Influence of Cloud Feedback Process on Annual Variation of Global Mean Surface Temperature
Yoko Tsushima
Frontier Research System for Global Change (FRSGC)
Tokyo, Japan
The goal of this study is to estimate cloud's radiative feedback effect on the annual variation of the global mean surface temperature. For this purpose, radiative flux data from the Earth Radiation Budget Experiment are used. We find that the influence of the cloud feedback process upon the change of the global mean surface temperature is quite small, though the increase of the temperature is as much as 3.3K from January to July.
On the global scale, we also find no significant relationship between either solar reflectivity of clouds or effective cloud top height and the annual cycle of surface temperature. The same analysis is repeated using the output from three general circulation models, which explicitly predict microphysical properties of cloud cover. On the global scale, both solar cloud reflectivity and cloud top height increase significantly with the increase of surface temperature in contrast to the observation. Comparative study of the annual variation of simulated and observed cloud forcing is a very promising approach for the evaluation of the cloud feedback process in a model.
