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695 results for “model output”
CESM2 model output from Eisenman & Armour (2024): Part 1
<p>Description of data: See README.txt</p> <p>Citation for data: I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p>See also https://eisenman-group.github.io for links to other fields from these simulations.</p>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Model outputs used in "Regional cooling potential from expansion of perennial grasses in Europe"
<p>This dataset accompanies a scientific publication: Xia Zhang, Bo Huang, Nariê Rinke Dias de Souza, Xiangping Hu, and Francesco Cherubini. 2024. Regional cooling potential from expansion of perennial grasses in Europe. Communications Earth & Environment <strong>5</strong>, 772 (2024). https://doi.org/10.1038/s43247-024-01923-5</p>
CESM2 model output from Eisenman & Armour (2024): Part 4
<p>Description of data: See README.txt</p> <p>Citation for data: I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p>See also https://eisenman-group.github.io for links to other fields from these simulations.</p>
CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs
<p>This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented. </p>
Data and model output for "Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation"
<p>Data, model output, and scripts supporting the manuscript:</p> <p>Litwin, D. G., & Harman, C. J. (2024) Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation. <em>Water Resources Research</em>, 60, e2024WR037301. https://doi.org/10.1029/2024WR037301</p>
Croco Idealized Model Output for paper: https://doi.org/10.1029/2024JC021204
<p>This is the output of croco idealized model for the JGR-Oceans paper</p>
Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)"
<p>Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)". The dataset includes COS ecosystem, vegetation and soil fluxes, as well as CO2 exchange fluxes. COS soil fluxes are calculated through the Ogee et al. (2016) soil model, and COS mole fractions vary spatially and temporally. The site-specific files provide SiB4 output only for the plant functional type that is represented by this site. The plant functional type is included in the file name. Datasets include 3 hourly output between 2000-2020 for site simulations, and monthly output between 2000-2020 for global simulations (at 0.5x0.5 degrees). The SiB4 model code is available online: https://gitlab.com/kdhaynes/sib4_corral</p>
Model output: CICE6 with WIFF
<p>CICE6 model experiments with different versions of wave fracture </p> <p> </p> <p>For further details, see </p> <p>Horvat, C. and Roach, L. (2021) WIFF1.0: A hybrid machine learning parameterization of Wave-Induced sea-ice Floe Fracture. To be submitted to Geoscientific Model Development</p>
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
Model output data for compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts
<p>The archive contains model output data for article "Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts" to be published in Monthly Weather Review.</p> <p>The article presents the semi-implicit compressible EULAGas a newdynamical core for convective-scale<br> numerical weather prediction. The core is implemented within the infrastructure of the<br> operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP<br> COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical<br> core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated<br> Forecasting System. The paper documents the first operational-like application of the dynamical<br> core for realistic weather forecasts. After discussing the formulation of the core and its coupling<br> with the host model, the paper considers several high-resolution prognostic experiments over<br> complex Alpine orography. Standard verification experiments examine the sensitivity of the CE<br> forecast to the choice of the advection routine and assess the forecast skills against those of the<br> default COSMO Runge-Kutta dynamical core at the 2.2 km grid showing a general improvement.<br> The skills are also compared using satellite observations for a weak-flow convective Alpine weather<br> case-study, showing favorable results. Additional validation of the new CE framework for partly<br> convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to<br> challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in<br> simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and<br> the flow’s realistic representation.</p>
The configurations, inputs and outputs of the EFDC model for all simulated episodes
<p>TAIHU EFDC.zip includes two file folders named 2015 and 2018. The 2015 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2015. The 2018 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2018.</p>
Lagrangian drifter output in the Southeast Indian Ocean using the Connectivity Modelling System output forced with TROPAC01
<p>This dataset contains Lagrangian drifter trajectories from the Connectivity Modelling System (CMS) in the Southeast Indian Ocean. CMS was forced with ocean velocity fields from TROPAC01 and this experiment was focused on sources of the Leeuwin Current. TROPAC01 is a high-resolution ocean general circulation model, developed by the European Drakkar cooperation [Barnier et al., 2007] it is based on the NEMO [v3.2 Madec, 2008] code. Specifically, it is a 1/10 horizontal resolution model of the tropical Indo- Pacific region (spanning the area from 73°E - 63°W to 49°S - 31°N), nested within a half-degree global ocean/ sea-ice model. More information on the model configuration used for this experiment can be found in [van Sebille et al., 2014]. Using the velocity fields from TROPAC01 we then use the Connectivity Modelling System (CMS) v1.1 [Paris et al., 2013] to integrate the virtual particles in three-dimensional time-evolving flow.</p> <p>Version v1.0 of this dataset includes ascii raw model output of CMS trajectories and the forcing file (seed file) that enables a user to calculate absolute time of a particle's location. Variables are: particle_number, time, longitude, latitude, depth, exit_code.</p> <p>These experiments were executed by Christopher Bull of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "Mechanisms and attribution of past and future ocean circulation change", as part of Christopher's PhD candidature.</p> <p> </p> <p>References:</p> <p> Code and documentation for the CMS is available at:</p> <p> https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Claire B. Paris, Judith Helgers, Erik van Sebille, Ashwanth Srinivasan, 2013.<br> Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean,<br> Environmental Modelling & Software, Volume 42, 2013, Pages 47-54, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2012.12.006.</p> <p>van Sebille, E., Sprintall, J., Schwarzkopf, F. U., Gupta, A. S., Santoso, A., England, M. H., Biastoch, A., and Böning, C. W. (2014), Pacific-to-Indian Ocean connectivity: Tasman leakage, Indonesian Throughflow, and the role of ENSO, <em>J. Geophys. Res. Oceans</em>, 119, 1365– 1382, doi:<a href="https://doi.org/10.1002/2013JC009525">10.1002/2013JC009525</a>.</p>
Model output from hydrothermal simulations
<p>Files Fe_spreading.nc, Fe_inverse.nc and Fe_inverseR.nc represent dissolved iron fields (mmol/m3) and files ECP_spreading.nc, ECP_inverse.nc and ECP_inverseR.nc export production at 100m (mol/m2/s) from experiments of those names in Tagliabue et al., "Constraining the contribution of hydrothermal iron to the Southern Ocean biological carbon pump using deep ocean iron observations" Frontiers in Marine Science</p>
Roach et al. (2019) coupled wave-ice model output (hourly coupling version): Beaufort Sea 2012-2019
<p>Wavewatch III model output from Roach et al. (2019) coupled wave-ice model with hourly coupling from the central Beaufort Sea, spanning 2012-2019.</p> <p>See manuscript below for further details:</p> <p>Roach, L., C. Bitz, C. Horvat, and S. Dean (2019), Advances in modelling interactions between sea ice and ocean surface waves. Journal of Advances in Modeling Earth Systems</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
<p>Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.</p>
model output
<p>The model output data used in the figures for the experiments of the study "Recent acceleration of Arabian Sea warming induced by the Atlantic-western Pacific trans-basin multidecadal variability"</p>
Rice productivity along waterlogging gradient in Indonesian peatlands (EPIC-IIASA model outputs)
<p>[1] The spatially explicit modelled water table depth data (WTBL, in m) were produced for a total of ten waterlogging intensity scenarios (WetScen) in Indonesian peatlands using the Environmental Policy Integrated Climate-based modelling framework EPIC-IIASA. The WetScen scenarios 1 to 10 represent a gradient from water-logged to deeply drained soils. The dataset provides monthly WTBL series simulated between 2001 and 2010 at 0.25×0.25 arc-deg spatial resolution across all Indonesia peatland soils (<a href="https://doi.org/10.5281/zenodo.6997968">https://doi.org/10.5281/zenodo.6997968</a>). This data brings harmonized groundwater regime data in large-scale agricultural modelling to allow assessing the impact of peatland rewetting on rice productivity in Indonesia. These data were prepared as a part of the RESTORE+ project, separately for two rice growing seasons (seasons 1 and 2) and rice double-cropping on mineral (miner) and shallow peat soils (peat) with a depth of less than 1 m.</p> <p>[2] The spatially explicit datasets of annual rice yield (YLDG in tDM/ha) and monthly root zone soil water content (RZSW, in mm) were simulated at 0.25×0.25° spatial resolution following the waterlogging scenarios described above. The datasets explore (a) two nutrient management, namely the good agricultural practice (GAP) and management that was back-calibrated against statistically reported regional yields (BackCalc), (b) two rice seasons (season 1 and 2) plus a double-cropping system, and (c) ten waterlogging scenarios (WetScen) in all peatland grids with soils suitable for rice production, i.e., all mineral soils (miner) and soils with the shallow peat material (peat). The simulations were carried out from 2001 to 2010, consistently with the scenario data and modelling framework described above. This database was created as a part of the RESTORE+ project. The details are described in a separate document (see <a href="https://doi.org/10.5281/zenodo.7355835">https://doi.org/10.5281/zenodo.7355835</a>).</p>
Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"
<p>Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"</p> <p>Annex I: This supporting information provides the software review for native and web-based tools that could be used for Circular Economy and environmental analysis.</p> <p>Annex II: This supporting information provides the use-case result analysis.</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)
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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.