Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals
Accurate projections of future surface solar radiation (SSR) are important for assessing the impacts of climate change and the potential of solar energy. However, climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit notable uncertainties in SSR projections. This stud...
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Advances in Climate Change Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674927825000425 |
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| author | Jun-Mei He Liang Hong Ning Lu Chang-Kun Shao Kun Yang Wen-Jun Tang |
| author_facet | Jun-Mei He Liang Hong Ning Lu Chang-Kun Shao Kun Yang Wen-Jun Tang |
| author_sort | Jun-Mei He |
| collection | DOAJ |
| description | Accurate projections of future surface solar radiation (SSR) are important for assessing the impacts of climate change and the potential of solar energy. However, climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit notable uncertainties in SSR projections. This study aims to develop a high quality monthly SSR dataset during 1850–2100 by synthesizing CMIP6 model projections and satellite-derived retrievals using a Bayesian Linear Regression (BLR) method. Five CMIP6 models are selected based on their historical performance in simulating SSR. The BLR method assigns gridded weights to each model based on how well the historical simulations matched the satellite-based SSR product (called ISCCP‒ITP‒CNN) over the period 1983–2014. The weighted multi-model ensemble is calculated to generate a synthesized long-term SSR dataset. Evaluation against ground-based observations during historical periods (1960–2017) shows that the synthesized SSR outperforms individual CMIP6 models and their original multi-model mean, with a reduced RMSE from 32 to 36 W/m2 to 25 W/m2 and a bias from 5 to 13 W/m2 to −1 W/m2 on monthly scales. The spatial patterns also agree well with the ISCCP‒ITP‒CNN (1983–2018). The high-resolution (0.1° × 0.1°) synthesized SSR dataset provides monthly projections over historical experiments and four future shared socio-economic pathway (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) during 1850–2100, representing future SSR changes and associated climate impacts. The dataset is expected to enhance simulations of land surface processes and solar energy applications under a variety of future climate scenarios. |
| format | Article |
| id | doaj-art-4f13b9405b0c47f7a3a382fe5ec30d9c |
| institution | Kabale University |
| issn | 1674-9278 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Advances in Climate Change Research |
| spelling | doaj-art-4f13b9405b0c47f7a3a382fe5ec30d9c2025-08-20T03:55:22ZengKeAi Communications Co., Ltd.Advances in Climate Change Research1674-92782025-04-0116229831110.1016/j.accre.2025.02.007Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievalsJun-Mei He0Liang Hong1Ning Lu2Chang-Kun Shao3Kun Yang4Wen-Jun Tang5National Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Yunnan Normal University, Kunming 650500, ChinaYunnan Normal University, Kunming 650500, China; Corresponding author.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, ChinaNational Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, ChinaNational Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Corresponding author.Accurate projections of future surface solar radiation (SSR) are important for assessing the impacts of climate change and the potential of solar energy. However, climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit notable uncertainties in SSR projections. This study aims to develop a high quality monthly SSR dataset during 1850–2100 by synthesizing CMIP6 model projections and satellite-derived retrievals using a Bayesian Linear Regression (BLR) method. Five CMIP6 models are selected based on their historical performance in simulating SSR. The BLR method assigns gridded weights to each model based on how well the historical simulations matched the satellite-based SSR product (called ISCCP‒ITP‒CNN) over the period 1983–2014. The weighted multi-model ensemble is calculated to generate a synthesized long-term SSR dataset. Evaluation against ground-based observations during historical periods (1960–2017) shows that the synthesized SSR outperforms individual CMIP6 models and their original multi-model mean, with a reduced RMSE from 32 to 36 W/m2 to 25 W/m2 and a bias from 5 to 13 W/m2 to −1 W/m2 on monthly scales. The spatial patterns also agree well with the ISCCP‒ITP‒CNN (1983–2018). The high-resolution (0.1° × 0.1°) synthesized SSR dataset provides monthly projections over historical experiments and four future shared socio-economic pathway (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) during 1850–2100, representing future SSR changes and associated climate impacts. The dataset is expected to enhance simulations of land surface processes and solar energy applications under a variety of future climate scenarios.http://www.sciencedirect.com/science/article/pii/S1674927825000425Surface solar radiationCMIP6Bayesian linear regressionCorrection |
| spellingShingle | Jun-Mei He Liang Hong Ning Lu Chang-Kun Shao Kun Yang Wen-Jun Tang Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals Advances in Climate Change Research Surface solar radiation CMIP6 Bayesian linear regression Correction |
| title | Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals |
| title_full | Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals |
| title_fullStr | Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals |
| title_full_unstemmed | Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals |
| title_short | Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals |
| title_sort | development of a high resolution dataset of future monthly surface solar radiation by combining cmip6 projections and satellite based retrievals |
| topic | Surface solar radiation CMIP6 Bayesian linear regression Correction |
| url | http://www.sciencedirect.com/science/article/pii/S1674927825000425 |
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