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Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy

Nature Climate Change volume 13, pages 693–700 (2023)Cite this article

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Abstract

Solar photovoltaic (PV) and wind energy provide carbon-free renewable energy to reach ambitious global carbon-neutrality goals, but their yields are in turn influenced by future climate change. Here, using a bias-corrected large ensemble of multi-model simulations under an envisioned post-pandemic green recovery, we find a general enhancement in solar PV over global land regions, especially in Asia, relative to the well-studied baseline scenario with modest climate change mitigation. Our results also show a notable west-to-east interhemispheric shift of wind energy by the mid-twenty-first century, under the two global carbon-neutral scenarios. Both solar PV and wind energy are projected to have a greater temporal stability in most land regions due to deep decarbonization. The co-benefits in enhancing and stabilizing renewable energy sources demonstrate a beneficial feedback in achieving global carbon neutrality and highlight Asian regions as a likely hotspot for renewable resources in future decades.

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Fig. 1: Changes of solar PVPOT.
Fig. 2: Attribution of solar PVPOT changes.
Fig. 3: Changes of WP.
Fig. 4: Changes of wind speed conditions.
Fig. 5: Changes in variability of solar PVPOT and WP at various time scales.

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Data availability

The ERA5 reanalysis can be obtained at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. The multi-model outputs (experiments names: historical, ssp245, ssp245-cov-strgreen and ssp245-cov-strgreen) are available at https://esgf-node.llnl.gov/search/cmip6/.

Code availability

The bias correction is based on the open-source R package of MBCn (https://rdrr.io/cran/MBC/man/MBCn.html).

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Acknowledgements

We thank the CovidMIP project for providing simulation outputs. We also thank A. J. Cannon for sharing the MBCn package. This work was jointly supported by the Special Project of National Natural Science Foundation of China (42341202 to X.Z., Z.W. and H.C.), the National Science Fund for Distinguished Young Scholars (41825011 to H.C.) and the National Natural Science Foundation of China (42275042 to Z.W. and 42205118 to Y.L.).

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Authors and Affiliations

  1. State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, China

    Yadong Lei, Zhili Wang, Deying Wang, Xiaoye Zhang, Huizheng Che, Junting Zhong, Lifeng Guo, Lei Li & Lin Liu

  2. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China

    Xu Yue & Chenguang Tian

  3. Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Hao Zhou

  4. Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA

    Yangyang Xu

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Contributions

Y.L. and Z.W. conceived the study. Y.L., Z.W. and Y.X. performed the data analysis. Y.L. and Z.W. led the writing of this study, with discussion and improvement from Y.X. D.W., X.Z., H.C., X.Y., C.T., J.Z., L.G., L. Li, H.Z. and L. Liu provided valuable comments and contributed to constructive revisions.

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Correspondence to Zhili Wang.

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Extended data

Extended Data Fig. 1 Changes of solar photovoltaic potential (\({{\boldsymbol{PV}}}_{{\boldsymbol{POT}}}\)) under different climate change scenarios, shown in absolute values rather than relative values in Fig.1.

(a), The changes of annual mean solar \({{PV}}_{{POT}}\) during 2040–2049 under SSP2-4.5 (S245) relative to the historical period (Unitless). (b)-(c), The changes of annual mean solar \({{PV}}_{{POT}}\) during 2040–2049 under the moderate (MOD) and strong (STR) carbon-neutral scenarios relative to S245 (Unitless). Hatched regions represent a change with high inter-model agreement defined as at least three of the four CovidMIP models agreeing on the direction of change.

Extended Data Fig. 2 Annual changes of temperature (T, units: °C) and downwelling shortwave radiation (I, units: W/m2).

(a-b), The changes of annual mean T and I during 2040–2049 under the SSP2-4.5 scenario (S245) relative to the historical period. (c-d), The changes of annual mean T and I during 2040–2049 under the moderate (MOD) carbon-neutral scenario relative to S245. (e-f), The changes of annual mean T and I during 2040–2049 under the strong (STR) carbon-neutral scenario relative to S245. Hatched regions represent a change with high inter-model agreement defined as at least three of the four CovidMIP models agreeing on the direction of change.

Extended Data Fig. 3 Changes of wind power (WP) under different climate change scenarios, shown in absolute values rather than relative values in Fig. 3.

(a), The changes of annual mean WP during 2040–2049 under SSP2-4.5 (S245) relative to the historical period (units: KW). (b)-(c), The changes of annual mean WP during 2040–2049 under the moderate (MOD) and strong (STR) carbon-neutral scenarios relative to S245 (units: KW). Hatched regions represent a change with high inter-model agreement defined as at least three of the four CovidMIP models agreeing on the direction of change.

Extended Data Fig. 4 Changes of wind power density (WPD) (units: %).

(a), The relative changes of annual mean WPD during 2040–2049 under S245 relative to the historical period. (b)-(c), The relative changes of annual mean WPD during 2040–2049 under MOD and STR relative to S245. Hatched regions have changes with high inter-model agreement defined as at least three of the four models agreeing on the sign of changes. (d), Regional mean relative changes of annual WPD during 2040–2049 under the S245 (red bars), MOD (blue bars), and STR (green bars) scenarios relative to the historical period. The black error bars represent one standard deviation of four climate models. The hatched red (blue and green) bars have changes with high inter-model agreement during 2040–2049 under S245 (MOD and STR) relative to the historical period (S245).

Extended Data Fig. 5 Variability of solar photovoltaic potential (\({{\boldsymbol{PV}}}_{{\boldsymbol{POT}}}\)) and wind power (WP) at various time scales in the historical period.

(a), (c), and (e), Day-to-day, month-to-month, and year-to-year variability of solar \({{PV}}_{{POT}}\) in the historical period (units: %). (b), (d), and (f), same as left panels but for WP.

Extended Data Fig. 6 Spatial distributions of observed and simulated solar photovoltaic potential (\({{\boldsymbol{PV}}}_{{\boldsymbol{POT}}}\)) and Wind Power Density (WPD).

(a, b), Observed annual mean solar \({{PV}}_{{POT}}\) (Unitless) and WPD (units: W/m2) in the historical period (1995–2014). (c)-(d), The relative biases of solar \({{PV}}_{{POT}}\) and WPD from raw multi-model mean simulation (units: %). (e)-(f), The relative biases of solar \({{PV}}_{{POT}}\) and WPD from bias-corrected multi-model mean simulation (units: %). Please note the differences in color scales.

Extended Data Fig. 7 Estimate of annual mean wind speed in the historical period (1995-2014).

(a, b), Annual mean wind speed at 10 m and 100 m. (c), Annual mean of scaling factor \(\alpha\) converting 10 m wind speed to 100 m. Scaling factor was calculated from daily data before taking annual average.

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Lei, Y., Wang, Z., Wang, D. et al. Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy. Nat. Clim. Chang. 13, 693–700 (2023). https://doi.org/10.1038/s41558-023-01692-7

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