Dominant role of clouds in regulating surface incident solar radiation from 20th century reanalyses over Mainland China.
Saved in:
| Title: | Dominant role of clouds in regulating surface incident solar radiation from 20th century reanalyses over Mainland China. |
|---|---|
| Authors: | Zhao, Jianlei1 (AUTHOR), Ma, Qian1 (AUTHOR) maqian@bnu.edu.cn, Wang, Kaicun2 (AUTHOR), Hao, Hongfei1 (AUTHOR) |
| Source: | Journal of Climate. May2026, Vol. 39 Issue 10, p1-23. 23p. |
| Subjects: | Cloudiness, Solar radiation, Climate change, Machine learning, Aerosols, Shapley Additive Explanations |
| Geographic Terms: | China |
| Abstract: | Long-term historical surface incident solar radiation (Rs) is essential for investigating climate change and evaluating climate model performance. Yet, the reliability of the available century-long reanalysis Rs products over mainland China remains uncertain. In this study, we systematically compare the performance of five 20th-century reanalyses in simulating Rs over mainland China, using sunshine-duration-derived Rs from approximately 2400 meteorological stations (1960–2010). The results indicate that only CERA20C and 20CRv3 provide better simulations of the long-term average Rs over mainland China, while none of the reanalyses adequately reproduce the observed long-term trends. We restrict the attribution analysis to the recent decade of 2000–2010 for CERA20C and 20CRv3, during which reliable clear sky solar radiation (Rc) data, which mainly reflect the aerosols' impact on Rs, are available. Using an Extreme Gradient Boosting (XGBoost) regression model interpreted with the SHapley Additive exPlanations (SHAP) framework, we quantify the relative contributions of biases in Rc and total cloud cover (TCC), together with auxiliary spatial and temporal predictors, to both the monthly biases and the trend biases of Rs. The attribution results consistently show that TCC biases exert larger relative contributions than Rc biases in both reanalysis datasets. Specifically, the fractional contribution of TCC biases is approximately 1.8–2.3 times that of Rc biases for monthly Rs biases, and 2.4–3.8 times for trend biases. Together, these findings highlight the dominant role of cloud-related biases in shaping both the average state and long-term trends of Rs in twentieth-century reanalyses, underscoring the need for improved representations of cloud processes and their interactions with radiation in future reanalysis products. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Climate is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Long-term historical surface incident solar radiation (Rs) is essential for investigating climate change and evaluating climate model performance. Yet, the reliability of the available century-long reanalysis Rs products over mainland China remains uncertain. In this study, we systematically compare the performance of five 20th-century reanalyses in simulating Rs over mainland China, using sunshine-duration-derived Rs from approximately 2400 meteorological stations (1960–2010). The results indicate that only CERA20C and 20CRv3 provide better simulations of the long-term average Rs over mainland China, while none of the reanalyses adequately reproduce the observed long-term trends. We restrict the attribution analysis to the recent decade of 2000–2010 for CERA20C and 20CRv3, during which reliable clear sky solar radiation (Rc) data, which mainly reflect the aerosols' impact on Rs, are available. Using an Extreme Gradient Boosting (XGBoost) regression model interpreted with the SHapley Additive exPlanations (SHAP) framework, we quantify the relative contributions of biases in Rc and total cloud cover (TCC), together with auxiliary spatial and temporal predictors, to both the monthly biases and the trend biases of Rs. The attribution results consistently show that TCC biases exert larger relative contributions than Rc biases in both reanalysis datasets. Specifically, the fractional contribution of TCC biases is approximately 1.8–2.3 times that of Rc biases for monthly Rs biases, and 2.4–3.8 times for trend biases. Together, these findings highlight the dominant role of cloud-related biases in shaping both the average state and long-term trends of Rs in twentieth-century reanalyses, underscoring the need for improved representations of cloud processes and their interactions with radiation in future reanalysis products. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 08948755 |
| DOI: | 10.1175/JCLI-D-25-0016.1 |
