Jun-Mei He , Liang Hong , Ning Lu , Chang-Kun Shao , Kun Yang , Wen-Jun Tang
{"title":"结合CMIP6预估和基于卫星的检索,开发未来每月地表太阳辐射的高分辨率数据集","authors":"Jun-Mei He , Liang Hong , Ning Lu , Chang-Kun Shao , Kun Yang , Wen-Jun Tang","doi":"10.1016/j.accre.2025.02.007","DOIUrl":null,"url":null,"abstract":"<div><div>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/m<sup>2</sup> to 25 W/m<sup>2</sup> and a bias from 5 to 13 W/m<sup>2</sup> to −1 W/m<sup>2</sup> 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.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 2","pages":"Pages 298-311"},"PeriodicalIF":5.2000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a high-resolution dataset of future monthly surface solar radiation by combining CMIP6 projections and satellite-based retrievals\",\"authors\":\"Jun-Mei He , Liang Hong , Ning Lu , Chang-Kun Shao , Kun Yang , Wen-Jun Tang\",\"doi\":\"10.1016/j.accre.2025.02.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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/m<sup>2</sup> to 25 W/m<sup>2</sup> and a bias from 5 to 13 W/m<sup>2</sup> to −1 W/m<sup>2</sup> 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.</div></div>\",\"PeriodicalId\":48628,\"journal\":{\"name\":\"Advances in Climate Change Research\",\"volume\":\"16 2\",\"pages\":\"Pages 298-311\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Climate Change Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674927825000425\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Climate Change Research","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674927825000425","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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 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.
期刊介绍:
Advances in Climate Change Research publishes scientific research and analyses on climate change and the interactions of climate change with society. This journal encompasses basic science and economic, social, and policy research, including studies on mitigation and adaptation to climate change.
Advances in Climate Change Research attempts to promote research in climate change and provide an impetus for the application of research achievements in numerous aspects, such as socioeconomic sustainable development, responses to the adaptation and mitigation of climate change, diplomatic negotiations of climate and environment policies, and the protection and exploitation of natural resources.