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42 results for “Atmospheric Chemistry”
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Atmospheric deposition chemistry data from dryfall at the Jornada Basin LTER: 1983-ongoing
This data package contains concentrations of water soluble components from dryfall (dust) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Dryfall occurring as atmospheric fallout is collected monthly. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Wetfall precipitation chemistry is available in data package knb-lter-jrn.210128002.
Atmospheric deposition chemistry data from wetfall at the Jornada Basin LTER: 1983-ongoing
This ongoing data package contains concentrations of water soluble components in wetfall (precipitation) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Wetfall occurring as precipitation is collected after each event with a sample size large enough to analyze. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Dryfall atmospheric deposition chemistry data is available in data package knb-lter-jrn.210128001.
ANA01 Weekly, seasonal and annual measurement of precipitation volume and chemistry collected as part of the National Atmospheric Deposition Program at Konza Prairie
Data set contains results of chemical analysis of wetfall samples collected on Konza Prairie. Analysis is done by the Central Analytical Lab (CAL), Champaign, IL as part of the National Atmospheric Deposition Program (NADP). NADP data products available on the NADP/NTN web site (nadp.slh.wisc.edu/data/NTN/) include: Annual Data Summaries, Semiannual Data Reports, Annual and Seasonal Averages, Monthly Averages, and Weekly data. Konza Prairie LTER archives and provides the weekly data in electronic form before May 2019.
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>
Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres - Posteriors
<p>Supplementary material for 'Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres', ApJL, 2017.</p> <p>Contains the posterior probability distributions resulting from atmospheric retrievals of WASP-31b, WASP-63b, and HD 209458b.</p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling
<p>Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023). </p> <p>Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days.</p> <p>Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename).</p> <p>`average' is the averaged output of the last 100 days.</p> <p>`daily' is the snapshot at the end of the simulation.</p>
Towards a mechanistic description of autoxidation chemistry: from precursors to atmospheric implications (data)
<p>The files contain the raw data from simulations of the plots for main manuscript figures 2 & 3. Any data not archived here (mainly ADCHEM simulations) can be provided upon request (contact: lukas.pichelstorfer@helsinki.fi)</p> <p>On the data naming, content and structure:</p> <p>file names are structured as <Facility/experiment type>_<experimental conditions>_<data_content>.dat</p> <p>"..._time.dat": contains all data output times during the simulation [s]</p> <p>"...T.dat": gas phase temperature during the simulation [K] for all output times (columns)</p> <p>"..._DIAMETER.dat" contains the diameters [nm] of all (100) size bins (lines) for each output time (columns);</p> <p>"...SPC_NAMES.dat": contains the chemical species names</p> <p>"...N_bins.dat": particle number concentration [1/m3] for each size bin (lines) and each output time (columns)</p> <p>"...conc.dat": contains the gas-phase concentrations [1/ccm] of chemical species for all output times (number of species * number of times)</p> <p>"mass_spec.dat": contains the particle phase mass ([µg/m3] for all chemical species -> lines) sum over 100 size bins and for all output times (columns)</p> <p>"FlowTube_autoAPRAM_composition.dat": composition C/H/O/N for each autoAPRAM-fw species</p> <p>"FlowTube_autoAPRAM_names.dat": autoAPRAM-fw species</p>
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry
<p><strong>OVERVIEW</strong></p> <p>The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>). The grid consists of four parameters – the effective temperature (T<sub>eff</sub>), gravity (log(g)), K<sub>zz</sub> in the radiative zones, and mixing length in the convective zones. Models with T<sub>eff </sub> between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K<sub>zz</sub> in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.</p> <p>There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). </p> <p><strong>ATMOSPHERIC COMPOSITION</strong></p> <p>The atmospheric composition files are located in the folder TP_chemistry. These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. </p> <p>The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.</p> <p><strong>SPECTRA</strong></p> <p>The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but <strong>please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage</strong>. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.</p> <p>The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using </p> <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre> <p> The first column of the file is wavenumbers in cm<sup>-1, </sup>which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm<sup>-1</sup>]. The second column of the file is flux in erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.</p> <p><strong>K<sub>zz</sub> PROFILE</strong></p> <p>The K<sub>zz </sub>as a function of pressure for each model is presented in these files. The K<sub>zz</sub> is reported in cm<sup>2</sup>/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm<sup>2</sup>/s.</p> <p> </p> <p><strong>EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES</strong></p> <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> <p><strong>CREDITS</strong></p> <p>If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a></p> <p> </p>
Sonora Bobcat: cloud-free, substellar atmosphere models, spectra, photometry, evolution, and chemistry
<p><strong>OVERVIEW</strong></p> <p>Presented here are models for non-irradiated, substellar mass objects belonging to the Sonora model series, described in Marley et al. (2021). The files presented here are model temperature-pressure structures ("structure"), emergent spectra from the top of the atmosphere ("spectra"), thermal evolution and photometry ("evolution_and_photometry"), and rainout chemical equilibrium tables used to compute the models ("chemistry"). </p> <p>Atmospheric structure and spectra .tar file names specify metallicity [M/H] and carbon-to-oxygen ratio (C/O) relative to solar. For example, "structures+0.0_co1.5<a href="../api/files/2e9ce76a-67fc-4fd6-ae5c-f88f16c610ea/structures%2B0.0_co1.5.tar.gz">.</a>tar.gz" contains the set of radiative-convective equilibrium atmospheric structures for solar metallicity ("+0.0") with C/O=1.5 times the solar abundance. The _co*.* is omitted for solar C/O, or co_1.0. The individual file naming convention is described below. All stated abundances and ratios are relative to Lodders (2010) abundances, see Marley et al. (2021) for details and use caution when referring to other abundance tabulations.</p> <p>This particular set of model atmosphere structures and associated spectra, photometry, and evolution, which we name <strong>Sonora Bobcat</strong>, are for cloudless objects with 3.25 ≤ log g (cgs) ≤ 5.5 and 200 ≤ Teff ≤ 2400K. Steps in T<sub>eff</sub> vary from 25K to 1000K and steps in log g are 0.25 or 0.5. Some combinations of model grid parameters include additional values of the gravity. Models are provided for [M/H] = -0.5, 0.0, and +0.5 and "rainout" chemical equilibrium. A limited set of models with carbon-to-oxygen ratio of 0.5 and 1.5 times solar abundance are also included. For the convenience of having a rectangular table in (T<sub>eff</sub>, gravity) space, models are calculated in regimes that are not reached by the evolution, such as very high gravity and very low T<sub>eff</sub>. Refer to the companion evolution tables to identify combinations of T<sub>eff</sub> and log g outside the bounds covered by the evolution.</p> <p><strong>ATMOSPHERIC STRUCTURE</strong></p> <p>Atmospheric structure and spectra filenames specify Teff and gravity (in mks units) along with [M/H] and (C/O) relative to solar. "co1.5" in version and spectra header nomenclature refers to 1.5 times the solar C/O ratio. _co*.* is generally omitted for 1.0, the solar value. For example, the file t1000g316nc_m-0.5.dat contains the structure of a model with T<sub>eff</sub>=1000K, g=316m/s<sup>2</sup> (the exact value of the gravity is given<sup> </sup>in the first line of the file, see below) , [Fe/H]=-0.5, and C/O=1.0 times the solar value. </p> <p>Temperature structure and spectra files have a one line header giving "Teff, grav(MKS), Y, f_sed, kz_min, [Fe/H], C/O, f_hole". Teff and grav are the effective temperature (K) and gravity (MKS), Y is the He mass fraction. f_sed is a cloud parameterization which is not relevant for these cloudless models and is arbitrarily given as 0.0. Likewise kz_min relates to the atmospheric eddy diffusion coefficient, which is also not relevant for these chemical equilibrium models and is arbitrarily set equal to a placeholder value that is not used in these models. [Fe/H] and C/O are the metallicity and C/O ratios as described above. [Fe/H] is identical to [M/H]. f_hole is another cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>Columns in the atmosphere structure files describe the atmosphere at discrete levels. Columns give: level index, P(bar), T(K), internally used check parameter, adiabatic temperature gradient (d ln T / d ln P), local temperature gradient (d ln T / d ln P), atmospheric density (g / cm<sup>3</sup>).</p> <p><strong>EVOLUTION AND PHOTOMETRY</strong></p> <p>Evolution and Photometry tables are described in detail in a README file included in that tar file. Evolution files connect mass, effective temperature, radius, age, gravity, and moment of inertia for these model sets. Each set of model spectra is complemented with tables of fluxes and of absolute magnitudes in a number of photometric systems commonly used in brown dwarf and exoplanet research (MKO, Keck, 2MASS, SDSS, WISE, Spitzer IRAC, etc). Fluxes and magnitudes for the full set of JWST filters is also included in separate tables. Magnitudes are computed on the Vega system (using the Vega spectrum of Bohlin & Gilliland 2004) or on the AB system (e.g. for SDSS).</p> <p><strong>SPECTRA</strong></p> <p>The model spectra each contain close to 362000 wavelength points. The resolving power varies with wavelength and ranges from R=6000 to 200000 but is otherwise the same for all spectra. The first line gives the model parameters in the same format as the structure files described above. This is followed by the spectrum</p> <p>Column 1: wavelength in µm</p> <p>Column 2: <strong>Radiation flux <em>F<sub>ν</sub></em></strong><sub> </sub>= \(4\pi\) x Eddington flux <em>H</em><sub>ν</sub>, in erg/cm<sup>2</sup>/s/Hz (always exercise caution with factors of \(4\pi\) when comparing to the radiation and Eddington flux, e.g., see Section 3.3 of Hubeny & Mihalas, "Theory of Stellar Atmospheres")</p> <p>The spectral fluxes are given at the top of the atmosphere and are strictly monochromatic. The model spectrum provides no information in the wavelength range between two tabulated points. Unless a spectral line or feature is well resolved,<em> interpolation in wavelength is not advised</em>. For comparison with data, the model spectra need to be convolved and binned to the instrumental resolution and sampling. In our experience, a minimum of 10 wavelength points is necessary to obtain a reasonable average flux over a wavelength interval. This is a rule of thumb and caution is advised, especially when comparing with high resolution data. The flux received at Earth is that given in the table scaled by (R/D)<sup>2</sup> where R is the radius of the object (given in the companion evolution tables) and D its distance. </p> <p>The solar spectra and photometry are the same as those archived at https://zenodo.org/record/1309035#.YOyz4S1h2X0, which did not provide the T(P) profiles available here.</p> <p><strong>CHEMISTRY</strong></p> <p>We also separately include rainout chemical equilibrium tables for these same atmospheric bulk abundances. These chemistry files are described in detail by their own README file. Additional chemistry tables, beyond those used for the models presented here, are also included for completeness. Users interested in the chemical abundances of the structure models must interpolate within the matching chemistry file for the atmospheric species of interest.</p> <p><strong>CREDITS</strong></p> <p>If you use these tables in your research, please cite Marley et al. (2021, Astrophysical Journal, Volume 920, Issue 2, id.85.)</p> <p>16 Feb 2024: Error corrected in Column 2 heading. Column 2 is the Radiation flux, not the Eddington flux as previously stated. Citation updated.</p> <p> </p>
Phosphorus Chemistry in the Earth's Upper Atmosphere
<p><strong>Phosphorus Chemistry in the Earth’s Upper Atmosphere</strong></p> <p>by</p> <p>John M.C. Plane<sup>1*</sup>, Wuhu Feng<sup>1,2</sup> and Kevin M. Douglas<sup>1</sup></p> <p><sup>1</sup> School of Chemistry, University of Leeds, UK</p> <p><sup>2</sup> National Centre for Atmospheric Science and School of Earth and Environment, University of Leeds, UK</p> <p> </p> <p>The repository contains the data used in the above paper.</p>
Atmospheric data used for calibrating the tropopause in global chemistry-climate or chemistry-transport models
Open the record for dataset details and reuse information.
An updated cloud-overlap photolysis module for atmospheric chemistry models, UCI Cloud-J v8.0, with near-UV H2O absorption
Open the record for dataset details and reuse information.
Dynamically coupled kinetic chemistry in brown dwarf atmospheres - II. Cloud and chemistry connections in directly imaged sub-Jupiter exoplanets
<p>Gifs of GCM output from the paper, model is Teff = 1000 K, log g = 3, M/H = 1. </p><p>The atmos_daily_2980.nc file contains the GCM NETCDF output at 2080 days.</p>
Model Datafiles for Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice
<p>Datafiles produced by the 1-D photochemistry model used to create the publication "Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice" - Taysum et al. 2024.</p> <p> </p> <p>matching_orbits_v2.txt : Lists the orbital parameters and water vapour / aerosol file names corresponding to each of the 77 ACS MIR HCl observations that we study.</p> <p> </p> <p><strong>MCD_GlobaMaps.zip</strong> : .nc files containing model output where the model is ran at Longitude 0 degrees, across Ls 0 --> 360 in intervals of 30 degrees, and latitudes 60 S to 60 N in intervals of 15 degrees.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li> </li> </ul> </li> </ul> <p><strong>ACS_Comparisons.zip </strong>: .nc files containing the model output for specific ACS MIR HCl observations in Mars Year 34.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, and TGO measured water ice, dust, and H2O vapor profiles, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> </ul>
Atmospheric hydroxyl distribution from the EMAC model (MOM kinetic chemistry mechanism)
<p>This dataset contains the output from the simulations with the EMAC model implementing the MOM kinetic chemistry mechanism presented in <a href="https://doi.org/10.5194/acp-16-12477-2016">Lelieveld et al. (2016)</a> study. In addition to the computed atmospheric hydroxyl (OH) and hydroperoxyl (HO2) radicals abundance distributions, we add related model fields facilitating usage/comparison of these results with other estimates.</p><p>The data containers format is netCDF v.4 (compressed); please refer to the container variables/attributes for the extended information. We present here the actual model output (weekly averages for the 2013−2014 period, in EMAC-MOM__*.nc) and the monthly "climatology" fields (average, SD, minima and maxima of the time steps falling in particular month, in EMAC-MOM__*--clim.nc, respectively).</p><p>See the .README.pdf file for additional notes.</p><p>Changes w.r.t. initial version from 2020/09/22:</p><p>2020/10/30: Updated attributes and DOIs in .nc/.jnl files, added OH "climatology", updated README.</p><p>2020/10/31: Updated description and "climatology" (SD fields were missing).</p><p>2022/06/19: Added hydroperoxyl (HO2) fields, updated species average concentration plot sample script.</p><p>2023/10/26: Dataset title adjusted for clarity</p>
NH3 levels over Europe during COVID-19 were modulated by changes in atmospheric chemistry
<p><span>The coronavirus outbreak in 2020 had a devastating impact on human life, albeit a positive effect for the environment, reducing primary atmospheric constituents and improving air quality. Here we present, for the first time, inverse modelling estimates of ammonia emissions during the European lockdowns of 2020 based on satellite observations. Ammonia has a strong seasonal cycle; it mainly originates from agriculture, which was influenced insignificantly by the lockdowns, as practically agricultural activity never ceased. The key result is a -0.7% decrease in emissions in the first half of 2020 compared to the same period in 2016–2019 attributed to restrictions related to the global pandemic or an abrupt -9.8% decrease due to reductions in the traffic-related precursors of atmospheric acids, with which ammonia reacts to form secondary aerosols. When comparing emissions before, during and after lockdowns, the typical seasonal trends of ammonia prevail. However, when reductions in the precursors of atmospheric acids are considered, a delay of 11% was found in the evolution of the emissions. Thus, changes in atmospheric conditions such as those of the ammonia's reactant precursor species induce extra bias in top-down calculations and, hence, emissions should be interpreted carefully. Despite the small drop in emissions, satellite levels of ammonia increased. On one hand, this was due to the reduction of atmospheric acids that caused binding and thus removing less ammonia; on the other, the reduction of traffic-related emissions in Europe increased the oxidative capacity of the atmosphere resulting in nitrate abatement that favored accumulation of free ammonia.</span></p> <p>Update March 2023:</p> <p>- 4deg_avgEENV.tar.gz file was added containing the inversion results using the avgEENV dataset as a priori information. This prior creates a better fit of the posterior modelled concentrations to ground-based independent observations of NH3 over Europe in the first half of 2020.</p>
Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
<p>This dataset contains the Gemini/FLAMINGOS-2 near-infrared spectrum of the planetary-mass companion, COCONUTS-2b, as presented in the paper "Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b" (Zhang et al. 2024) for publication by Astronomical Journal (arXiv:2410.10939). This file includes three columns: wavelengths (unit: micron), flux (unit: erg/s/cm2/Angstrom), and flux uncertainties (unit: erg/s/cm2/Angstrom). Please cite this paper and the Zenodo repository if these data are used in your work.</p> <p> </p> <p> </p>
Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry
<p>This data set consists of 1d radiative-convective equilibrium models intended for Y Dwarfs or cool giant planets with negligible levels of external irradiation, computed using coolTLUSTY and a recently updated set of molecular absorption cross sections. Models span effective temperatures of 200 - 600 K, metallicities of [-0.5], [0], and [+0.5] dex, and surface gravities of log10(g / [cm/s^2]) = 3.5 - 5.0, and are computed for both cloudy and clear cases, in thermochemical equilibrium and with nonequilibrium carbon and nitrogen chemistry due to vertical mixing. Models extend from 0.5 microns to 300 microns with 30,000 frequency points evenly spaced in ln(frequency), which corresponds to an average resolving power of R ~ 4340.</p> <p>See README.txt for a description of file formats, naming conventions, and decompressed sizes.</p> <p>A more thorough summary of the assumptions made in computing these models and a walk-through of their properties can be found in Lacy & Burrows 2023 "Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry" accepted for publication in the Astrophysical Journal and available on arXiv.</p> <p>If you make use of these models please cite that publication, along with this zenodo data set.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.