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36 results for “atmospheric composition”
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
iNRACM: Incorporating 15N into the Regional Atmospheric Chemistry Mechanism (RACM) for assessing the role photochemistry plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate
<p><sup>15</sup>N compounds and reactions were incorporated into Regional Atmospheric Chemistry Mechanism (RACM), based on recent experimental or calculated values of isotope fractionation factors (α), to simulate δ<sup>15</sup>N values in NO<sub>y</sub> compounds.</p>
GHOST: A globally harmonised dataset of surface atmospheric composition measurements
<div> <div>GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.</div> <br> <div>The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided. </div> <h3>Data Access </h3> <div>The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST. </div> <div> </div> <div>The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files.</div> <div> </div> <div>Each network .zip file can be decompressed via the following syntax:<br><em>unzip [network].zip</em></div> <div> </div> <div>Component tar.xz files can be decompressed via the following syntax:<br><em>tar -xf [component].tar.xz</em></div> <h3>How to Use</h3> <p>Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: <em>https://doi.org/10.5194/essd-2023-397</em></p> <div>If you have any other doubts of queries regarding the dataset, please email: <em>dene.bowdalo@bsc.es</em></div> <h3>How to Cite</h3> <p>If you plan to use this work please kindly cite both this dataset and the describing publication:</p> </div> <div><br> <div><em>Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024.</em></div> <br> <div><em>Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024.</em></div> <h3>Acknowledgements</h3> <div>We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements.</div> <div> </div> <div>We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115).</div> <div> </div> <div>The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. </div> <div> </div> <div>The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550).</div> </div>
Sonora 2018: Cloud-free, solar composition, solar C/O substellar atmosphere models and spectra
<p>These models for non-irradiated, substellar mass objects belong to the Sonora 2018 model series, to be described in Marley et al., currently in preparation for submission to Astrophysical Journal.</p> <p>This particular set of model atmosphere structures and associated spectra are for cloudless, solar metallicity, solar C/O ratio objects (relative to Lodders (2010) abundances) with <span class="math-tex">\(3.25 \le \log g \le 5.5\)</span> and <span class="math-tex">\(200 \le T_{\rm eff} \le 2400\,\rm K.\)</span> "Rainout" chemical equilibrium is assumed. Spectra describe the emergent flux from the top of the atmosphere of the object, units in file header. Profiles give temperature and pressure through the model radiative-convective equilibrium structure. Volume mixing ratios for a few gasses of interest are also tabulated. Additional gas species are included in the calculation.</p> <p>Filename specifies <span class="math-tex">\(T_{\rm eff}\)</span> and gravity (in mks units). </p> <p>"co1.0" in version and spectra header nomenclature refers to 1.0 times the solar C/O ratio. Y is the He mass fraction. f_hole is a cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>The two "flux table" files give fluxes, in mJy, for various standard filter bandpasses of interest for each model case.</p> <p> </p> <p> </p> <p> </p>
Atmospheric clumped O2 isotope composition simulation data and analysis scripts from EMAC/aMC models
<p>This publication contains source code, data and analysis scripts/results of the simulations presented in the following manuscript:</p> <blockquote> <p>Laskar, A.H., G.A. Adnew, S.S. Gromov, R. Peethambaran, B. Steil, J. Lelieveld, T. Blunier and T. Röckmann (2022). "Large variations in atmospheric oxidants and temperature during the Holocene" (in review)</p> </blockquote> <p> </p> <p><strong>EMAC simulations analysis</strong></p> <p>The analysis contains integrals of species burdens and other atmospheric physicochemical parameters obtained with the clumped isotopes of oxygen (CIO)-enabled ECHAM/MESSy Atmospheric Chemistry model (EMAC, see <a href="https://www.messy-interface.org">MESSy consortium website</a> for more information) model in various climate states. Simulations were performed in 2021–2022 at the <a href="https://www.dkrz.de">German Climate Computing Centre</a> (DKRZ) with the support of the <a href="https://www.palmod.de">PalMod project</a>.</p> <p>Analysis data is stored in human/machine-readable file <code>D36-EMAC-analysis.dat</code>, please refer to its header for variables description, etc.</p> <p>Additional (to those presented in the manuscript) analysis plots from EMAC data analysis are available in <code>D36-EMAC-analysis.vsz</code> (see the hardcopy in <code>D36-EMAC-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software.</p> <p> </p> <p><strong>2BM/MC (two-box Monte-Carlo) model code, simulation data and analysis</strong></p> <p>2BM/MC code/simulation setup is implemented within the advanced Monte-Carlo framework (aMC) and is available in the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO">respective repository</a>. A copy of the source code used to perform simulations is provided here (see <code>aMC-vpCIO.tar.gz</code> archive).</p> <p>2BM/MC output is stored in the <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF format</a> (ver. 4) and can be read in by any compatible software. The output contains probe statistics (reference <em>probed</em> distributions of the variables) in <code>vpCIO-probe_stat-*.nc</code> and resulting statistics (distributions <em>matching</em> given criteria, i.e. changes to the Δ36 signature vs. PD conditions) in <code>vpCIO-delta-*.nc</code> files, respectively.</p> <p>We use <a href="https://ferret.pmel.noaa.gov">NOAA Ferret</a> software to derive additional statistics of the third parameter (viz. average STE (<em>S</em>) changes) over the obtained 2D frequency histograms of other parameters (viz. changes to equilibration rate (<em>Req)</em> and temperature (<em>Teq</em>)). The scripts exemplifying this calculation are presented in <code>D36-vpCIO-analysis__proc*</code> files, which output results/overview plots in <code>vpCIO-delta-*__proc.nc</code> and <code>vpCIO-delta-*.gif</code> files.</p> <p>The analysis of the 2BM/MC simulation is available in <code>D36-vpCIO-analysis.vsz</code> script (see the hardcopy in <code>D36-vpCIO-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software. Note that some plots require the abovementioned third-parameter statistics as input.</p> <p><strong>Performing simulations with 2BM/MC</strong></p> <p>In order to perform simulations (e.g. with altered parameters), please follow the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO#integrating-your-code-building-executing">respective guide</a> for and build the <code>aMC-vpCIO</code> model. A typical sequence of shell commands to build and run 2BM/MC (which is referred to as <code>vpCIO</code> generic model within the <code>aMC</code>) is:</p> <pre><code># clone the distribution and check-out `vpCIO` branch or particular commit referenced in the repository history [user@pc]/~> git clone https://gitlab.com/sergey.gromov/amc.git [user@pc]/~> cd amc [user@pc]/~/amc> git checkout vpCIO # or unpack the source code available in this publication: [user@pc]/~> tar -xvf `aMC-vpCIO.tar.gz` [user@pc]/~> cd amc # build the aMC/vpCIO model executable # (note that you need at least a GCC or Intel compiler suite and respective netCDF v.4 library Fortran interface available in your environment): [user@pc]/~/amc> make vpCIO # adjust model setup (see the `vpCIO/amc.nml` namelist) ... # perform simulation [user@pc]/~/amc> cd vpCIO [user@pc]/~/amc/vpCIO> ./xamc # calculate additional statistics/produce overview with NOAA Ferret: [user@pc]/~/amc/vpCIO> ferret -gif -script D36-vpCIO-analysis__proc.jnl MH [user@pc]/~/amc/vpCIO> ./D36-vpCIO-analysis__proc</code></pre> <p>Note that output files contain the build timestamp and repository commit hash for the code used in the simulation, e.g.:</p> <pre><code>[user@pc]/~/amc/vpCIO> ncdump -h ./vpCIO-delta-dMH.nc | grep 'build' :build = "vpCIO@https://gitlab.com/sergey.gromov/amc__aMC_v1.9-110-g2566229@2022-12-09T16:43:12+01:00__built@2022-12-09T16:48:03+01:00__<user>@<email.com>" ;</code></pre> <p> </p> <p>Please contact Sergey Gromov ( sergey.gromov (at) mpic.de ) for additional information and access to the original experiment data.</p> <p> </p>
"Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets" Data Repository
<p>This repository contains the data files, analysis Jupyter notebooks and figures from Thompson et al. 2023 "Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets"</p>
WRF 3.5km North Atlantic August - October 2020 (TC tracks and atmospheric composites data)
<p>This zip file contains simulated TC tracks and some atmospheric composites for August to October 2020.</p> <p>- TC tracks information: timing, intensity, and location</p> <p>- Composites: vertical wind shear (200 - 850 hPa), potential intensity, 850 hPa absolute vorticity, and 700 hPa specific humidity.</p>
The oxygen isotope compositions of large numbers of small cosmic spherules: Implications for their sources and the isotopic composition of the upper atmosphere
<p>Cosmic spherules are micrometeorites that melt at high altitude as they enter Earth's atmosphere and their oxygen isotope compositions are partially or completely inherited from the upper atmosphere, depending on the heating experienced and the nature of their precursor materials. In this study, the <b>three oxygen isotope</b> compositions of 137 <b>cosmic spherules are</b> determined using 277 in-situ analyses by ion probe. Particles of each different type of cosmic spherule (scoriaceous, porphyritic, cryptocrystalline, barred, glass, calcium aluminium and titanium (CAT), G-type and I-type) in the diameter range ~52–480mm were analysed. The results confirm that the <b>three</b> oxygen isotope compositions of melted <b>micrometeorites</b> reflect a combination of their precursor composition, exchange with the atmosphere and mass fractionation owing to evaporation during entry heating. The data <b>appear to </b>reveal an increase in average δ<sup>18</sup>O values of silicate dominated (S-type) spherules in the series scoriaceous<porphyritic<barred<glass<CAT spherules (~20, 22, 25, 26 and 50‰) that is consistent with the evolution of oxygen isotopes by mass fractionation owing to increased average entry heating. The trend of δ<sup>17,18</sup>O is broadly parallel to the terrestrial fractionation line and thus suggests mass fractionation dominates changes in isotopic composition, with atmospheric exchange a less significant effect. The D<sup>17</sup>O values of spherules, therefore, are mostly preserved and suggest that ~80% of particles are related to the carbonaceous chondrites (CC) and are probably samples of C-type asteroids. The genetic relationships between different S-types can also be determined with scoriaceous, barred and cryptocrystalline spherules mostly having low D<sup>17</sup>O values <b>(≤0‰)</b> suggesting they are mainly derived from CC-like sources, whilst porphyritic mostly have positive D<sup>17</sup>O <b>(>0‰)</b> suggesting they are largely from ordinary chondrite (OC)-like sources related to S(IV)-type asteroids. Glassy and CAT-spherules have D<sup>17</sup>O values <b>indicating</b> they formed by intense entry heating of both CC and OC-like materials. I-type cosmic spherules have a narrow range of δ<sup>17</sup>O (~20–25‰) and δ<sup>18</sup>O (~38–48‰) values, with D<sup>17</sup>O (~0‰) <b>suggesting</b> their oxygen is obtained entirely from the Earth's atmosphere, albeit with significant mass fractionation owing to evaporation during entry heating. The observed range of δ<sup>18</sup>O with the size is suggested here to reflect entry angle with high values representing enhanced heating at high angle. Finally, G-type <b>cosmic spherules</b> have unexpected isotopic compositions suggesting little mass-fractionation from a CC-like source and are suggested to have sulphide-silicate precursors with relatively low melting temperatures. The results of this study provide a <b>vital assessment</b> of the <b>wider</b> population of extraterrestrial dust arriving <b>at the</b> Earth.</p>
NCAR/ACOM FTIR Atmospheric Composition Dataset
<p>The NCAR/ACOM Optical Techniques Project operates three high spectral resolution, solar viewing FTIR instruments at Thule, Greenland (76.5ºN, 291.3ºE, 225 masl), Boulder Colorado (40.03ºN, 254.7ºE, 1612 masl) and Mauna Loa Observatory Hawaii (19.5ºN, 204.4ºE, 3396 masl). These instruments operate within the framework of the Network for the Detection of Atmospheric Composition change (NDACC). The raw data are recorded autonomously and downloaded to ACOM servers daily. They are processed at ACOM and produce vertical profiles of several trace atmospheric constituents: O3, HNO3, HCl, HF, CO, N2O, CH4, HCN, C2H6, OCS, H2CO, H2O and ClONO2. These data constitute a long-term reference dataset for tropospheric and stratospheric chemistry investigations.</p> <p>The data, along with metadata and full error analysis are saved to GEOMS conforming HDF files. Per our agreement with NDACC and our sponsor NASA, they are uploaded bi-yearly to the NDACC data handling facility (DHF) at www.ndacc.org. For select species, HDF files are uploaded on a near-real-time basis for the CAMS27 project which use the data for realtime air quality forecasting. Once these data are uploaded they are publicly available. The NDACC has implemented Creative Commons licensing designations for data at the DHF. These data maintain a CC0 license.</p>
dataset for: Carbonate content and stable isotopic composition of atmospheric aerosol carbon in the Canadian High Arctic
<p>Dataset related to publication of the same title in <a href="https://www.atmospheric-chemistry-and-physics.net/">Atmospheric Chemistry and Physics</a></p>
Spring tropical cyclones modulate near-surface isotopic compositions of atmospheric water vapour at Kathmandu, Nepal
<p>All these data have been published in a ACP paper. If you use these data in any conditions, please cite the following publicaiton: Adhikari, N., Gao, J., Zhao, A., Xu, T., Chen, M., Niu, X., and Yao, T.: Spring tropical cyclones modulate near-surface isotopic compositions of atmospheric water vapour at Kathmandu, Nepal, EGUsphere, https://doi.org/10.5194/egusphere-2023-2186, 2023.</p>
The data for "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions"
<p>Supporting code and data for Salazar & Wordsworth, 2024 </p> <p><strong>Description of Jupyter Notebook files:</strong></p> <p>(Figure_X.ipynb) Contains code for recreating all figures appearing in S&W, 2024 (labeled by figure number, X)</p> <p>(ExoTides_analytic.ipynb) Contains functions from the analytic model for calculating torque and rotation states as a function of orbital and atmospheric paramters</p> <p>(LMDZ_Processing.ipynb) Code for processing raw LMDZ data, including centering at the substellar point, spherical harmonic decomposition, and calculation of torque, amplitude, and lag</p> <p>(ExoTides_int.ipynb) Demonstrates solutions by integrating Equation (7) numerically (not done in paper). Also includes a comparison with the analytic model</p> <p><strong>Description of Numpy array files:</strong></p> <p>These files contain numpy arrays of the torque (im_q), amplitude (amp), lag angle (lag), and parameter values (list) for each of the GCM sensitivity tests performed in S&W, 2024. These files were created using the LMDZ_Processing.ipynb file, which takes raw GCM output and outputs the torque information. Full details are available in the publication.</p> <p>The beginning of each .npy file name signifies which sensitivity test the file is (ex: taulw indicates the longwave optical depth sensitivity test). The Figure_2.ipynb file shows how these .npy files are used. </p> <p><strong>Descrition of .csv file:</strong></p> <p>The nasa_exoplanet.csv file contains output from the NASA Exoplanet Archive (DOI: 10.26133/NEA12) and is used in the creation of Figure 7. </p> <p> </p>
The data for "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions"
<p>This repo contains the processed LMD Generic model output data needed to reproduce figures in Salazar & Wordsworth, 2024: "The feasibility of asynchronous rotation via thermal tides for diverse atmospheric compositions". Raw GCM data can be made available upon request.</p> <p> </p> <p>The processed data is saved as numpy arrays. For each sensitivity test detailed in Salazar & Wordsworth, 2024, the GCM data was processed by taking surface temperature and surface pressure output and phase folding the data so that the substellar point is the origin of longitudes. Full details are available in the paper.</p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>
Data reported in: Effects of ozone isotopologue formation on the clumped-isotope composition of atmospheric O2
<p>Tropospheric <sup>18</sup>O<sup>18</sup>O is an emerging proxy for past tropospheric ozone and free-tropospheric temperatures. The basis of these applications is the idea that isotope-exchange reactions in the atmosphere drive <sup>18</sup>O<sup>18</sup>O abundances toward isotopic equilibrium. However, previous work used an offline box-model framework to explain the <sup>18</sup>O<sup>18</sup>O budget, approximating the interplay of atmospheric chemistry and transport. This approach, while convenient, has poorly characterized uncertainties. To investigate these uncertainties, and to broaden the applicability of the <sup>18</sup>O<sup>18</sup>O proxy, we developed a scheme to simulate atmospheric <sup>18</sup>O<sup>18</sup>O abundances (quantified as ∆<sub>36</sub> values) online within the GEOS-Chem chemical transport model. These results are compared to both new and previously published atmospheric observations from the surface to 33 km. Simulations using a simplified O<sub>2</sub> isotopic equilibration scheme within GEOS-Chem show quantitative agreement with measurements only in the middle stratosphere; modeled ∆<sub>36</sub> values are too high elsewhere. Investigations using a comprehensive model of the O-O<sub>2</sub>-O<sub>3</sub> isotopic photochemical system and proof-of-principle experiments suggest that the simple equilibration scheme omits an important pressure dependence to ∆<sub>36</sub> values: the anomalously efficient titration of <sup>18</sup>O<sup>18</sup>O to form ozone. Incorporating these effects into the online ∆<sub>36</sub> calculation scheme in GEOS-Chem yields quantitative agreement for all available observations. While this previously unidentified bias affects the atmospheric budget of <sup>18</sup>O<sup>18</sup>O in O<sub>2</sub>, the modeled change in the mean tropospheric ∆<sub>36</sub> value since 1850 C.E. is only slightly altered; it is still quantitatively consistent with the ice-core ∆<sub>36</sub> record, implying that the tropospheric ozone burden increased less than ~40% over the twentieth century.</p>
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
Soil protist functional composition shifts with atmospheric nitrogen deposition in subtropical forests
<p><span>1. </span><span>Soil protist plays a key role in ecological functions through predation and parasitism. However, little is known about how nitrogen (N) deposition and seasonal variations influence soil protist function in forest soils. </span></p> <p><span>2. </span><span>Here, we assessed firstly the impacts of N deposition (control, 50 kg N ha<sup>-1</sup> yr<sup>-1</sup>, 100 kg </span><span>N ha<sup>-1</sup> yr<sup>-1</sup></span><span>, and 150 kg </span><span>N ha<sup>-1</sup> yr<sup>-1</sup></span><span>) on the functional composition of the soil protist community in summer and winter, using amplicon sequencing of environmental DNA from a subtropical natural forest. </span></p> <p><span>3. </span><span>We found that soil protists were dominated by consumers (42.6–51.6%), followed by parasites (32.9–40.9%) and phototrophs (3.2–13.1%), implying a predominant role of consumers and potential top-down effects on the other trophic groups in subtropical forest soils. The functional composition of soil protists was greatly influenced by N deposition, but these responses were dependent on seasonal variations. The diversity of phototrophs was lower in summer than in winter. Instead, an opposite pattern was observed for consumers, resulting in a significantly higher protist diversity in summer than in winter, which indicates a greater sensitivity of soil protists to seasonal variations. Furthermore, low and high N deposition simplified the structural complexity of soil protist communities, suggesting a nonlinear response of the protist structural stability to N deposition.</span></p> <p><span>4. <em>Synthesis and applications.</em></span><span><em> </em>This study provides unprecedented evidence that season variation plays an important role in regulating responses of soil protist functional composition to N deposition, and highlights the nonlinear effects of rising N deposition levels on the soil food web. </span></p>
Dataset of Numerical prediction of changes in atmospheric chemical compositions during a solar energetic particle event on Mars
<p>This dataset contains the input parameters and the simulated results used for figures in the paper "Numerical prediction of changes in atmospheric chemical compositions during a solar energetic particle event on Mars" by Y. Nakamura, F. Leblanc, N. Terada, S. Hiruba, I. Murata, H. Nakagawa, S. Sakai, S. Aoki, A. Piccialli, Y. Willame, L. Neary, A. C. Vandaele, K. Murase, and R. Kataoka.</p> <p>Detailed informations about the data files can be found in "README.txt".</p>
Data reported in: Effects of ozone isotopologue formation on the clumped-isotope composition of atmospheric O2
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Soil protist functional composition shifts with atmospheric nitrogen deposition in subtropical forests
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