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Adaptive emission reduction approach to reach any global warming target

Abstract

The parties of the Paris Agreement agreed to keep global warming well below 2 °C and pursue efforts to limit it to 1.5 °C. A global stocktake is instituted to assess the necessary emissions reductions every 5 years. Here we propose an adaptive approach to successively quantify global emissions reductions that allow reaching a temperature target within ±0.2 °C, solely based on regularly updated observations of past temperatures, radiative forcing and emissions statistics, and not on climate model projections. Testing this approach using an Earth system model of intermediate complexity demonstrates that defined targets can be reached following a smooth emissions pathway. Its adaptive nature makes the approach robust against inherent uncertainties in observational records, climate sensitivity, effectiveness of emissions reduction implementations and the metric to estimate CO2 equivalent emissions. This approach allows developing emission trajectories for CO2, CH4, N2O and other agents that iteratively adapt to meet a chosen temperature target.

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Fig. 1: Schematic of the AERA to limit global warming.
Fig. 2: Globally averaged surface atmospheric temperature anomalies and GHG emissions following the AERA.
Fig. 3: GHG emissions and aerosol radiative forcing following the AERA for the 1.5 °C temperature target using different assumptions for non-CO2 radiative forcing agents.

Data availability

The Bern3D-LPX model output is publicly available via SEANOE (https://doi.org/10.17882/90901)90. All other data are available in the main text or the supplementary materials.

Code availability

The AERA code is publicly available via https://github.com/Jete90/AERA74.

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Acknowledgements

This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821003 (project 4C, Climate-Carbon Interactions in the Current Century) (J.T., T.L.F., F.J., P.F.) and grant agreement No 101003687 (project PROVIDE, Paris Agreement Overshooting) (T.L.F.), and by the Swiss National Science Foundation under grant PP00P2_198897 (T.L.F.) and grant #200020_200511 (F.J.). The work reflects only the 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. We thank D. Guignet for initial analysis, S. Lienert and A. Jeltsch-Thömmes for help with Bern3D-LPX, and the 4C partners for helpful discussions.

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Authors

Contributions

J.T., T.L.F., F.J. and P.F. conceptualized the project. J.T., M.T.A., T.L.F. and F.J. developed the methodology. J.T. and M.T.A. developed the software. J.T. conducted the investigation. J.T., T.L.F. and F.J. performed visualization. T.L.F., F.J. and P.F. acquired funding. T.L.F. and F.J. administered the project. J.T. wrote the original draft. J.T., M.T.A., T.L.F., F.J. and P.F. reviewed and edited the manuscript.

Corresponding author

Correspondence to Jens Terhaar.

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The authors declare no competing interests.

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Nature Climate Change thanks Piers Forster, Joeri Rogelj and Katsumasa Tanaka for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Historical and simulated globally averaged surface atmospheric temperature anomaly with respect to 1850-1900 for different model configurations.

(a-i) Global mean surface temperature (GMST) from 1850 to 2020 for 9 model configurations with varying ECS without the superimposed inter-annual variability. The blue lines show the simulated GMST, and the orange lines show the determined anthropogenic warming. The diapycnal diffusivity coefficients are 1×10−5, 2×10−5 and 1×10−4 m2 s−1 (from top to bottom) and the different numbers for the internal Bern3D model parameter that accounts for climate feedbacks, which are not explicitly represented in the model, are 0.1, −0.3, and −0.7 W m−2 K−1 (from left to right). The HadCRUT5 observation-based GMST time-series is shown in black in all panels.

Extended Data Fig. 2 Globally averaged surface atmospheric temperature anomaly with respect to 1850-1900, CO2-fe emissions, their annual rate of change, as well as CO2, CH4, and N2O emissions when applying the adaptive emission reduction approach every ten years.

(a) Temperature anomalies with respect to 1850-1900, (b) CO2-fe emissions, and (c) their annual rate of change if the AERA is applied every ten years starting in the year 2025 for the 1.5 °C target (blue) and the 2.0 °C target (orange). In addition, the AERA-calculated emission curves for (d) CO2, (e) CH4, and (f) N2O are shown. CO2 emission curves shown here do not include emissions from prescribed land-use change. As compared to Fig. 2 in the main text, here the AERA is applied every 10 years instead of every 5 years. The thick solid lines show the average of the 8 simulations with varying magnitude and timing of added inter-annual temperature variability of the Bern3D-LPX model configuration with an ECS of 3.2 °C, the thin solid lines show the same for the remaining 8 configurations covering ECS from 1.9 to 5.7 °C, and the shaded area shows the range of all configurations that fall within the likely range of ECS as defined by Sherwood et al.24. The grey shading in (a) indicates the uncertainty with which the anthropogenic warming can be determined (±0.2 °C)26,27,28,29.

Extended Data Fig. 3 Adaptive CO2-fe emissions and resulting temperature anomaly for 1.5 °C and 2.0 °C target for different non-CO2 GHG emissions and aerosol radiative forcing.

(a, c, e, g) Temperature anomalies with respect to 1850-1900 and (b, d, f, h) corresponding CO2-fe emissions if the AERA is applied every five years starting in the year 2025 for the 1.5 °C target (blue) and the 2.0 °C target (orange) for four different idealized cases: (a, b) aerosol radiative forcing decreases exponentially and CO2, CH4, and N2O emissions evolve proportionally, (c, d) aerosol radiative forcing decreases according to the CO2 emissions and CO2, CH4, and N2O emissions evolve proportionally, (e, f) aerosol radiative forcing decreases exponentially but CH4, and N2O emissions follow prescribed trajectories from SSP1-2.6 after 2025 and only CO2 evolves dynamically, and (g, h) aerosol radiative forcing remains constant after 2025 and CO2, CH4, and N2O emissions evolve proportionally. CO2 emission curves shown here do not include emissions from prescribed land-use change. The thick solid lines show the average of the 8 simulations with varying magnitude and timing of added inter-annual temperature variability of the Bern3D-LPX model configuration with an ECS of 3.2 °C and the shaded area shows the range of all configurations that fall within the likely range of ECS as defined by Sherwood et al.24. The grey shading in (a, c, e, g) indicates the uncertainty with which the anthropogenic warming can be determined (±0.2 °C)26,27,28,29. The corresponding CO2, CH4, and N2O emissions and aerosol forcing for each simulated case are shown in Fig. 3.

Extended Data Fig. 4 Globally averaged surface atmospheric temperature anomaly with respect to 1850-1900, CO2-fe emissions, their annual rate of change, as well as CO2, CH4, and N2O emissions following the adaptive emission reduction approach when forcing CO2 emissions to remain constant.

(a) Temperature anomalies with respect to 1850-1900, (b) CO2-fe emissions, and (c) their annual rate of change if the AERA is applied every five years starting in the year 2025 for the 1.5 °C target (blue) and the 2.0 °C target (orange). In addition, the AERA-calculated emission curves for (d) CO2, (e) CH4, and (f) N2O are shown. As compared to Fig. 2 in the main text, here the CO2 emissions are forced to remain constant while only CH4, N2O, VOC, NOx, and CO emissions evolve proportionally. The thick solid lines show the average of the 8 simulations with varying magnitude and timing of added inter-annual temperature variability of the Bern3D-LPX model configuration with an ECS of 3.2 °C, the thin solid lines show the same for the remaining 8 configurations covering ECS from 1.9 to 5.7 °C, and the shaded area shows the range of all configurations that fall within the likely range of ECS as defined by Sherwood et al.24. The grey shading in (a) indicates the uncertainty with which the anthropogenic warming can be determined (±0.2 °C)26,27,28,29.

Extended Data Fig. 5 Globally averaged surface atmospheric temperature anomaly with respect to 1850-1900, CO2-e emissions, their annual rate of change, as well as CO2, CH4, and N2O emissions following the adaptive emission reduction approach using GWP-100 instead of CO2-fe to split CO2-e emissions.

(a) Temperature anomalies with respect to 1850-1900, (b) CO2-e emissions, and (c) their annual rate of change if the AERA is applied every five years starting in the year 2025 for the 1.5 °C target (blue) and the 2.0 °C target (orange). In addition, the AERA-calculated emission curves for (d) CO2, (e) CH4, and (f) N2O are shown. CO2 emission curves shown here do not include emissions from prescribed land-use change. As compared to Fig. 2 in the main text, here the GWP-100 approach was used to calculate CO2 equivalent emissions from CH4 and N2O emissions and the CO2-fe emissions approach was applied to calculate CO2 equivalent emissions from the remaining forcing agents. The thick solid lines show the average of the 8 simulations with varying magnitude and timing of added inter-annual temperature variability of the Bern3D-LPX model configuration with an ECS of 3.2 °C, the thin solid lines show the same for the remaining 8 configurations covering ECS from 1.9 to 5.7 °C, and the shaded area shows the range of all configurations that fall within the likely range of ECS as defined by Sherwood et al.24. The grey shading in (a) indicates the uncertainty with which the anthropogenic warming can be determined (±0.2 °C)26,27,28,29.

Extended Data Fig. 6 Adaptive emissions and resulting temperature anomaly for 1.5 °C and 2.0 °C target with varying compliance.

Temperature from 2020 to 2300 for three model configurations with varying ECS (1.9 °C (a, d, g, j), 3.2 °C (b, e, h, k), 5.7 °C (c, f, i, l)) averaged over four simulations each with different inter-annual variability for the (a-c) 1.5 °C and (g-i) 2.0 °C temperature target and (d-f, j-l) the respective CO2-fe emission curves with different compliance, that is, at each stocktake the 17th (orange), 33rd (blue), 50th (green), 67th (red), or 83rd percentile (violet) was implemented. The percentiles are scaled at each stocktake based on the percentiles of the REB in 2020 from Table 5.8 of the IPCC AR6 WG1 report91. The grey shading in (a, b, c, g, h, i) indicates the uncertainty with which the anthropogenic warming can be determined (±0.2 °C)26,27,28,29.

Extended Data Fig. 7 Overshoot cumulative intensity for 1.5 °C and 2 °C temperature targets dependent on compliance and model configuration.

Overshoot cumulative intensity (°C years), defined as the sum of the overshoot temperatures in each year, in dependence of model configuration (ECS from 1.9 °C to 5.7 °C) and the REB that was used in the AERA (17th, 33rd, 50th, 67th, and 83rd percentile) for (a) 1.5 °C and (b) 2 °C target.

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Supplementary Discussion and Figs. 1–5.

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Terhaar, J., Frölicher, T.L., Aschwanden, M.T. et al. Adaptive emission reduction approach to reach any global warming target. Nat. Clim. Chang. 12, 1136–1142 (2022). https://doi.org/10.1038/s41558-022-01537-9

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