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Variations in all-sky and clear-sky long-wave effective radiation (LER) in China during the period 2001–2016 were determined using monthly radiative datasets from the Clouds and the Earth’s Radiant Energy System (CERES). Annual and seasonal spatial distributions are found to be quite similar and show a decreasing trend from northwest to southeast, although highest values are found in spring. Mean LER under clear-sky conditions is approximately 20–30 Wm −2  higher than that under all-sky conditions. There is a consistent downward trend in annual and seasonal variations of LER under different weather conditions in China especially after 2007. In northwest China, the eastern Tibetan Plateau, and southeast and northeast China, LER is significantly reduced in two weather conditions and this is more pronounced in spring. However, decreases in clear-sky LER are more obvious. Empirical orthogonal function (EOF) results for LER differences between all-sky conditions and clear-sky conditions were used to analyze regional characteristics and modulating factors. The first mode shows that the LER differences of two weather conditions over China become larger and significant after 2007. The second mode reflects the spatial characteristics, and four climate regions are divided according to the second pattern. According to the definition of LER, regression analysis shows that downward long-wave radiation has a greater influence on LER. When considering cloud effects and other modulating factors, LER has higher correlation with relative humidity in climate regions 3 and 4. However, there are higher negative correlations with middle and high clouds in regions 1 and 2, which are modulated by cloud characteristics. When these factors influence LER together, their correlation is significant in all regions (correlation coefficients are on average higher than 0.7). In summary, changes of LER can well reflect the change of climate system.

Seasonal and Regional Variability of Long-Wave Effective Radiation in China and Associated Modulating Factors