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3 results for “negative CO2 emissions”
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>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
<p>The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled <em>Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.</em></p>
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