Method to Determine A CDR Target


Climate Science Papers that outlines how to calculate Carbon Removal targets from total Anthropogenic emitted Carbon.


Shannon A. Fiume




Collaboration with Foundation for Climate Restoration on a brief using MAGICC 6.8 is in progress. Experiments to test 300 ppm, then to 280 ppm, and calibrating MAGICC 6.8 for removal is in progress. Seeking additional funding for a full detailed paper.

The full experimental paper is on hold until the experiments can be rerun on a full CDRMIP Atmosphere-Ocean General Circulation Model. Once the experiments are run on a full AOGCM the results will more accurately (compared to a Reduced-complexity Model such as MAGICC 6.8) show if the hypothesis of halting anthropogenic emissions from all greenhouse gasses and additionally remove cumulative anthropogenic carbon dioxide, would restore the climate to that existed at 1800 with a CO₂ concentration of about 280 ppm.

Note, a brief commentary on Reduced Complexity Model results may have limited impact as the experiments would need to be repeated on a cluster running the full AOGCM to better project the impact of strong feedbacks.


Theory paper

Presented version: Targeting All Anthropogenic Carbon Dioxide Emissions

Revised version: Alternative Method to Determine a Carbon Dioxide Removal Target (2018)

Authors: Shannon A. Fiume

Short paper outlining how much carbon we need to solidify for complete climate restoration and the carbon's location. Shows why 300 ppm and less needs to be fully explored for complete climate restoration.

Experimental Validation Paper

Working draft title 'Modeling of large-scale CDR Scenarios'.

Experimental validation of Targeting All Anthropogenic Carbon Dioxide Emissions with MAGICC 6, and pymagicc.

Authors: Shannon A. Fiume


The experiments explore the effects of halting anthropogenic emissions from all greenhouse gasses and additionally removing cumulative anthropogenic carbon dioxide in less than 100 years. Two pairs of idealized greenhouse gas emissions modeling experiments were completed in MAGICC 6.801, a Reduced Complexity Model. The experiment pair explores the difference between exponential removal and linear removal for both a forty- and eighty-year time frame. Results were graphed, extending to 2500, showing a converging temperature, greenhouse concentration, and warming. The Reduced Complexity climate model, when all greenhouse gas emissions were halted, and cumulative anthropogenic carbon dioxide was removed, excluding ammonia under .1ºC of warming was realized.

Results Summary

2021 Results

Current results in pymagicc/MAGICC 6.8:,

2018 Results

The experiments removed all anthropogenic carbon dioxide and forced all other GHGs to zero, excluding ammonia, which resulted in under .1ºC of warming.

Graphs of Experiment Scenarios

CO₂ ppm

CO₂-eq ppm

CO₂ RF W/m²

Total Anthro RF W/m²

Global Temp ºC

Maximum Exponential Decline CDR Scenario (emax)






Maximum Linear Decline CDR Scenario (lmax)






Minimum Exponential Decline CDR Scenario (emin)






Minimum Linear Decline CDR Scenario (lmin)












Std dev






2018 Data

CSV of control files (Also see the SCENemax, SCENlmax, etc. tabs in Total Emitted Carbon Graphs and Comparisons gsheets)

SCEN Control files

2021 Code Repository

CDR Modeling Experiment Github repository:


  • wine - Wine Is Not an Emulator, emulation software for running Windows programs on Linux and Unixes including MacOS
  • pymagicc

Pymagicc will install the following:

MAGICC 6.801


Code to translate CSVs and display scenarios are forthcoming.

SSP Database, International Institute for Applied Systems Analysis:, About page


Reference Papers

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The SSP greenhouse gas concentrations and their extensions to 2500, Meinshausen, M., Nicholls, Z., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J.P., Daniel, J.S., John, A., Krummel, P., Luderer, G., Meinshausen, N., Montzka, S., Rayner, P., Reimann, S., Smith, S.J.,  van den Berg, M., Velders, G.J.M., Vollmer, M., Wang, H.J.R., DOI: 10.5194/gmd-2019-222, 2019 (preprint)

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