Climate change impacts on renewable energy supply

An Author Correction to this article was published on 18 February 2021

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Abstract

Renewable energy resources, which depend on climate, may be susceptible to future climate change. Here we use climate and integrated assessment models to estimate this effect on key renewables. Future potential and costs are quantified across two warming scenarios for eight technologies: utility-scale and rooftop photovoltaic, concentrated solar power, onshore and offshore wind energy, first-generation and lignocellulosic bioenergy, and hydropower. The generated cost–supply curves are then used to estimate energy system impacts. In a baseline warming scenario, the largest impact is increased availability of bioenergy, though this depends on the strength of CO2 fertilization. Impacts on hydropower and wind energy are uncertain, with declines in some regions and increases in others, and impacts on solar power are minor. In a future mitigation scenario, these impacts are smaller, but the energy system response is similar to that in the baseline scenario given a larger reliance of the mitigation scenario on renewables.

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Fig. 1: Multi-model mean change in climate patterns and yields determining renewable energy potential for RCP6.0.
Fig. 2: Multi-model mean change of technical potential under RCP6.0.
Fig. 3: Global mean changes in technical potential for each renewable technology under RCP6.0.
Fig. 4: The combined relative effect of climate impacts on cumulative primary energy supply for each IMAGE model region.

Data availability

Source data are provided with this paper.

Code availability

The code that produced the renewable energy potentials and cost curves can be found at https://github.com/davidgernaat. PBL holds the proprietary rights to the IMAGE computer code; extensive documentation is provided (https://models.pbl.nl/image).

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Acknowledgements

A. Righart is acknowledged for editing part of the manuscript. The research leading to these results has received funding from EU’s Horizon 2020 Navigate (no. 821124). We thank the JPI Climate initiative and participating grant institutes for funding the ISIpedia project.

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Authors

Contributions

D.E.H.J.G. and D.P.v.V. developed the idea. D.E.H.J.G. designed the experiments and wrote the manuscript. S.G.Y. managed all climate input data. C.M. conducted model simulations and provided bioenergy yield data. V.D. calculated the bioenergy potential. All authors discussed the results and contributed to the manuscript.

Corresponding author

Correspondence to David E. H. J. Gernaat.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Andre Lucena, Hannes Weigt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 GCM model mean for historical 30-year (1970–2000) average climate data used as input to calculate energy potentials.

a, Solar irradiance (kWh m−2 day−1) (global horizontal). b, Temperature (°C). c, Wind speeds (m s−1). d, Runoff (kg m−2 s−1). e, Sugar cane and maize yields (crop selected with highest yield per cell) (%). f, Lignocellulosic crop yields (switchgrass and Miscanthus, or trees) (crop selected with highest yield per cell) (%). Source data

Extended Data Fig. 2 GCM model mean change of climate patterns and yields in RCP2.6.

a, solar irradiance (%) (global horizontal). b, temperature (%). (%). c, Wind speed (%). d, Runoff (%). e, Sugar cane and maize yields with CO2 fertilisation (crop selected with highest yield per cell) (%). f, Lignocellulosic crop yields (switchgrass and Miscanthus, or trees) with CO2 fertilisation (crop selected with highest yield per cell) (%). Source data

Extended Data Fig. 3 Schematic illustration showing how climatic parameters can change the design discharge and load factors of a hydropower system.

a, The purple line shows a typical historical discharge pattern at a hydropower location with a wet and dry season. The yellow line shows how new climate-change-induced precipitation patterns influence the discharge pattern, in this case with a wetter wet season and a prolonged dry season. Ordering the yellow line data into a flow duration curve, as illustrated in b, changes the design flow and design load factors. b, The flow duration curve with the new discharge pattern. The new discharge pattern (yellow line in a) forms a new flow duration curve with new design flow (defined as the fourth highest discharge month) and new design load factor (note that the months have shifted, too). The grey lines represent the old climate, the black lines illustrate the new.

Extended Data Fig. 4 Multi-model mean change of technical potential in RCP2.6.

a, Utility-scale PV and rooftop PV. b, Concentrated Solar Power (CSP). c, Onshore and offshore wind energy. d, Hydropower. e, First-generation bioenergy with CO2 fertilisation. f, Lignocellulosic bioenergy with CO2 fertilisation. Source data

Extended Data Fig. 5 The global mean changes in technical potential per renewable technology under RCP2.6.

a, Absolute change in technical potential compared to the historical situation (EJ y−1). b, Relative change in technical potential compared to the historical situation (%). Source data

Extended Data Fig. 6 Shared Socioeconomic Pathways (SSPs) assumptions for IMAGE.

a, Global population (million) for SSP1-3. b, Economic development for SSP1-3 (GDP trillion USD2005 y−1). c, Global final energy demand per sector for SSP1-3. d, Global primary energy use per energy carrier for SSP2 and SSP2-RCP26.

Extended Data Fig. 7 The direct and indirect effect of climate impacts on cumulative primary energy in SSP2-RCP60-CI without CO2 fertilisation (2070–2100).

The top row shows the combined (a), direct (b) and indirect (c) mean change between a run with and without climate impacts on renewables in cumulative energy production (2070–2100) per technology group (%). The bottom row shows the uncertainty using the combined (d), direct (e) and indirect (f) absolute and relative standard deviation of the data shown in the top row. Source data

Extended Data Fig. 8 The direct and indirect effect of climate impacts on cumulative primary energy in SSP2-RCP60-CI with CO2 fertilisation (2070–2100).

The top row shows the combined (a), direct (b) and indirect (c) mean change between a run with and without climate impacts climate impact in cumulative energy production (2070–2100) per technology group (%). The bottom row shows the uncertainty using the combined (d), direct (e) and indirect (f) absolute and relative standard deviation of the data shown in the top row. Source data

Extended Data Fig. 9 The combined relative effect of SSP2-RCP60-HRES climate impacts on cumulative primary energy supply per IMAGE model region.

a, The mean change (over the GCMs) of the cumulative primary energy supply in the period 2070–2100 per technology. b, The absolute (shown in orange gradient) and relative (shown in grey dot size) standard deviation of the data shown in a. Source data

Extended Data Fig. 10 The combined relative effect of SSP2-RCP26 climate impacts on cumulative primary energy supply per IMAGE model region.

a, The mean change (over the GCMs) of the cumulative primary energy supply in the period 2070–2100 per technology. b, The absolute (shown in orange gradient) and relative (shown in grey dot size) standard deviation of the data shown in a. Source data

Supplementary information

Supplementary Information

Supplementary Texts 1–3, Tables 1–5 and Figs. 1–12.

Source data

Source Data Fig. 1

Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) historical (1970–2000), RCP2.6 (2070–2100) and RCP6.0 (2070–2100) climate input data: Solar irradiance (kWh m−2 per day) (global horizontal), temperature (°C), wind speed (m s−1), runoff (kg m−2 s−1), sugar cane and maize yields (t ha−1 yr−1) and lignocellulosic crop yields (switchgrass and Miscanthus, or trees) (t ha−1 yr−1).

Source Data Fig. 2

Technical potential per GCM for the historical (1970–2000) period, and the future RCP2.6 (2070–2100) and RCP6.0 (2070–2100) periods: utility-scale PV and rooftop PV, concentrated solar power (CSP), pnshore and offshore wind energy, hydropower, first-generation bioenergy, and lignocellulosic bioenergy with and without CO2 fertilization.

Source Data Fig. 3

Technical potential per region, GCM and RCP for: utility-scale PV and rooftop PV, concentrated solar power (CSP), onshore and offshore wind energy, hydropower, first-generation bioenergy, and lignocellulosic bioenergy with and without CO2 fertilization.

Source Data Fig. 4

Primary energy supply (2071–2100) based on historical, RCP2.6 and RCP6.0 climate with and without CO2 fertilization (PJ).

Source Data Extended Data Fig. 1

Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) historical (1970–2000), RCP2.6 (2070–2100) and RCP6.0 (2070–2100) climate input data: solar irradiance (kWh m−2 per day) (global horizontal), temperature (°C), wind speed (m s−1), runoff (kg m−2 s−1), sugar cane and maize yields (t ha−1 yr−1) and lignocellulosic crop yields (switchgrass and Miscanthus, or trees) (t ha−1 yr−1).

Source Data Extended Data Fig. 2

Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) historical (1970–2000), RCP2.6 (2070–2100) and RCP6.0 (2070–2100) climate input data: solar irradiance (kWh m−2 per day) (global horizontal), temperature (°C), wind speed (m s−1), runoff (kg m−2 s−1), sugar cane and maize yields (t ha−1 yr−1) and lignocellulosic crop yields (switchgrass and Miscanthus, or trees) (t ha−1 yr−1).

Source Data Extended Data Fig. 4

Technical potential per GCM for the historical (1970–2000) period, and the future RCP2.6 (2070–2100) and RCP6.0 (2070–2100) periods: utility-scale PV and rooftop PV, concentrated solar power (CSP), pnshore and offshore wind energy, hydropower, first-generation bioenergy, and lignocellulosic bioenergy with and without CO2 fertilization.

Source Data Extended Data Fig. 5

Technical potential per region, GCM and RCP for: utility-scale PV and rooftop PV, concentrated solar power (CSP), onshore and offshore wind energy, hydropower, first-generation bioenergy, and lignocellulosic bioenergy with and without CO2 fertilization.

Source Data Extended Data Fig. 7

Primary energy supply (2071–2100) based on historical, RCP2.6 and RCP6.0 climate with and without CO2 fertilization (PJ).

Source Data Extended Data Fig. 8

Primary energy supply (2071–2100) based on historical, RCP2.6 and RCP6.0 climate with and without CO2 fertilization (PJ).

Source Data Extended Data Fig. 9

Primary energy supply (2071–2100) based on historical, RCP2.6 and RCP6.0 climate with and without CO2 fertilization (PJ).

Source Data Extended Data Fig. 10

Primary energy supply (2071–2100) based on historical, RCP2.6 and RCP6.0 climate with and without CO2 fertilization (PJ).

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Gernaat, D.E.H.J., de Boer, H.S., Daioglou, V. et al. Climate change impacts on renewable energy supply. Nat. Clim. Chang. 11, 119–125 (2021). https://doi.org/10.1038/s41558-020-00949-9

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