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35 results for “atmospheric transport”
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 1
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 1 data from each instrument. At this level, several processing steps were applied to the raw data to minimize offsets, reduce the number of spurious data and derive wind speed and direction profiles; however, the data from each instrument is kept on its original spatial and temporal resolution. The raw data is available for the users on request from the corresponding author.</p> <p><strong>Prefix identificaiton:</strong></p> <p>Lidar data: cmtrace_cabauw_wls200-218<br> Scanning radar data: cmtrace_cabauw_rpg_radar_35-94<br> Vertically pointing radar data: cmtrace_cabauw_rpg_radar_94</p> <p> </p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C
<p>The data set complementary to manuscript "Analysis of regional CO<sub>2</sub> contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ<sup>13</sup>C" in <em>Atmospheric Chemistry and Physics</em> (<a href="https://acp.copernicus.org">https://acp.copernicus.org</a>).</p>
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 2
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 2 data from the campaign. At this level, several processing steps were applied to the Level 1 data from each instrument to minimize the differences between the sampled volumes resampled and temporal and spatial resolution to generate merged profiles of wind speed and direction. The raw dataset is available for the users on request from the corresponding author.</p>
Freshwater transport from the Atlantic to the Pacific Ocean through the atmosphere
<p>emp_eul_lagrang.mat: This mat file contains global evaporation - precipitation (E-P) data obtained from ERA-Interim, Eulerian and Lagrangian method. The validation of the atmospheric water-mass version of TRACMASS had been done using this data.</p> <p>psixz_ep_eastward.zip: This zip file contains 12 netcdf files corresponding to each month. These files were used to compute E - P, atmospheric freshwater transport as a function of month and atmospheric zonal water-mass stream function for the water-mass traveling from the Atlantic to Pacific Ocean over Afro-Eurasia. </p> <p>psixz_ep_westward.zip: Same as above but for the water-mass traveling from the Atlantic to Pacific Ocean over America. </p> <p>resdt_eastward.nc: This netcdf file consists of atmospheric water residence time extracted from water-mass trajectories traveling from the Atlantic to Pacific Ocean over Afro-Eurasia.</p> <p>resdt_westward.nc: Same as above but from water-mass trajectories traveling from the Atlantic to Pacific Ocean over America.</p> <p>resdt_all.nc: This netcdf file consists of atmospheric water residence time computed from water-mass trajectories traveling from the Atlantic to Pacific Ocean regardless of its pathway (Eastward over Afro-Eurasia + Westward over America).</p> <p>resdt_all_global.nc: This netcdf file consists of global atmospheric water residence time calculated from the global TRACMASS run.</p> <p> </p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
Atmospheric data used for calibrating the tropopause in global chemistry-climate or chemistry-transport models
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Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
CM1 files for JGR-Atmospheres Paper Sensitivities of Cross-Tropopause Transport in Midlatitude Overshooting Convection to the Lower Stratosphere Environment
<p>Initial conditions to run the CM1 model simulations used in the JGR-Atmospheres Paper <em>Sensitivities of Cross-Tropopause Transport in Midlatitude Overshooting Convection to the Lower Stratosphere Environment</em>. The namelist.input file contains the initial conditions used to run the simulations, and the input sounding files (input_sounding_st_plume, input_sounding_st_noplume, input_sounding_dt_plume, input_sounding_dt_noplume) are the initial soundings used to run the CM1 model for each of the four different stratospheric environments. </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>
The Model Grid for The atmosphere of HD 149026b: Low metal-enrichment and weak energy transport
<p>This grid contains cloud-free 1-dimensional radiative-convective-thermochemical equilibrium atmosphere models created using the Python-based code <a href="https://natashabatalha.github.io/picaso/">PICASO</a>. The parameters varied for this grid are the atmospheric metallicity (<em>[M/H]</em>), Carbon-to-Oxygen ratio (<em>C/O</em>), heat redistribution factor (<em>rfacv</em>), and the intrinsic temperature of the planet (<em>Tint</em>). The ranges of these parameters have been outlined in the paper. </p> <p>The profile and spectra are provided for each model as a .dat file. Each profile contains the temperature and abundance for a variety of chemicals at each of the 91 pressure levels modeled for the atmosphere. The spectra file contains the wavelength in microns, transit depth, eclipse depth, and emission flux from the planet in ergs/s/cm^3. The isolated planetary thermal emission spectrum needs to be multiplied by 1e6 to be in ppm. There are four types of models, ones with VO, ones with TiO, ones with TiO and VO, and ones without TiO or VO. The files are labeled based on each of the 4 atmospheric parameters and whether they contain TiO and VO.</p> <p>Note on TiO: The inclusion of gaseous TiO in the atmosphere was found to cause strong inversions in the temperature-pressure profile and a worse fit of the thermal emission spectrum to the data. This finding has been described in the paper.</p>
Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model
<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>
Identification of non-turbulent motions for enhanced estimation of turbulent transport using the anisotropy of atmospheric Turbulence
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Atmospheric transport is a major pathway of microplastics to remote regions
<p>In the recent years, a large attention has been given to pollution from plastic products as a major environmental problem. Plastics degrade into smaller particles in the environment via photodegradation, physical abrasion, hydrolysis and biodegradation. Microplastics (1 um to 5 mm size particles) have been reported to affect coral reefs, marine and terrestrial animals, as well as humans. It has been reported that about 30% of microplastics in freshwater and oceanic ecosystems are tire wear particles (TWPs), while brake wear particle (BWP) emissions constitute 55% of all non-exhaust traffic-related particle emissions and 21% of all traffic-related PM emissions. There is a general conviction that the relative contribution of TWP and BWP emissions to total transport-related emissions will grow in the near future, due to the continuous reduction of exhaust traffic-related emissions. Although transport of TWPs and BWPs via runoff and wash-out processes to the marine and/or freshwater ecosystem has been studied extensively, very little is known about how these particles are dispersed in the atmosphere and where they are deposited. This is important due to the aforementioned impact in animals and humans. They also have an environmental impact as they are derived by materials made from fossil fuels such as ethylene and propylene. Thus, larger needs of plastics result in larger emissions of greenhouse gases. Since TWPs and BWPs can become airborne and have been detected already in remote areas, they may absorbe light decreasing surface albedo and accelerating ice melting.<br> Here, we present for the first time the results of the atmospheric dispersion and deposition of traffic-related microplastics (TWPs and BWPs). We assess the suggested emissions using two methods, one indirect based on CO2 country ratios with road microplastics and extrapolation, and another employing an emissions model, which has been extensively used to determine global emissions of various substances by IIASA-International Institute for Applied Systems Analysis (GAINS model). We calculate that 34.4–290 kt y<sup>-1</sup> (mean: 100 kt y<sup>-1</sup>), out of 102–787 kt y<sup>-1</sup> (mean: 284 kt y<sup>-1</sup>) of PM10 TWPs emitted, were deposited in the World Ocean, while the respective annual terrestrial and riverine discharges are about 64 kt y-1. This shows that direct deposition of airborne road microplastics is likely the most important source for the ocean and marine biota. The calculated transport of PM10 road microplastics shows a relatively high efficiency over remote regions such as the Arctic Ocean (14%). High latitudes and the Arctic are highlighted as an important receptor of mid-latitude microplastic emissions, which may imply a future climatic risk taking into account that TWPs and BWPs constitute a small portion of the total plastic emissions. As of now, snow concentrations of road microplastics are 100 times lower than those of black carbon or polymers of larger usage (e.g., PVC or PPC). Around 15% of the PM2.5 road microplastic emissions were deposited in the Atlantic Ocean, whereas coarse particles were less efficiently deposited there (10-11%). The efficiency of PM2.5 deposition (TWPs: 19% - BWPs: 18%) over the Pacific Ocean was even more strongly enhanced relative to PM10 deposition (TWPs: 12% - BWPs: 11%), due to their smaller size. Transport efficiencies of coarse particles were up to twice of those for the fine particles in areas surrounded by microplastic emissions sources (e.g., Alps, Mediterranean, Baltic and South China Seas).</p>
Trajectories for 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''
<p>This is the trajectories output for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport''.</p> <p>The settings for FLEXPART-WRF is also included.</p>
Trajectory Movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'
<p>The supplement movie for the article 'A Lagrangian view of the atmospheric river related to the heavy rainfall of July 2020 in Japan: Importance of moisture gain during transport'.</p>
contribution of atmospheric transport to phosphorus in the East China Sea in summer
<p>For the analysis of the main sources of phosphate in the East China Sea during summer, we choose the area between 120.93 ˚ E-125.9 ˚ E and 26.08˚ N-32.35 ˚ N as research sites . The samples were all from the voyage of the research ship (Xiangyanghong 18). Surface seawater was collected at 49 stations for δ<sup>18</sup>O<sub>p</sub> analysis by a conductivity-temperature-depth profiler. Then we utilized δ<sup>18</sup>O<sub>p</sub> to trace the source of phosphate in the East China Sea. </p> <p>The δ<sup>18</sup>O<sub>p</sub> values in seawater are affected by water masses with different phosphate concentrations.We need the two-component mixing model for the analysis of water masses that control phosphate.In this research, atmospheric deposition and adjacent sea transportation were used as terrestrial and seawater end-members respectively to construct the δ<sup>18</sup>O<sub>p</sub> end-member mixing model.</p> <p>In order to quantify the source of phosphate in the sea, a Bayesian isotope mixing model run in the R software package (Stable Isotope Analysis in R, SIAR) was selected for analysis.</p>
Sand transport processes change in response to changing atmospheric properties at higher elevations
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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)
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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.