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Uncertainty in wind and solar projections depends on global and regional climate models

Nina Effenberger, Reto Knutti

Abstract

Ensembles of regional-global climate model combinations show substantial spread in projected wind and solar resources. Using 31 RCM-GCM pairs, we quantify the sources of this spread with a spatially and seasonally resolved variance decomposition, separating contributions from RCMs and GCMs. For both wind speed and solar radiation, RCMs dominate the variability in the absolute historical fields. In contrast, projected changes in wind speed are largely controlled by the driving GCMs, except in mountainous regions where RCM-induced variance becomes larger than that induced by GCMs. For solar radiation, contributions are strongly season-dependent, with RCMs dominating in summer and GCMs in winter. Our findings support that GCM and RCM variability together define the uncertainty of wind and solar climate projections. This provides guidance for designing climate model ensembles that better support uncertainty-aware energy system decisions under climate change.

Uncertainty in wind and solar projections depends on global and regional climate models

Abstract

Ensembles of regional-global climate model combinations show substantial spread in projected wind and solar resources. Using 31 RCM-GCM pairs, we quantify the sources of this spread with a spatially and seasonally resolved variance decomposition, separating contributions from RCMs and GCMs. For both wind speed and solar radiation, RCMs dominate the variability in the absolute historical fields. In contrast, projected changes in wind speed are largely controlled by the driving GCMs, except in mountainous regions where RCM-induced variance becomes larger than that induced by GCMs. For solar radiation, contributions are strongly season-dependent, with RCMs dominating in summer and GCMs in winter. Our findings support that GCM and RCM variability together define the uncertainty of wind and solar climate projections. This provides guidance for designing climate model ensembles that better support uncertainty-aware energy system decisions under climate change.
Paper Structure (17 sections, 9 equations, 16 figures, 3 tables)

This paper contains 17 sections, 9 equations, 16 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Data
  4. Definition of Aspects
  5. Analysis Approach
  6. Robustness
  7. Analysis of variance
  8. Uncertainty partitioning
  9. Results
  10. Variability in climate data
  11. Variability of climate change
  12. Robustness with respect to data selection
  13. Projected changes
  14. Discussion
  15. Conclusion
  16. ...and 2 more sections

Figures (16)

  • Figure 1: Geographic region analyzed in this study, covering the Alps and surrounding areas in Central Europe.
  • Figure 2: Spatial mean changes for the SMHI/MPI-M-MPI-ESM-LR ensemble between the 1995–2004 and 2045–2054 periods. Each map represents an individual ensemble member showing the difference in average fields.
  • Figure 3: Results of the two-way ANOVA on historical solar radiation data between 1995-2004 across the 31 RCM–GCM pairs. The figure shows the ratio between RCM and GCM contribution to variability in different aspects of solar radiation, including seasonal mean, variance, median (50th percentile), and high extremes (90th percentile). RCMs dominate variability in most regions, seasonal differences are also visible.
  • Figure 4: Results of the two-way ANOVA on historical wind speed data between 1995-2004 across the 31 RCM–GCM pairs. The figure shows the ratio between RCM and GCM contribution to variability in different aspects of surface wind speed, including seasonal mean, variance, median (50th percentile), and high extremes (90th percentile). RCMs dominate variability in most regions, GCMs dominate over seas.
  • Figure 5: Results of the two-way ANOVA on combined aspects of solar radiation and wind speed data between 1995-2004 across the 31 RCM–GCM pairs. The figure shows the ratio between RCM and GCM contribution to variability in correlation and drought days. RCMs dominate variability in most regions, particularly in high elevation regions.
  • ...and 11 more figures