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

2026-03-20

AI Summary

This study addresses the significant yet poorly understood uncertainties in climate projections of wind and solar energy resources. Leveraging an ensemble of 31 regional–global climate model (RCM–GCM) combinations, it employs a spatiotemporally resolved variance decomposition method to systematically quantify, for the first time, the relative contributions of RCMs and GCMs to uncertainties in historical simulations and future projections of wind speed and surface solar radiation across seasons and terrains. The results reveal that RCMs dominate uncertainty in the spatial patterns of historical climate variables, while GCMs primarily drive uncertainty in projected future wind speed changes—except in mountainous regions. Uncertainty in solar radiation exhibits strong seasonal dependence. These findings provide a critical foundation for constructing uncertainty-aware climate model ensembles tailored to energy-sector decision-making.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• wind energy
• solar radiation
• climate model uncertainty
• regional climate models
• global climate models

Innovation

• variance decomposition
• regional climate models
• global climate models
• renewable energy projections
• climate uncertainty