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1,477 results for “earth”
Global mean TAS and net TOA flux in CMIP5 piControl and abrupt4xCO2 experiments using EC-Earth model
<p>Near surface air temperature (tas) and net heat flux at top of the atmosphere (NetTOA) from CMIP5 piControl and abrupt4xCO2 experiments with EC-Earth2.3. Each file contains the annual global mean from one experiment for the respective variable, calculated using cdo.</p>
Electronic representation of Russian journals on Earth Sciences
<p>The dataset describes the deprth of digital archives of the most authoritative Russian journal on Earth Sciences. The five figures demostrate the results of graphical processing while preparing digital archives of Geologiya i Geofizika and Zapiski Gornogo Instituta journals, as well as the forms of metadata presentation in electronic archives.</p>
Data of the publication Rare Earth‐Diamond Hybrid Structures for Optical Quantum Technologies
<p>Data of the publication published under the reference: I.G. Balașa et al., Advanced Optical Materials, 2401487 (2024).</p>
Data for "Impact of the Out-of-Plane Flow Shear on Magnetic Reconnection at the Flanks of Earth's Magnetopause"
<p>Data for Figures 3-8 in the paper (data for Figures 5 has been updated on 2024-09-20). The data is compatible with all data-analysis software. Here are the guidelines for reading and visualizing the data:</p> <p>(1) The filenames "noshear", "MA0p7", and "MA2p3" correspond to the simulation runs with no flow shear, Mach number M_A=0.7 flow shear, and M_A=2.3 flow shear.</p> <p>(2) The "upper" and "lower" mean upper and lower current sheet, corresponding to dusk-side and dawn-side reconnection respectively. For the "noshear" case, only the "upper" is considered.</p> <p>(3) Each data file (*.dat) is written in ASCII format and has multiple columns. The first row is the header.</p> <ul> <li>The first column is always the x-coordinates of the figure. </li> <li>For the line plots, all columns starting from the second column are the y-coordinates for different variables. The variables names can be found at the header. </li> <li>For the 2D image plots, the second column is the y-coordinates, and the third column is the value of the variable at a given (x,y) location. </li> </ul> <p>(4) The files "fig4_*_field_*.dat" are the magnetic potential in the x-y domain. The contour of this potential gives the in-plane field line configurations.</p>
Idealized precession GCM simulations with EC-Earth-2-2
<p>This repository contains output from the EC-Earth-2-2 extreme precession simulations (Prec-min / Pmin and Prec-max / Pmax). The experiment details are described in the papers given as references. In this repository, monthly output of the last 50 years of simulation is given for precipitation (in m/s), zonal wind (east-west, in m/s, parameter 165), meridional wind (north-south, in m/s, parameter 166), mixed layer depth (MLD, in m, based on a σ0 difference of 0.01 with the surface), sea surface salinity (in PSU) and thermocline depth (TCD, in m).</p> <p>Furthermore, climatological mean monthly output (averaged over the last 50 years of simulation) is given for surface air temperature (parameter 167), surface sensible heat flux (146), surface latent heat flux (147), surface solar radiation downwards (169), surface thermal radiation downward (175), net surface solar radiation (176), net surface thermal radiation (177), net top solar radiation (at top of atmosphere, 178), and net top thermal radiation (at top of atmosphere, 179). Furthermore the following 3D variables are given on pressure levels: geopotential height (129), specific humidity q (133), zonal wind (east-west, 131), meridional wind (north-south, 132), vertical velocity (135). Please see the ECMWF GRIB parameter database for units and more details on these parameters: https://apps.ecmwf.int/codes/grib/param-db.</p> <p>More output from these simulations can be found through the repository:<br> Bosmans, Joyce. (2019). Idealized orbital extreme GCM simulations with EC-Earth-2-2 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3268528</p> <p>References:</p> <p>Bosmans, J. H. C. (2014). A model perspective on orbital forcing of monsoons and Mediterranean climate using EC-Earth. UU Depts. of Physical Geography and Earth Sciences.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., & Lourens, L. J. (2015). Response of the North African summer monsoon to precession and obliquity forcings in the EC-Earth GCM. Climate dynamics, 44(1-2), 279-297.<br> Bosmans, J. H. C., Hilgen, F. J., Tuenter, E., & Lourens, L. J. (2015). Obliquity forcing of low-latitude climate. Climate of the Past, 11(10), 1335-1346.<br> Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Lourens, L. J., & Rohling, E. J. (2015). Precession and obliquity forcing of the freshwater budget over the Mediterranean. Quaternary Science Reviews, 123, 16-30.<br> Bosmans, J. H. C., Erb, M. P., Dolan, A. M., Drijfhout, S. S., Tuenter, E., Hilgen, F. J., Edge, D., Pope, J.O. & Lourens, L. J. (2018). Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. Quaternary Science Reviews, 188, 121-135.<br> Chetankumar, J., Bosmans, J.H.C., Srinivasan, J. & Chakraborty, A. (2019): The response of tropical precipitation to Earth's precesion: the role of energy fluxes and vertical stability. Climate of the Past, 15, 449-462.</p> <p> </p> <p>Contact:<br> Joyce.Bosmans@ru.nl</p> <p>Acknowledgements:<br> These model simulations were performed during Joyce Bosmans' PhD research, funded by an Utrecht University `Focus en Massa' grant and partly performed at the Royal Netherlands Meteorological Institute (KNMI), with support from ECMWF.</p>
A compositional model of the Earth's mantle transition zone from SS and PP data
<p>This dataset provides the thermochemical model of the Earth's mantle transition zone obtained from the inversion of travel-times and amplitudes of seismic waves reflected at mineralogical phase transitions (PP and SS precursors).</p> <p>3 columns : latitude (degree), longitude (degree), potential temperature Tpot (Kelvin), basalt fraction (min 0.0, max 1.0)</p>
Icons of the tipping points in the Earth System from the H2020 COMFORT project (820989)
<p>Triple threat processes and/or other forcings can lead to changes in the ocean happening fast and abruptly. These changes, referred to as “tipping points”, are critical thresholds in a marine system that, when exceeded, can lead to a significant change in the state of the system, which often can be irreversible. This product has been prepared with the financial support of Norges forskningsråd (Research Council of Norway) (309382) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points). The work reflects only the author’s/authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.</p>
Sentinel-2: Cloud Probability in Earth Engine
<p>Links:</p> <ul> <li><a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY">Sentinel-2: Cloud Probability</a> in Earth Engine's Public Data Catalog</li> <li><a href="https://radiantearth.github.io/stac-browser/#/external/storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json">Sentinel-2: Cloud Probability</a> in Earth Engine STAC viewed with STAC Browser</li> </ul> <p>The S2 cloud probability is created with the <a href="https://github.com/sentinel-hub/sentinel2-cloud-detector">sentinel2-cloud-detector</a> library (using <a href="https://github.com/microsoft/LightGBM">LightGBM</a>). All bands are upsampled using bilinear interpolation to 10m resolution before the gradient boost base algorithm is applied. The resulting <code>0..1</code> floating point probability is scaled to <code>0..100</code> and stored as a UINT8. Areas missing any or all of the bands are masked out. Higher values are more likely to be clouds or highly reflective surfaces (e.g. roof tops or snow).</p> <p>Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas.</p> <p>The Level-2 data can be found in the collection <a href="https://radiantearth.github.io/stac-browser/COPERNICUS_S2_SR">COPERNICUS/S2_SR</a>. The Level-1B data can be found in the collection <a href="https://radiantearth.github.io/stac-browser/COPERNICUS_S2">COPERNICUS/S2</a>. Additional metadata is available on assets in those collections.</p> <p>See <a href="https://developers.google.com/earth-engine/tutorials/community/sentinel-2-s2cloudless">this tutorial</a> explaining how to apply the cloud mask.</p>
Divergent Evolution of Earth and Venus—Transparent Version
<p>The theory of planetary heat pipes now tells us Venus and Earth may follow separate ways even though they shared a similar beginning. In the context of exoplanet studies, this means rocky planets with a hot surface—or with a thick atmosphere that acts like a blanket—may not exhibit plate tectonics. Also read: <a href="https://doi.org/10.1029/2022GL100987">https://doi.org/10.1029/2022GL100987</a></p>
Mapping ecosystem types and land cover types in the Seychelles granitic islands, using Earth Engine and Sentinel-2
<p>We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis</p> <p>The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution.</p> <p>A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.</p>
Supporting data for "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, digitized observation-summary tables, and Python scripts for reproducing the analysis results/ figures presented in "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information. </p>
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
A dataset of Earth Observation Data for Lithological Mapping using Machine Learning
<p><strong>Dataset Information</strong></p> <p>Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data covers wide geographical areas and could be used to solve the problem of the demand of various in-situ data. Lithologicall mapping using remotely sensed data is one of the most challenging applications of ML algorithms. In the framework of the “AI for Geoapplications” project , ML and especially Deep Learning (DL) methodologies are investigated for the identification and characterization of the lithology based on remote sensing data in various pilot areas in Greece. In order to train and test the various ML algorithms, a dataset consisting of 30 ROIs selected mainly from low -vegetated areas, that cover 2% of the total area of Greece was created</p> <p><strong>Dataset Preprocessing</strong></p> <p>Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools.</p> <p>Preprocessing steps:</p> <p>Defining areas with the following properties:</p> <ul> <li> <p>Zero cloud and snow coverage</p> </li> <li> <p>No water bodies</p> </li> <li> <p>Minimum vegetation</p> </li> </ul> <p>For the Aster Images:</p> <ul> <li> <p>Subset on defined areas</p> </li> <li> <p>Mosaic images when needed</p> </li> <li> <p>Digitising clouds</p> </li> </ul> <p>For the Labels:</p> <ul> <li> <p>We got the Soil map from YPEN (<a href="https://ypen.gov.gr/">https://ypen.gov.gr/</a>)</p> </li> <li> <p>Subset on defined areas</p> </li> <li> <p>All categories are represented with good analogies</p> </li> <li> <p>Clip label files with digitised clouds</p> </li> <li> <p>Rasterize</p> </li> </ul> <p> </p> <p>For the Labels we have eighteen categories for the twenty-eight areas that we collected data. We use the following coding for the Labels of our <strong>Dataset</strong>:</p> <table> <tbody> <tr> <td> <p><strong>Alluvial deposits</strong></p> </td> <td> <p><strong>0</strong></p> </td> </tr> <tr> <td> <p><strong>Limestone colluvial deposits</strong></p> </td> <td> <p><strong>1</strong></p> </td> </tr> <tr> <td> <p><strong>Limestones</strong></p> </td> <td> <p><strong>2</strong></p> </td> </tr> <tr> <td> <p><strong>Schists</strong></p> </td> <td> <p><strong>3</strong></p> </td> </tr> <tr> <td> <p><strong>Quaternary sediments</strong></p> </td> <td> <p><strong>4</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss</strong></p> </td> <td> <p><strong>5</strong></p> </td> </tr> <tr> <td> <p><strong>Slope fan debris</strong></p> </td> <td> <p><strong>6</strong></p> </td> </tr> <tr> <td> <p><strong>Mixed flysch</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch shale and cherts</strong></p> </td> <td> <p><strong>8</strong></p> </td> </tr> <tr> <td> <p><strong>Dolomites</strong></p> </td> <td> <p><strong>9</strong></p> </td> </tr> <tr> <td> <p><strong>Granite</strong></p> </td> <td> <p><strong>10</strong></p> </td> </tr> <tr> <td> <p><strong>Sandstone flysch</strong></p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch colluvial deposits</strong></p> </td> <td> <p><strong>12</strong></p> </td> </tr> <tr> <td> <p><strong>Peridotite and Gabbro</strong></p> </td> <td> <p><strong>13</strong></p> </td> </tr> <tr> <td> <p><strong>River bed deposits</strong></p> </td> <td> <p><strong>14</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss colluvial deposits</strong></p> </td> <td> <p><strong>15</strong></p> </td> </tr> <tr> <td> <p><strong>Not available</strong></p> </td> <td> <p><strong>-100</strong></p> </td> </tr> <tr> <td> <p><strong>cloud coverage</strong></p> </td> <td> <p><strong>-999</strong></p> </td> </tr> </tbody> </table> <p>The following table lists the available <strong>areas </strong>and the <strong>categories </strong>that each contains<strong>: <a href="https://docs.google.com/spreadsheets/d/17q0L5Ltz7V4uBY9i6DhULJsJCtf7BOY1nbblB-hf3Pw/edit?usp=share_link">Lithology_Dataset</a> </strong></p> <p> </p> <p>For the <strong>Sentinel-2 images</strong>, we made the following process:</p> <ul> <li> <p><strong>Resampling 10m</strong></p> </li> <li> <p><strong>Subset on defined areas</strong></p> </li> </ul> <p>The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background</p> <p>The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution). </p> <ul> <li> <p>Bands 1-14: Aster</p> </li> <li> <p>Bands 15-24: S2</p> </li> <li> <p>Band 25: Label</p> </li> </ul> <p>The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link: </p> <p><a href="https://github.com/georgegiannop/Lithology">https://github.com/georgegiannop/Lithology</a></p> <p><strong>Citation</strong></p> <p>If you use this dataset in your work, please cite our paper:</p> <p>Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17570, <a href="https://doi.org/10.5194/egusphere-egu23-17570">https://doi.org/10.5194/egusphere-egu23-17570</a>, 2023.</p> <p> </p> <p> </p>
Global Earth Mineral Inventory Data Dump
<p>Contains data dumps of various versions of the Global Earth Mineral Inventory (GEMI) - One of the Deep carbon observatory data legacies. </p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </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>
Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system
<p>The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings.</p> <p><br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows:</p> <p>c#k#_VARIABLE</p> <p>c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided.</p> <p><br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows:</p> <p>c#k#_hyst_o2thermo.nc</p> <p>where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3].</p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions
<p>The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af</p> <p><br> The data are provided as .csv and .nc files<br> There are different types of data</p> <p><br> 1) TIMESERIES DATA (Fig. 1 and 2)<br> =================================<br> The name of the files indicates the variable:<br> co2_ts.csv change in atm. co2 [ppm]<br> cumulativeEmissions_ts.csv cumulative emissions [GtC]<br> cumulativeAOflux_ts.csv cumulative atm-ocean C flux [GtC]<br> cumulativeABflux_ts.csv cumulative atm-land C flux [GtC]<br> sat_ts.csv change in surface air temperature [degC]<br> ohc_ts.csv change in ocean heat content [10^24 J]<br> amoc_ts.csv change in Atlantic meridional overturning circulation strength [Sv]<br> seaice_ts.csv fraction of pre-industrial sea-ice area remaining [fraction of PI]<br> <br> The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows:<br> c4k#_###</p> <p>c4 indicates the maximum co2 as times pre-industrial (4 times)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> ### indicates the rate of CDR:<br> 010: 0.1% yr^-1<br> 010: 0.3% yr^-1<br> 010: 0.5% yr^-1<br> 010: 0.7% yr^-1<br> 100: 1% yr^-1<br> 200: 2% yr^-1<br> 400: 4% yr^-1<br> 600: 6% yr^-1</p> <p><br> 2) HYSTERESIS DATA (Fig. 3)<br> ===========================<br> The name of the files indicates the variables:<br> cumulativeEmissions_sat.csv cumulative emissions and change in surface air temperature [degC]<br> cumulativeEmissions_OHCsurf.csv cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]<br> cumulativeEmissions_o2thermo.csv cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]<br> cumulativeEmissions_OM_arag.csv cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction]</p> <p>each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention)</p> <p><br> 3) SPATIAL DATA (Fig. 4 and 5)<br> ==============================<br> All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:<br> c4k2_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]<br> c4k3_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]<br> c4k5_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]<br> <br> c4k2_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]<br> c4k3_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]<br> c4k5_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]<br> <br> c4k3_100_Om_arag_up mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]<br> c4k3_100_Om_arag_do mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]</p> <p> </p> <p> </p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Dataset and Software for The Relationships Between Large-scale Variations in Shear Velocity, Density, and Compressional Velocity in the Earth's Mantle
<p><strong>Is there a chemically distinct reservoir in the Earth?</strong><br><strong>Do superplumes overly denser-than-average material?</strong><br><strong>Can we detect these anomalies with seismic data?</strong><br><strong>Can we evaluate statistical significance of the features in tomography?</strong></p><p>This study presents the <strong>strongest evidence</strong> to date (ca. 2015) of <strong>large-scale thermo-chemical heterogeneities in the lowermost mantle</strong> using the full spectrum of seismic data. A large data set of surface-wave phase anomalies, body-wave travel times, normal-mode splitting functions and long-period waveforms is used to investigate the scaling between shear velocity, density and compressional velocity in the Earth's mantle (ϱ=dln ρ/dln vS, ν=dln vS/dln vP). Our preferred joint model consists of denser-than-average anomalies (∼1% peak-to-peak) at the base of the mantle roughly coincident with the low-velocity superplumes. The relative variation of shear velocity, density and compressional velocity in our study disfavors a purely thermal contribution to heterogeneity in the lowermost mantle, with implications for the long-term stability and evolution of superplumes.</p><p><strong>Note on Odd Degree Structure:</strong></p><p>Since the self-coupled normal-mode splitting observations constrain only even-degree density variations, all inversions strongly disfavored even-degree vS-ρ correlation (R2 ~ –0.46 to –0.25) in the lowermost mantle, which also disfavors a purely thermal contribution to heterogeneity in this region. However, the starting assumptions on positive vS-ρ correlation persisted in the remaining regions and for odd degree variations. In viscosity inversions with the geoid, opposing sign of the correlation of the longest wavelength even-versus odd-degree structure maps into a region of reduced viscosity in the lower mantle (Rudolph et al., 2020, doi:10.1029/2020gc009335). While important for such dynamical implications, <strong>odd-degree density variations in the lowermost mantle are poorly constrained in this study and should not be interpreted</strong>. We therefore used even-degree variations up to degree 6 for our inferences on thermo-chemical variations in the lowermost mantle (Figure 14), and provide those values in the files below.</p><p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p><p><strong>Reference:</strong></p><p><i>Please cite the following work if you use this data or software.</i></p><ul><li>Moulik, P. & Ekström, G., 2016. The relationships between large-scale variations in shear velocity, density and compressional velocity in the Earth's mantle, <i>J. Geophys. Res.</i>, <strong>121</strong>, doi: <a href="http://dx.doi.org/10.1002/2015JB012679">10.1002/2015JB012679</a>. <a href="https://rajmoulik.com/Publications/MoulikEkstrom_JGR2016.pdf"><i>pdf</i></a></li></ul><p><i>You can also cite the dataset and software from this Zenodo page (Optional).</i></p><p>Moulik, P. & Ekström, G. (2016). Dataset and Software for The Relationships Between Large-scale Variations in Shear Velocity, Density, and Compressional Velocity in the Earth's Mantle. In J. Geophys. Res. Solid Earth (v1.0, Vol. 121, pp. 2737–2771). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8356540">10.5281/zenodo.8356540</a></p><p><strong>Data Products:</strong></p><ul><li><strong>ME16_Figures(</strong><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16_Figures.tar.gz"><strong>.tar.gz</strong></a><strong> or </strong><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16_Figures.pdf"><strong>.pdf</strong></a><strong>)</strong> - contains all figures from the paper in .png format</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16"><strong>ME16</strong></a><strong> - </strong>Coefficients of the spline basis functions for each parameter. Refer cij in equation 3. This is our preferred global model of anisotropic elastic parameters and density. Density variations are allowed to deviate from a constant scaling with shear-velocity variations in the lowermost mantle, which is required to fit the longest-period normal modes (e.g. 0S2). Radial anisotropy is confined to the uppermost mantle (that is, since the anisotropy is parameterized with only the four uppermost splines, it becomes very small below a depth of 250 km, and vanishes at 410 km). This is an updated version of S362ANI+M (Moulik and Ekström, 2014) which did not solve independently for density and compressional-wave velocity variations and imposed a constant scaling throughout the mantle instead (ϱ=0, ν=1/0.55).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/STW105"><strong>STW105</strong></a> - reference model used in ME16. Described in Kustowski et al. (2008)</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/setup.cfg"><strong>setup.cfg</strong></a><strong> - </strong>Some configuration metadata relevant to this model for reproducibility.</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/epix.tar.gz"><strong>epix.tar.gz</strong></a> - Perturbations in horizontally (<i>vsh</i>) and vertically polarized shear velocity (<i>vsv</i>), Voigt-average isotropic shear-wave (<i>vs</i>) and compressional-wave velocity (<i>vp</i>), density (<i>rho</i>). anisotropy (<i>as</i>) and topography of the internal boundaries. This is calculated from the spline coefficients at every 1 by 1 degree cell-centered pixel and at every ~25 km depth region from Moho to the core-mantle boundary and stored in extended pixel format (.epix) ASCII files. Even-degree variations up to degree 6 are provided for density (<i>rho_even6)</i> and isotropic shear-wave velocity (<i>vs_even6</i>), which should be used for density inferences on thermochemical structure (See note above).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/ME16.BOX25km_PIX1X1.avni.nc4"><strong>ME16.BOX25km_PIX1X1.avni.nc4</strong></a> - The perturbations in a standard AVNI format that utilizes the NETCDF4 container format. This file can be read in Python using either xarray or AVNI libraries. For example, to plot even-degree variations up to degree 6 in Voigt-averaged shear velocity perturbations at the bottom of the mantle (2875-2891 km depth)<ul><li><i>import xarray as xr</i></li><li><i>ds = xr.open_dataset('ME16.BOX25km_PIX1X1.avni.nc4')</i></li><li><i>ds['vs_even6'][-1].plot()</i></li></ul></li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/PROGRAMS.tar.gz"><strong>PROGRAMS.tar.gz</strong></a> - Fortran tools for obtaining model values at specific locations. After creating the executables from source code in the <i>src</i> folder, the <i>readme</i> script generates most of the epix files provided in epix.tar.gz above<strong>.</strong></li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/profilescaling.txt"><strong>profilescaling.txt</strong></a> - contains the median scaling ratios as used in Figure 15(a).</li><li><a href="https://zenodo.org/api/files/9aa99409-20ae-495e-b5a3-288fd57ecaeb/scaling3D_MoulikJGR16.tar.gz"><strong>scaling3D_MoulikJGR16.tar.gz</strong></a> - contains the scaling ratios and poisson ratio calculated from the joint model, as used in Figure 15(b).</li></ul>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
ScienceDex guides
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