The net warming effect of clouds on global surface temperatrue is weakening or even disappearing

This is the dataset generated by our research. This includes training data, validation data, and average annual CET, Ts_all, and Ts_clr estimated by machine learning models. In addition, there are some auxiliary data, such as masks for sea and land, masks for the Tibet Plateau, soil types, and the ERA5 raw dataset used in Figures 1 and 3 of the paper. Note: In this dataset, the word "CRE" in filenames actually means CET.Note: In this dataset, the word "CRE" in filenames actually means CET. Note: In this dataset, the word "CRE" in filenames actually means CET. Ts_all: all-sky surface tempearture Ts_clr: hypothetical clear-sky surface tempeartureCET: Cloud Effect on earth's surface Tempeartue. CET = Ts_all - Ts_clr本数据集由本研究生成。其包含训练数据、验证数据，以及经机器学习模型估算得到的年平均地表云效应（CET, Cloud Effect on Earth's Surface Temperature）、全天空地表温度（Ts_all, all-sky surface temperature）与晴空假想地表温度（Ts_clr, hypothetical clear-sky surface temperature）。此外还附带部分辅助数据，例如海陆掩膜、青藏高原掩膜、土壤类型数据，以及论文图1与图3所使用的ERA5原始数据集。 需注意：本数据集中文件名中的"CRE"实际指代CET。 需注意：本数据集中文件名中的"CRE"实际指代CET。 需注意：本数据集中文件名中的"CRE"实际指代CET。 术语说明如下： Ts_all：全天空地表温度（all-sky surface temperature） Ts_clr：晴空假想地表温度（hypothetical clear-sky surface temperature） CET：地表云效应（Cloud Effect on Earth's Surface Temperature），其计算公式为 CET = Ts_all - Ts_clr