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195 results for “Coupled models”
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Coupled climate-glacier modelling of the last glaciation in the Alps: modelling data
<p>This dataset contains key distributed 2D variables resulting from the modelling of the Alpine Ice Field over the last glacial cycle from Jouvet and al. (2023, 10.1017/jog.2023.74), including basal surface topography, ice thickness, pressure-adjusted basal temperature, basal and surface ice flow speeds. The results are given on a raster grid in UTM system of coordinate with a spatial resolution of 2 km and a temporal resolution of 100 year. The data are compiled in netCDF.</p> <p>As explained in the paper, the model was designed to match LGM evidence. Modelled results related to intermediate states and to the Holocene must be interpreted with caution considering the relative coarse resolution (2 km). Small ice caps aside the main Alpine Icefield (except the Jura) were excluded.</p>
Next generation global ice-ocean-biogeochemistry coupled model with 13C-cycling (GFDL MOM5-BLING13C)
<p> </p> <p>======= DESCRIPTION =======</p> <p>This is the model output supporting our paper <em>A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C</em> (2021 Global Biogeochemical Cycles).</p> <p>This model output simulates the transient response of ocean carbon biogeochemistry to anthropogenic CO<sub>2</sub> and <sup>13</sup>CO<sub>2</sub> atmospheric emissions with a nominal lateral resolution of 1° and 50 vertical levels. The model uses the NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) MOM5 coupled to the NOAA-GFDL Biogeochemistry with Light Iron Nutrients and Gas (BLING) with <sup>13</sup>C-cycling. Atmospheric forcing is prescribed using the repeating annual cycle of the Common Ocean Reference Experiment version 2 normal year forcing dataset (COREv2-NYF). The implementation of <sup>13</sup>C-cycling applies isotopic fractionations during air-sea gas exchange, photosynthetic production of organic matter, and formation of calcium carbonate. The sensitivity of dissolved inorganic <sup>13</sup>C in the ocean to the CO<sub>2</sub> gas exchange rate is explored by repeating the simulation twice, once using the latest OMIP-CMIP6 protocol for the k-U<sub>10 </sub>parameterization (standard) and once using the previous OCMIP2 protocol (fast-gas-exchange).</p> <p> </p> <p>Files information:</p> <ul> <li><strong>ocean_static.nc</strong>: Static fields (longitude, latitude, area).</li> <li><strong>1990-2002.ocean_month.nc</strong>: Monthly output between 1990 and 2002 of ocean physical variables (temperature, salinity, averaged mixed layer depth, maximum mixed layer depth).</li> <li><strong>1990-2002.ocean_bling_trc_month_CMIP6.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OMIP-CMIP6 air-sea gas exchange protocol.</li> <li><strong>1970_1989_d13c_org_mldave_CMIP6.nc</strong>: Monthly output between 1970 and 1989 of d<sup>13</sup>C of organic matter averaged over the mixed layer.</li> <li><strong>1990-2002.ocean_bling_trc_month_OCMIP2.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OCMIP2 air-sea gas exchange protocol.</li> </ul> <p>* Biogeochemical variables are dissolved inorganic carbon, dissolved inorganic carbon-13, oxygen, and dissolved inorganic phosphate.</p> <p> </p> <p>======= HOW TO CITE =======</p> <p>This model output can be freely distributed, but please cite it using the following paper:</p> <p>Claret, M., Sonnerup, R. E., & Quay, P. D. (2021). A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C. <em>Global Biogeochemical Cycles</em>, 35, e2020GB006757. <a href="https://doi.org/10.1029/2020GB006757">https://doi.org/10.1029/2020GB006757</a></p> <p> </p> <p>======= ACKNOWLEDGEMENTS =======</p> <p>This work was funded by the National Science Foundation (NSF-OCE 1356756 and NSF-OCE 1829796). We would also like to acknowledge high-performance computing support from Cheyenne (<a href="https://doi.org/10.5065/D6RX99HX">doi:10.5065/D6RX99HX</a>) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF.</p> <p> </p> <p>======= QUESTIONS AND REQUESTS? =======</p> <p>Please contact Mariona Claret (mclaret@uw.edu) or Rolf Sonnerup (rolf@uw.edu).</p> <p> </p>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Data and code for "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling"
<p>The data includes atomic structures, time-dependent dipole moments, and photoabsorption spectra of the systems modeled and analyzed in the article "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling" by Tuomas P. Rossi, Timur Shegai, Paul Erhart, and Tomasz J. Antosiewicz.</p> <p>The input scripts for reproducing the data are also included. The time-dependent density-functional theory calculations use the LCAOTDDFT module of <a href="https://wiki.fysik.dtu.dk/gpaw/">the GPAW code</a>, and the atomic structures are created with <a href="https://wiki.fysik.dtu.dk/ase/">the ASE code</a>.</p> <p>See <em>README.md</em> in the archive for a detailed description.</p>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
Coupled Hydrological and Thermal Models of Rockwall Permafrost
<p>This dataset contains forcing data and selected output of coupled thermal and hydrological simulations applied to a high-elevated rockwall site (the Aiguille du Midi, 3842 m asl, Mont Blanc massif, France).</p> <p>All data are provided as .shp and .shx for display, as well as a .dbf file for quick reading. They are made of 5 columns, whose:</p> <ul> <li>« Node » is the Node ID,</li> <li>« X » is the position (in m) on the x axis,</li> <li>« Y » is the position (in m) on the y axis,</li> <li>« xINIT » is the calculated value for the parameter indicated in the file name(head, temperature, etc.)</li> <li>« Time » is the time step at which the value is calculated.</li> </ul> <p>The model output are gathered according to various cases studies of saturation and water flows. The model settings of the various cases studies are outlined in this file but more details about the mathematical approach and modeling settings and strategy are provided in the study to which the dataset belongs and which was submited for the first time to Journal of Geophysical Research: Earth Surface in July 2020.</p> <ul> <li><strong><em>SaFl </em></strong>corresponds to a saturated with forced water flows case study by assuming a constant recharge and discharge.</li> <li><strong><em>SaNF</em></strong> is a saturated case study with no water flows.</li> <li><strong><em>uSFl</em></strong> corresponds to an unsaturated case study with forced water flows in selected fractures only.</li> <li><strong><em>uSLF</em></strong> is unsaturated with limited water flows.</li> </ul> <p>For <strong><em>SaFl</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at various time step after of transient simulations (1550 AD, 2000 AD, 2015 AD, 2030 AD) such as displayed in Figures S2 and S3.</li> <li>Hydraulic heads (m) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 6 and S3.</li> <li>The ice bulk volumetric fraction at various time step after of transient simulations (2000 AD, 2015 AD, 2030 AD) such as in Figure S3.</li> <li>Temperature (°C) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 4 and S3.</li> </ul> <p>For <strong><em>SaNF </em></strong>the following output are provided:</p> <ul> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> <li>Hydraulic heads (m) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 6.</li> </ul> <p>For <strong><em>uSFl </em></strong>the following output are provided:</p> <ul> <li>Hydraulic heads (m) and temperature (°C) at 2100 AD such as in Figure 4 and 6.</li> <li>Saturation in 852 AD, 853 AD and 854 AD such as in Figure 5.</li> </ul> <p>For <strong><em>uSLF</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at 1550 AD and 2015 AD such as in Figure S2</li> <li>Hydraulic heads (m) after initialization (0 AD) and at 1850 AD and 2100 AD) such as in Figure 6.</li> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> </ul> <p>In addition, the temperature and hydrological (hydraulic head changes for the unsaturated cases studies only) forcing data (« Input_data_boundary_conditions.csv ») are provided. This last file contains:</p> <ul> <li><em>A to D</em>: the surface points extracted along the 4-m resolution DEM transect (Horizontal (X) position in m, Vertical (Y) position (elevation in m)) and MARST map (for the period 1961-1990: MARST<sub>init</sub>) as illustrated on Fig. S1, together with the adjusted MARST to run the model initialization simulations.</li> <li><em>F to I</em>: the surface points taken on for forcingthe model at its upper boundary and in between which the forcing data were interpolated.</li> <li><em>L to BH</em>: Data used for transient simulations with the time in year (<em>L</em>), the MARST anomaly applied to the adjusted MARST (<em>M</em>), the time in days (<em>N</em>), the MARST value applied at each surface node from 1 to 23 (<em>O </em>to <em>AK</em>), as well as the changes in head values applied at at each surface node from 1 to 23 (<em>AL </em>to <em>BH</em>) for the concerned simulations.</li> </ul> <p> </p> <p>More information can be made available by contacting Florence Magnin or Jean-Yves Josnin at <a href="mailto:florence.magnin@univ-smb.fr"><em>florence.magnin@univ-smb.fr</em></a><em> </em>or <a href="mailto:jean-yves.josnin@univ-smb.fr"><em>jean-yves.josnin@univ-smb.fr</em></a></p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</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>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors
<p>Data supporting the manuscript published in PRX Energy titled "Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors". Jupyter notebook and included data for recreating the figures in the paper and for additional analysis.</p>
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
<p>This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO<sub>2</sub> and JRA55-do atmospheric reanalyses. Three simulations are provided:</p> <p><strong>simulation A:</strong> with varying climate forcing conditions and varying atmospheric CO<sub>2</sub></p> <p><strong>simulation B:</strong> with constant climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p><strong>simulation D:</strong> with varying climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p>The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid:</p> <ul> <li><strong>Alk:</strong> Alkalinity (2012-2021)</li> <li><strong>CO2f:</strong> Air-Sea CO<sub>2</sub> flux (1800-2021)</li> <li><strong>DFe: </strong>Dissolved Iron concentration (2012-2021)</li> <li><strong>DIN:</strong> Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li><strong>DIC:</strong> Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li><strong>DSi:</strong> Dissolved Inorganic Silicon concentration (2012-2021)</li> <li><strong>DiaChl:</strong> Chlorophyll a concentration of diatoms (2012-2021)</li> <li><strong>DetC:</strong> Carbon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetCalc: </strong>Calcite concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetSi: </strong>Silicon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>idetz2c:</strong> Carbon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2calc:</strong> Calcite concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2si:</strong> Silicon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>HetC:</strong> Small zooplankton carbon biomass (2012-2021)</li> <li><strong>MLD:</strong> Mixed Layer Depth (2012-2021)</li> <li><strong>NPPn:</strong> Net Primary Production of small pyhtoplankton (2012-2021)</li> <li><strong>NPPd:</strong> Net Primary Production of diatoms (2012-2021)</li> <li><strong>O2:</strong> Dissolved Oxygen concentration (2012-2021)</li> <li><strong>PhyChl:</strong> Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li><strong>Zoo2C:</strong> Macrozooplankton carbon biomass (2012-2021)</li> <li><strong>pCO2s:</strong> Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li><strong>salt:</strong> Salinity (2012-2021)</li> <li><strong>temp:</strong> Temperature (2012-2021)</li> <li><strong>w:</strong> Vertical velocity (2012-2021)</li> </ul> <p>Please contact the corresponding author (ozgur.gurses@awi.de) for further information.</p>
Realistic modeling of mesoscopic ephaptic coupling in the human brain
<p>Comsol models with E-field distributions generated by dipole sources in a realistic head model and a stylized 'toy' model representing a sulcus.</p>
Pre-built Swiss-Calliope sector-coupled energy model
<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <br> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <br> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>
SPT results - Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury
<p>Results of the study "Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury"</p>
Input data for PARASO, a circum-Antarctic fully-coupled 5-component model
<p>Input data for running the PARASO experiments.</p> <p>These files should be extracted, and the folder containing them should be referred to in the `data.cfg` Coral configuration file. See also PARASO documentation from the PARASO sources.</p> <p>The ERA5 forcings (COSMO boundary files and NEMO surface forcings) are not provided herein as they are too large, but we provide:</p> <p>- scripts for downloading and post-processing the ERA5 NEMO forcings;</p> <p>- INT2LM configuration file, with the new Antarctic geometry, to generate COSMO lateral forcings.</p> <p>A 3-month sample of ERA5 data is also available (see <strong>Forcings</strong> below).</p> <p><strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Forcings: </strong>Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5590053">10.5281/zenodo.5590053</a><br> </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>ORAS5: </strong>Zuo, H, Alonso-Balmaseda, M, Mogensen, K, Tietsche, S: OCEAN5: The ECMWF Ocean Reanalysis System and its Real-Time analysis component. 2018. <a href="https://doi.org/10.21957/la2v0442">10.21957/la2v0442</a> downloaded from the <a href="https://www.cen.uni-hamburg.de/en/icdc/data/ocean/easy-init-ocean/ecmwf-oras5.html">ICDC</a> (University of Hamburg) on 01-SEP-2019. <em>(The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.)</em></p> <p><strong>BedMachine: </strong>Morlighem, M. 2020. <em>MEaSUREs BedMachine Antarctica, Version 2</em>. Ice-shelf Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">10.5067/E1QL9HFQ7A8M</a>. Accessed 01-DEC-2019.</p> <p>Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, <em>Nature Geoscience</em>. 13. 132-137. <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a></p> <p><strong>Iceberg forcings: </strong>Jourdain, Nicolas C., Merino, Nacho, Le Sommer, Julien, Durand, Gaël, & Mathiot, Pierre. (2019). Interannual iceberg meltwater fluxes over the Southern Ocean (1.0) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.3514728">10.5281/zenodo.3514728</a></p> <p>Merino N., Jourdain, N. C., Le Sommer, J., Goose, H., Mathiot, P. and Durand, G (2018). Impact of increasing Antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, 121, 76-89. <a href="https://doi.org/10.1016/j.ocemod.2017.11.009">10.1016/j.ocemod.2017.11.009</a></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.