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Dataset

 

Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.12 (v20220824)

Latest Data Update: 2022-12-16
Status: Ongoing
Online Status: ONLINE
Publication State: Citable
Publication Date: 2023-05-10
DOI Publication Date: 2023-07-03
Download Stats: last 12 months
Dataset Size: 57 Files | 161MB

Abstract

Input Data for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 6.12 shows contribution to effective radiative forcing (ERF) and global mean surface air temperature (GSAT) change from component emissions between 1750 to 2019 based on CMIP6 models.

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How to cite this dataset
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When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:
Szopa, S., V. Naik, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L. Gallardo, A. Kiendler-Scharr, Z. Klimont, H. Liao, N. Unger, and P. Zanis, 2021: Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 817–922, doi:10.1017/9781009157896.008.

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Figure subpanels
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The figure has 2 subpanels, with data provided for both panels.

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List of data provided
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This dataset contains:

- Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models

ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6.

Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b).

‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3).

For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1.

Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively.

Further details on data sources and processing are available in the chapter data table (Table 6.SM.3)

CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.
ERFari stands for Effective Radiative Forcing of aerosol-radiation interactions.
ERFaci stands for Effective Radiative Forcing of aerosol-cloud interactions.

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Data provided in relation to figure
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Data provided in relation to Figure 6.12:

- Data file: hodnebrog_tab3.csv: Radiative forcing for HFCs from Hodnebrog et al (2020)
- Data file: recommended_irf_from_2xCO2_2021_02_25_222758.csv: Impulse response function (IRF) from AR6
- Data file: table2_thornhill2020.csv: ERF from Thornhill et al (2021)
- Data file: attribution_input.csv
- Data file: attribution_input_sd.csv

The folder: 'LLGHG_history_AR6_v9_updated' - contains csv files for each sheet in excel file 'LLGHG_history_AR6_v9_updated.xlsx' which gives historical concentrations from AR6.

The folder CEDS_v2021-02-05_emissions (historical emissions of SLCFs from CEDS) contains the following file formats:

${component}$_${region}$_CEDS_emissions_by${category}$_${type}$_2021_02_05.csv, with:

- ${component}: BC, CH4, CO2, CO, N2O, NH3, NMVOC, NOx, OC, SO2
- ${region}: blank, or 'global'
- ${category}: sector, country, sector and country
- ${type}: blank, or 'fuel'

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Notes on reproducing the figure from the provided data
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Panels were plotted using Python and the code has been embedded in Jupyter notebooks for reproducibility - code is available in the GitHub repository linked in the documentation.

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Sources of additional information
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The following weblinks are provided in the Related Documents section of this catalogue record:
- Link to the figure on the IPCC AR6 website
- Link to the report component containing the figure (Chapter 6)
- Link to the Supplementary Material for Chapter 6, which contains details on the input data used in Table 6.SM.3
- Link to the GitHub repository containing the Jupyter notebooks used to run the code associated with this figure.
- Link to the code for the figure, archived on Zenodo.

Citable as:  Blichner, S.M.; Berntsen, T. (2023): Chapter 6 of the Working Group I Contribution to the IPCC Sixth Assessment Report - Input data for Figure 6.12 (v20220824). NERC EDS Centre for Environmental Data Analysis, 03 July 2023. doi:10.5285/c6b366dabf9b4536b5500e5f1f7a7235. https://dx.doi.org/10.5285/c6b366dabf9b4536b5500e5f1f7a7235
Abbreviation: Not defined
Keywords: IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, historical effective radiative forcing, attributed historical warming

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://creativecommons.org/licenses/by/4.0/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).
Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).

Data Quality:
Data as provided by the IPCC
File Format:
CSV, XLSX, txt, net-CDF

Process overview

This dataset was generated by the computation detailed below.
Title

Caption for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)

Abstract

Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models (Thornhill et al. , 2021b). ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Input Description

None

Output Description

None

Software Reference

None

No variables found.

Coverage
Temporal Range
Start time:
1750-01-01T00:00:00
End time:
2019-12-31T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°