Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 6
Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 6
Data description paper
 | 
06 Jun 2025
Data description paper |  | 06 Jun 2025

Estimation of long-term gridded cloud radiative kernel and radiative effects based on cloud fraction

Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu

Data sets

Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs) Xinyan Liu https://doi.org/10.5281/zenodo.13907217

A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy Xinyan Liu and Tao He https://doi.org/10.5281/zenodo.7478918

ISCCP-FH Cloud radiative kernel for TOA and surface from the ISCCP-FH Flux Production code based on ISCCP-H data Yuanchong Zhang https://doi.org/10.5281/zenodo.4677580

TOA and surface cloud radiative kernels calculated with RRTM (Version v3) Chen Zhou https://doi.org/10.5281/zenodo.5176193

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Short summary
This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of measurements of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
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