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1,574 results for “atmospheres”
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Data & model products from "Identification of carbon dioxide in an exoplanet atmosphere"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05269-w">https://www.nature.com/articles/s41586-022-05269-w</a><br> <br> OVERVIEW: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called “metallicity”), and thus formation processes of the primary atmospheres of hot gas giants. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS). The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide, and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.</p>
Additional Figures for winning models for sample in A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties
<p>Additional Figures for winning models for sample in <em>A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties (<a href="https://arxiv.org/pdf/2209.02754.pdf">https://arxiv.org/pdf/2209.02754.pdf</a>).</em></p> <p>Model naming key: NC = cloud-free, d2_89 = power-law deck cloud</p> <p>SDSS J1416+1348A: Winning model: power-law deck cloud</p> <p>Spectral Type Comparison J1526+2043 Winning model: Cloud-free</p> <p>Temperature Comparisons</p> <p>J1539-0520 Winning model: Power-law deck cloud and cloud-free tied.</p> <p>J0539-0059 Winning model: Power-law deck cloud and cloud-free tied. </p> <p><br> </p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
Data for "Simulated impact of ocean alkalinity enhancement on atmospheric CO 2 removal in the Bering Sea"
<p>This is accompanying data for a submission to AGU Earth's Future by lead author Hongjie Wang and corresponding author Brendan Carter. This submission primarily contains processed model simulation output. The original model output was generated by Kelly Kearney. Unfortunately, the original model output is requires too much memory to submit in its entirety, so this submission is intended to grant interested readers access to the worked-up fields used to produce the figures and values in the manuscript. Individuals interested in higher-resolution model output are encouraged to contact Kelly Kearney to discuss possible transfer solutions.</p> <p> </p> <p>A data hosting solution for the full simulation is being explored by the Bering10k team, and this description will be updated if a publicly accessible alternative to these processed fields becomes available.</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>
Direct Observation of the Transitional Stage of Mixing-State- Related Absorption Enhancement for Atmospheric Black Carbon
<p>Data for article <em>Direct Observation of the Transitional Stage of Mixing-State-Related Absorption Enhancement for Atmospheric Black Carbon.</em></p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering in visible satellite imagery
<p>Look-up tables for rayleigh scattering correction of satellite imager data in the visible spectral range.</p> <p>Derived from LibRadTran simulations for various (aerosol free) standard atmospheres.</p> <p> </p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (antarctic) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (antarctic) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and antarctic aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental average) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental average) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and continental average aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.</p> <p> </p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (urban) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (urban) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and urban aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (rural) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (rural) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and rural aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine tropical aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine polluted) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine polluted) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine polluted aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental polluted) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental polluted) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and continental polluted aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine clean) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine clean) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine clean aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental clean) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental clean) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and continental clean aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and desert aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Modeling the hydrological cycle in the atmosphere of Mars: Influence of a bimodal size distribution of aerosol nucleation particles
<p>Data from figures.</p>
ScienceDex guides
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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.