However, the effects of clouds on temperature are uncertain so many studies have tried to identify whether the cloud coverage has a negative or a positive feedback to the warming effects. For example, Dessler concluded that cloud coverage has a positive effect on the temperature. Likewise, Clement, Burgman, and Norris and Lauer et al. claimed the cloud has a positive effect over the eastern Pacific. Spencer and Braswell even claimed not carbon dioxide but cloud cover causes global warming. On the other hand, McLean claimed that the reduced cloud coverage from 1987 to late 1990s accounts for the rise in temperature since 1987. Similarly, Kauppinen, Heionen, and Malmi , discussing the impact of cloud cover on the temperature, claimed that one

The Pearsons correlation test was also used to determine the correlation between the cloud coverage and the temperature. As shown in Fig. 2, the correlation coefficients were -0.5276 for spring, -0.6180 for summer, 0.4517 for autumn, and -0.3106 for winter. Negative correlation coefficients indicate that cloud coverage and temperature have a negative relationship. The coefficients of determination were 0.2783 for spring, 0.3819 for summer, 0.2040 for autumn, and 0.0964 for winter. The absolute values of correlation coefficients were higher than the critical values over all seasons, 0.3008 for spring, summer, and autumn and 0.3044 for winter. Therefore, the hypothesis that the temperature was related cloud was supported by the data with the confidence interval of 0.05. percent increase in low cloud cover decreases 0.11 degree Celsius. Based on these studies, solutions related to clouds were proposed to lessen climate change. For instance,

Lomborg of Copenhagen Consensus Center came up with a proposal to develop cloud whitening technology to reflect solar radiation . Similarly, Russell claimed cloud brightening research had merit in combating global warming. Since these recent studies have reflected the importance of clouds to the climate, it is time to verify the correlations among the CO2 emissions, the cloud coverage, and the temperature and identify the impact

2. Experimetal Setup The Pearsons correlation test was also used to check the correlation between the carbon dioxide emission and the cloud coverage. As shown in Fig. 3, the correlation coefficients were -0.5967 for spring, 0.4718 for summer, -0.3749 for fall, and -0.4015 for winter. Negative correlation coefficients demonstrate that carbon dioxide emissions and cloud coverage have a negative relationship. The coefficients of determination were 0.3561 for spring, 0.2226 for summer, 0.1406 for autumn, and 0.1612 for winter, which is an acceptable number for r2. The absolute values of correlation coefficients were higher than the critical values over all seasons, 0.3008 for spring, summer, and autumn and 0.3044 for winter. Therefore, the possibility that carbon dioxide could be the factor of the cloud coverage is supported by the data with the confidence interval of 0.05.