Distribution of Different Cloud Types and Their Effects on Near-Surface Air Temperature during Summer Daytime in Central Eastern China

Ting YOU; Hua ZHANG; Haibo WANG; Min ZHAO

doi:10.3878/j.issn.1006-9895.1909.19160

Chinese Journal of Atmospheric Sciences > 2020 > 44(4): 835-850. > DOI: 10.3878/j.issn.1006-9895.1909.19160

YOU Ting, ZHANG Hua, WANG Haibo, et al. 2020. Distribution of Different Cloud Types and Their Effects on Near-Surface Air Temperature during Summer Daytime in Central Eastern China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(4): 835−850. DOI: 10.3878/j.issn.1006-9895.1909.19160

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Distribution of Different Cloud Types and Their Effects on Near-Surface Air Temperature during Summer Daytime in Central Eastern China

Ting YOU^1,,
Hua ZHANG^1, ,,
Haibo WANG^{2, 3},
Min ZHAO¹

1.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
3.
University of Chinese Academy of Sciences, Beijing 100049

Funds: National Key R&D Program of China (Grant 2017YFA0603502), National Natural Science Foundation of China (Grants 91644211, 41575002), Public Meteorology Special Foundation of MOST (Grant GYHY201406023)

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Received Date: May 06, 2019
Available Online: September 08, 2020
Published Date: September 15, 2020

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Abstract

Abstract

The spatial–temporal variation characteristics of the daytime cloud fraction and cloud optical thickness of various cloud types over central eastern China in summer are explored using ERA5 reanalysis data and CERES satellite data during the period of 2001–2017. The effects of various cloud types on the near-surface air temperature are quantitatively analyzed using a radiative–convective model. The observations show that the annual mean daytime total cloud fraction and its optical thickness decrease gradually from south to north, while the upper-middle cloud fraction dominates the total cloud fraction. For annual mean changing rates, the total cloud fraction shows a significant decrease of 0.3% a⁻¹ with the largest contribution from low clouds (−0.27% a⁻¹). The increasing trend of total cloud optical thickness ranges from 0 to 0.1 a⁻¹, where low and lower-middle cloud optical thickness show an increase of 0.06 a⁻¹ and 0.03 a⁻¹, respectively, while the upper-middle and high cloud optical thickness show a decreasing trend of 0.08 a⁻¹ and 0.03 a⁻¹, respectively. The model results show that the annual mean CET (Cloud Effect Temperature) of the four different cloud types are negative, with values of 2.9°C, 2.7°C, 2.2°C, and 1.7°C for low, lower-middle, upper-middle, and high clouds, respectively, indicating the cooling effects of various cloud types. The low cloud CET in the North China Plain is up to −5°C, while the lower-middle and upper-middle clouds are up to −7.8°C in the Sichuan Basin and Yunnan–Guizhou Plateau. The interannual variations of CETof different cloud types and near-surface air temperature have good consistency. The near-surface air temperature decreases (increases) before (after) 2004, while the CET of different cloud types decrease (increase) during this period, which indicates good correspondence between the strengthening (weakening) of the cloud cooling effect and the decrease (increase) of the near-surface temperature. Specifically, a positive correlation of the four cloud types and near-surface air temperature over central eastern China occurs during the daytime in the summer. The annual mean daytime upper-middle cloud fraction plays an important role in all types of clouds over central eastern China in the summer, and the correlation coefficient between the CET and near-surface air temperature is as high as 0.63. In summary, the effects of different cloud types on the near-surface air temperature are different, but all show positive correlations. The quantitative analysis of the influence of different cloud types on the near-surface air temperature can provide a scientific reference for the accurate measurement of global warming, the role of cloud feedback in regional warming, and accurate prediction of regional warming scenarios.
- Cloud types,
- Near-surface air temperature,
- Central eastern China,
- Cloud effect temperature

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