Future changes in surface solar radiation over India: A bias-corrected and downscaled assessment of CMIP6 projections for renewable energy planning

IF 5.6 Q2 ENERGY & FUELS
Ashwin Vijay Jadhav, Rohini Lakshman Bhawar
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引用次数: 0

Abstract

Understanding future changes in surface solar radiation (SSR) is crucial for long-term renewable energy planning, especially in solar-rich countries such as India. However, high-resolution and bias-corrected SSR datasets for India remain scarce, limiting accurate assessment of future solar energy potential. In this study, we develop daily bias-corrected and statistically downscaled (BCD) dataset of SSR at a spatial resolution of 0.25°×0.25° for the Indian subcontinent. Simulations from nine Coupled Model Intercomparison Project Phase-6 (CMIP6) climate models are used for the historical period (1951–2014) and future period (2021–2100) under three Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5). The BCD was implemented using the Empirical Quantile Mapping (EQM) method at each fine-resolution grid point, improving local-scale variability representation. ERA5’s surface solar radiation downward (SSRD) is used as the reference dataset to downscale, bias correct, and validate the models. Evaluation of BCD dataset against observations showed substantial performance improvement, with correlation coefficients increasing from 0.65 to 0.81 (raw) to 0.89–0.94 (corrected) and reducing root mean square error across all models. These improvements in dataset reliability lay a strong foundation for analysing future SSR dynamics under different climate scenarios.
Future projections indicate a progressive decline in SSR with increasing emission intensity: modest changes under SSP1–2.6, with slight positive anomalies over parts of northern India, and pronounced reductions of up to –10 % under SSP2–4.5 and SSP5–8.5, particularly over central, southern, and eastern India. Decadal trends reveal average declines of –0.11 %, –0.52 %, and –0.60 % per decade under SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively, with a temporary slowdown in mid-century (2041–2060) under SSP2–4.5 followed by renewed intensification. The resulting high-resolution BCD dataset not only represents a technical advancement in the robust downscaling of CMIP6 SSR for India but also provides a practical resource to guide solar park siting, optimize grid integration strategies, and support climate-resilient energy policy development.
印度地表太阳辐射的未来变化:对可再生能源规划的CMIP6预估的偏差校正和缩小的评估
了解地表太阳辐射(SSR)的未来变化对于长期的可再生能源规划至关重要,特别是在太阳能资源丰富的国家,如印度。然而,印度的高分辨率和偏差校正SSR数据集仍然稀缺,限制了对未来太阳能潜力的准确评估。在这项研究中,我们开发了印度次大陆SSR的每日偏差校正和统计降尺度(BCD)数据集,空间分辨率为0.25°×0.25°。利用9个耦合模式比对项目第6阶段(CMIP6)气候模式对历史时期(1951—2014年)和未来时期(2021—2100年)在3种共享社会经济路径(ssp1—2.6、ssp2—4.5、ssp5—8.5)下的模拟结果。在每个精细分辨率网格点上使用经验分位数映射(EQM)方法实现BCD,改善了局域尺度变异性的表示。以ERA5的地表太阳向下辐射(SSRD)作为参考数据集,对模型进行降尺度、偏置校正和验证。BCD数据集对观测数据的评估显示出显著的性能改进,相关系数从0.65 - 0.81(原始)增加到0.89-0.94(修正),并降低了所有模型的均方根误差。这些数据可靠性的提高为分析未来不同气候情景下SSR动态奠定了坚实的基础。未来的预测表明,SSR随着排放强度的增加而逐渐下降:在SSP1-2.6下变化不大,在印度北部部分地区有轻微的正异常,在SSP2-4.5和SSP5-8.5下显著减少高达- 10%,特别是在印度中部、南部和东部。年代际趋势显示,在SSP1-2.6、SSP2-4.5和SSP5-8.5条件下,平均每10年下降幅度分别为- 0.11%、- 0.52%和- 0.60%,在SSP2-4.5条件下,在本世纪中期(2041-2060年)出现暂时放缓,随后再次增强。由此产生的高分辨率BCD数据集不仅代表了印度CMIP6 SSR稳健缩减的技术进步,而且为指导太阳能园区选址、优化电网整合策略和支持气候适应型能源政策制定提供了实用资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and climate change
Energy and climate change Global and Planetary Change, Renewable Energy, Sustainability and the Environment, Management, Monitoring, Policy and Law
CiteScore
7.90
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