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1,477 results for “earth”
3D simulations of the Archean Earth including photochemical haze profiles [dataset]
<p>Dataset of 3D simulations of the Archean Earth including photochemical haze profiles</p> <p>We present results from 3D simulations of the Archean Earth including a prescribed spherical haze generated through a 1D photochemical model. Our simulations suggest that a thin haze layer, formed when CH4/CO2 = 0.1, leads to global warming of ~10.6K due to the change of water vapour and cloud feedback, compared to the simulation without any haze. However, a thicker haze layer, formed when CH4/CO2 > 0.1, leads to global cooling of up to ~65K as the scattering and absorption of shortwave radiation from the haze reduces the radiation from reaching the planetary surface. A thermal inversion is formed with a lower tropopause as the CH4/CO2 ratio increases. The haze reaches an optical threshold thickness when CH4/CO2 ~ 0.175 beyond which the atmospheric structure and the global surface temperature do not vary much.</p> <p> </p> <p>The IRIS python package developed by the Met Office is needed to analyse the data.</p>
Data for "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)"
<p>This tar file contains the data produced for the manuscript "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)". The contents include model data from the simulation presented in the paper, a four-year benchmark simulation using version 10.23.0 of GEOS with GOCART2G, spanning the period of 2016 through 2019. Aerosol mass budget terms including emissions, production, and deposition are included as well as the model data sampled according to the observational data sets for MODIS, AERONET, OMPS-LP, CALIOP, IMPROVE, and EMEP.</p>
Speciation across the Earth driven by global cooling in terrestrial Orchids
<p>Although climate change has been implicated as a major catalyst of diversification, its effects are thought to be inconsistent and much less pervasive than localized climate or the accumulation of species with time. Focused analyses of highly speciose clades are needed in order to disentangle the consequences of climate change, geography, and time. Here, we show that global cooling shapes the biodiversity of terrestrial orchids. Using a phylogeny of 1,475 species of Orchidoideae, the largest terrestrial orchid subfamily, we find that speciation rate is dependent on historic global cooling, not time, tropical distributions, elevation, variation in chromosome number, or other types of historic climate change. Relative to the gradual accumulation of species with time, models specifying speciation driven by historic global cooling are over 700 times more likely. Evidence ratios estimated for 212 other plant and animal groups reveal that terrestrial orchids represent one of the best-supported cases of temperature-spurred speciation yet reported. Employing >2.5 million georeferenced records, we find that global cooling drove contemporaneous diversification in each of the seven major orchid bioregions of the <span class="inserted">Earth</span>. With the current emphasis on understanding and predicting the immediate impacts of global warming, our study provides a clear case study of the long-term impacts of global climate change on biodiversity.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Lunar ejecta origin of near-Earth asteroid Kamo`oalewa is compatible with rare orbital pathways
<p>Outcomes of numerical simulations for orbital elements and collisional states of test particles launched from the surface of the Moon as described by:</p> <p>Castro-Cisneros, Malhotra, Rosengren (2023). Lunar ejecta origin of near-Earth asteroid Kamo`oalewa is compatible with rare orbital<br> pathways. Accepted for publication in Communications Earth & Environment.</p> <p> </p> <p> </p>
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels.</p> <p> </p> <p>The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. </p> <p> </p> <p>The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.</p> <p><br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (<a href="https://github.com/ai2es/miles-guess">https://github.com/ai2es/miles-guess</a>).</p>
Data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"
<p>Main data used in the recently submitted AGU manuscript "Global Mantle Conductivity Imaging using 3-D GDS Inversion with Real Earth Surface Conductivity Constraint"</p>
Atlas holding the Earth
Atlas doing morning exercise. Made with Zbrush and Substance Painter. Source: Objaverse 1.0 / Sketchfab
Comb from Birkas black earth
A comb from Hjalmar Stolpes excavations of the viking age town of Birka. http://historiska.se/upptack-historien/object/269175 Photogrammetry / Fotogrammetri Xenter 3D-tekniker Midori Åstrand Source: Objaverse 1.0 / Sketchfab
Bare-earth lidar point cloud, Santa Ynez Mountains, near Montecito, CA, USA, year 2015
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Dataset accompanying Buscombe et al.: Human-in-the-loop segmentation of earth surface imagery
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Eccentric orbits may enhance the habitability of Earth-like exoplanets
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Data from: Earth history and the passerine superradiation
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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.