is a folder that contains third-party routines to support the main routine.</p>
Implications of emission sources and biosphere exchange on temporal variations of CO2 and δ13C using continuous atmospheric measurements at Shadnagar (India)
<p>The data contains daily atmospheric CO2 mixing ratios and 13C of CO2 over the Shadnagar region of India during November 2018 to October 2019.</p>
Carbon absorption capacity determines the response of algal competitiveness to elevated atmospheric CO2
<p><span>Although many studies have focused on </span><span>the effects of elevated </span><span>atmospheric</span> <span>CO<sub>2</sub></span><span> on algal growth</span><span>, few studies have demonstrated how </span><span>CO2</span><span> interacts with carbon absorption capacity to determine the algal competition at the population level</span><span>.</span><span> We conducted a pairwise competition experiment of <em>Phormidium</em></span><span><em> </em>sp., <em>Scenedesmus quadricauda</em>, <em>Chlorella vulgaris, </em>and <em>Synedra ulna</em>. The results showed that when the </span><span>CO<sub>2</sub></span><span> concentration increases from 400 to 760 ppm, the competitiveness of <em>S. quadricauda</em> increased, the competitiveness of <em>Phormidium </em>sp. and <em>C. vulgaris</em> decreased, and the competitiveness of <em>S. ulna</em> was always the lowest. we constructed a model</span><span> to explore whether interspecific differences in affinity and flux rate for CO<sub>2</sub> and HCO<sub>3</sub><sup>−</sup> could explain changes of </span><span>competitiveness</span><span> between algae species along the gradient of atmospheric </span><span>CO<sub>2</sub></span><span> concentration.</span><span> Affinity and flux rate are the capture capacity and transport capacity of substrate, respectively, which are inversely proportional to each other. Low resource concentration is beneficial to the growth and reproduction of algae with high affinity. The simulation results showed that when the atmospheric </span><span>CO<sub>2</sub></span><span> concentration was low</span><span>, </span><span>species with high affinity for both </span><span>CO<sub>2</sub> and </span><span>HCO<sub>3</sub><sup>−</sup> (HCHH) had the highest </span><span>competitiveness, followed by the species with high affinity for </span><span>CO<sub>2</sub> and low affinity for </span><span>HCO<sub>3</sub><sup>−</sup> (HCLH), </span><span>the species with low affinity for </span><span>CO<sub>2</sub> and high affinity for </span><span>HCO<sub>3</sub><sup>−</sup> (LCHH) and the species </span><span>with low affinity for both </span><span>CO<sub>2</sub> and </span><span>HCO<sub>3</sub><sup>−</sup> (LCLH); when the CO2 concentration was high, the species were ranked according to the competitive ability: LCHH > LCLH > HCHH > HCLH</span><span>. Thus, with the increase of atmospheric </span><span>CO<sub>2</sub> concentration, the competitive advantage changed from </span><span>HCHH</span> <span>species to </span><span>LCHH</span> <span>species.</span><span> These results indicate the important species types contributing to water bloom under the background of increasing global atmospheric </span><span>CO<sub>2</sub>, highlighting the importance of carbon absorption characteristics in understanding, predicting and regulating population dynamics and community composition of algae.</span></p>
Selected CO2 Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub>data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Speciation data for "Genesis of a CO2-rich and H2O-depleted atmosphere from the Earth's early global magma ocean"
<p>Here we present the speciation statistics for volatile-bearing pyrolite melts. For computational details and nomenclature, please refer to the manuscript, "Genesis of a CO2-rich and H2O-depleted atmosphere from the Earth’s early global magma ocean" by N.V. Solomatova and R. Caracas.</p> <p>The data is organized by composition and temperature.</p> <p>File names contain the lattice parameter (e.g., a16.0) in Angstroms, indicating that the cell is 16x16x16 Angstroms in size, for example. The corresponding volumes and densities are tabulated in each folder (e.g., "4CO_4000K_densities.dat"). </p> <p>The first column of each *stat.dat file contains the species type. Long species (e.g., Na_1Ca_2Fe_4Mg_30Al_3Si_24O_93C_4) typically represent the silicate melt and short species (e.g., O_1C_1 and O_2C_1) represent the vapor phase. Underscores represent subscripts (e.g., O_2C_1 denotes a CO2 molecule). The second column is the lifetime of the species in femtoseconds. The third column is the percent of the total. The fourth column is the number of elements in each species (e.g., O_1C_1 has 2 elements and O_2C_1 has 3 elements). Note that the majority of the "H_1" species represent free protons within the melt phase.</p>
Trajectories and metadata for Bach et al.: 'Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO2 removal'
<p>Trajectory and metadata files from a Southern Ocean surface particle release experiment using Connectivity Modeling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>) run offline in the MOM01 model (Stewart et al., 2017; Spence et al., 2017; Morrison et al., 2020), a global 0.1° ocean sea-ice model, based on version 5 of the Modular Ocean Model (MOM) code (mom-ocean.github.io) (Griffies, 2012).</p> <p> Here particle trajectories are are split into 28 netcdf files (numbered traj_file_01.nc through traj_file_28.nc), and uploaded altogether as a single .tar.gz compressed file. Metadata files are also uploaded containing information on the CMS setup and release locations.</p> <p>Netcdf files contain particle trajectory positions, along with temperature, salinity, status in or out of the mixed layer, release date, and particle exit status (see <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a> for details on trajectory files). Other CMS setup files included in each release directory are nest_1.nml, runconf.list, ibm.list, and releaseFile_matchtransport. These input files contain information needed to reproduce the experiment using the MOM01 model output, and are explained in detail in the <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a>.</p> <p> </p> <p>Citation of associated paper: Bach, L., V. Tamsitt, K. Baldry, J. McGee, E. Laurenceau-Cornec, R. Strzepek, Y. Xie, P. W. Boyd, 2021. Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO<sub>2</sub> removal, <em>submitted </em></p> <p> </p> <p>References:</p> <p>Griffies, S. M. (2012). Elements of the modular ocean model (MOM). <em>GFDL Ocean Group Tech. Rep</em>, 7(620), 47.</p> <p> Morrison, A. K., Hogg, A. M., England, M. H., & Spence, P. (2020). Warm Circumpolar Deep Water transport towards Antarctic driven by local dense water export in canyons. <em>Science Advances</em>, 6(18), eaav2516.</p> <p> Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, 42, 47-54.</p> <p>Spence, P., Holmes, R. M., Hogg, A. M., Gries, S. M., Stewart, K. D., & England, M. H. (2017). Localized rapid warming of West Antarctic subsurface waters by remote winds. <em>Nature Climate Change</em>, 7(8), 595.</p> <p>Stewart, K., Hogg, A. M., Gries, S., Heerdegen, A., Ward, M., Spence, P., & England, M. H. (2017). Vertical resolution of baroclinic modes in global ocean models. <em>Ocean Modelling</em>, 113, 50-65.</p>
Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
<p>This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO<sub>2</sub> Over the Arctic Ocean. <em>Journal of Geophysical Research: Atmospheres</em>. In Review.</p>
Response of atmospheric CO2 changes to the abyssal Pacific overturning during the last glacial cycle
<p>Despite its critical role in regulating the global climate and carbon cycle, the evolution of Pacific abyssal circulation has not been fully deciphered during the last glacial cycle. Using the oxygenation and benthic δ<sup>13</sup>C gradient records, tracers that are sensitive to ocean circulation rates, we establish the abyssal Pacific overturning conditions since ~90 ka. Our results indicate that the intensity of abyssal Pacific overturning is closely related to atmospheric CO<sub>2</sub>, with accelerated overturning during steps of CO<sub>2</sub> rise. Those depict a potential pathway of deep carbon dioxide into the atmosphere and highlight the critical roles of deep Pacific circulation in the global carbon cycle.</p>
Rapid shoaling of Aragonite Saturation Horizon (ASH) in the Indian Ocean: Influence of accumulation of anthropogenic CO2 and atmospheric pollutants
<p>This data set contains the various cruise data of Aragonite saturation depth in the Northern Indian Ocean. </p> <p>The shallowest aragonite saturation horizon (ASH) was observed in the Bay of Bengal (BoB; 219±10 m) within the tropical Indian Ocean. The ASH shoaled at the rate of 6.3±5 and 4.4±3 m y<sup>-1</sup> in the past two and half decades in the BoB and Arabian Sea respectively. As a result, an increase in total alkalinity (TA) was observed at the rate of 0.5±0.3 and 0.25±0.2 mmol kg<sup>-1</sup> y<sup>-1</sup> at the depth of ASH in the BoB, and Arabian Sea respectively. The rapid shoaling of ASH in the BoB than the Arabian Sea may result from the higher accumulation of anthropogenic CO<sub>2</sub> due to the freshening of the upper ocean associated with an increase in river discharge/ precipitation and deposition of atmospheric pollutants leading to corrosion of the aragonite skeletal material. Under a business-as-usual scenario, aragonite-secreting organisms may not survive by the middle of this century in the BoB.</p>
Full-coverage, 1-km atmospheric carbon dioxide (CO2) dataset across China
<p>We employed an enhanced regression-based machine learning model to reconstruct full-coverage daily atmospheric CO2 concentrations in China from 2015 to 2020 at a 0.01° spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO2 (XCO2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively. </p> <p> </p> <p>The annual mean and monthly mean data are archieved in Geotiff format. If you want to use this dataset, please cite the following publication. If you want to more data (e.g., daily XCO2 estimates), please contact us via qqhe@whut.edu.cn.</p> <p>--He, Q., Ye, T., Chen, X., Dong, H., Wang, W., Liang, Y., & Li, Y. (2023). Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution. <em>Journal of Cleaner Production</em>, 139290. [<a href="https://doi.org/10.1016/j.jclepro.2023.139290">url</a>]</p> <p> </p> <p>If you want daily data, please go to <a href="13623590">10.5281/zenodo.13623590</a>. If you have any questions or suggestions, please contact us via qqhe@whut.edu.cn.</p> <p> </p> <p>If you want more atmospheric-related datasets, e.g., full-coverage, 1-km AOD and PM2.5 datasets over China, please go to <a href="https://doi.org/10.5281/zenodo.7229348">10.5281/zenodo.7229348.</a></p>
Data from: Nutrient availability and atmospheric CO2 partial pressure modulate the effects of nutrient heterogeneity on the size structure of populations in grassland species
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Data from: Rising atmospheric CO2 is reducing the protein concentration of a floral pollen source essential for North American bees
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Carbon absorption capacity determines the response of algal competitiveness to elevated atmospheric CO2
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Contrasting effects of Miocene and Anthropocene levels of atmospheric CO2 on silicon accumulation in a model grass
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Water availability drives fine root dynamics in a Eucalyptus woodland under elevated atmospheric CO2 concentration
<p>Fine roots are a key component of carbon and nutrient dynamics in forest ecosystems. Rising atmospheric [CO<sub>2</sub>] (eCO<sub>2</sub>) is likely to alter the production and activity of fine roots, with important consequences for forest carbon storage. Yet empirical evidence of the role of eCO<sub>2</sub> in driving root dynamics in low-nutrient forested ecosystems is limited, particularly for grassy woodlands, an ecosystem type of global importance.</p> <p>We sampled fine roots across seasons over a two-year period to examine the effects of eCO<sub>2</sub> on their biomass, production, turnover and functional traits in a native mature grassy <i>Eucalyptus</i> woodland in eastern Australia (EucFACE).</p> <p>Fine root biomass, production and turnover varied greatly through time, increasing as soil water content declined. Despite a lack of persistent effects of eCO<sub>2</sub> on fine root biomass, production or turnover across the two-year sampling period, we found enhanced production pulses under eCO<sub>2</sub> between 10-30 cm soil depth. These eCO<sub>2</sub>-driven production pulses were associated with large changes in abiotic conditions. In addition, eCO<sub>2</sub> led to greater carbon and phosphorus concentrations in fine roots and increased root diameter, but no detectable effects on other morphological traits.</p> <p>Synthesis. We found minor quantitative effects of eCO<sub>2</sub> on fine root biomass dynamics that were largely driven by temporal variations in soil water availability. Our results suggest that in this mature grassy woodland, and perhaps also in other similar forested ecosystem types characterized by low phosphorus content in the soil, eCO<sub>2</sub> effects are small and transient. This suggests limited belowground fine root productivity responses to rising atmospheric CO<sub>2</sub> concentrations and, thus, perhaps also a limited ability of these systems to mitigate climate change through belowground mechanisms.</p>
Data from: Impacts of silicon-based grass defences across trophic levels under both current and future atmospheric CO2 scenarios
Silicon (Si) has important functional roles in plants, including resistance against herbivores. Environmental change, such as increasing atmospheric concentrations of CO2, may alter allocation to Si defences in grasses, potentially changing the feeding behaviour and performance of herbivores, which may in turn impact on higher trophic groups. Using Si-treated and untreated grasses (Phalaris aquatica) maintained under ambient (400 ppm) and elevated (640 and 800 ppm) CO2 concentrations, we show that Si reduced feeding by crickets (Acheta domesticus), resulting in smaller body mass. This, in turn, reduced predatory behaviour by praying mantids (Tenodera sinensis), which consequently performed worse. Despite elevated CO2 decreasing Si concentrations in P. aquatica, this reduction was not large enough to affect the feeding behaviour of crickets or their predator. Our results suggest that Si-based defences in plants have adverse impacts on both primary and secondary trophic taxa, and these are not likely to decline under future climate change scenarios.
Full-coverage atmospheric CO2 reconstruction data in China from 2010 to 2019
<p>This monthly atmospheric CO2 dataset was reconstructed from GOSAT XCO2 retrievals using a spatiotemporal kriging method. We employed this dataset to investiage the spatiotemporal variation in atmospheric CO2 and its influencing factors. Please the publication as below:</p><p>--Chen, X., He, Q., Ye, T., Liang, Y., & Li, Y. (2023). Decoding spatiotemporal dynamics in atmospheric CO2 in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis. <i>Science of The Total Environment</i>, 167917.</p><p> </p><p>We also share other atmopsheric reconstruction datasets:</p><p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p><p>For full-coverage, 1-km, CO2 data in China, please go to <a href="https://zenodo.org/doi/10.5281/zenodo.10022904">10.5281/zenodo.10022904. </a>This dataset was reconstructed based on OCO-2 XCO2 retrievals and machine learning algorithm.</p><p>For full-covereage, 1-km, PM2.5 data in China, please go to <a href="https://zenodo.org/doi/10.5281/zenodo.8437234">10.5281/zenodo.8437234 or </a><a href="https://zenodo.org/record/8347128">10.5281/zenodo.8347128.</a></p>
Flowfield Properties for Calculating Electrical Conductivity in a Rarefied CO2 Atmosphere with DSMC
<p>This dataset provides flowfield quantities necessary to calculate the electrical conductivity in the flowfield around an entry vehicle in a rarefied CO2 atmosphere. The DSMC data was generated using the Stochastic PArallel Rarefied-gas Time-accurate Analyzer. The freestream conditions cover velocities from 6 - 8.5 km/s and altitudes from 80 - 110 km at Mars. The vehicle geometry is a 70 deg spherecone with varying diameters between 5 and 10 m.</p>
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