Cloud forcing Totally Explained

Everything about Cloud Forcing totally explained

Cloud forcing (sometimes described as cloud radiative forcing) is the difference between the radiation budget components for average cloud conditions and cloud-free conditions. Much of the interest in cloud forcing relates to its role as a feedback process in the present period of global warming. All global climate models used for climate change projections include the effects of water vapor and cloud forcing.
Roughly speaking, clouds increase the albedo from 15 to 30%, which results in a reduction of absorbed solar radiation of about 44 W/m². This cooling is offset somewhat by the greenhouse effect of clouds which reduces the outgoing longwave radiation by about 31 W/m², so the net cloud forcing of the radiation budget is a loss of about 13 W/m² . Were the clouds to be removed with all else remaining the same, the Earth would gain this last amount in net radiation and begin to warm up. These numbers shouldn't be confused with the usual radiative forcing concept, which is for the change in forcing related to climate change.
Without the inclusion of clouds, water vapor alone contributes between 36-70% of the greenhouse effect on Earth. When considering water vapor and clouds together, the contribution is between 66-85%. In these estimates, the lower bounds are the amount of change if water vapor and clouds are removed, and the upper bounds are the remaining greenhouse effect if everything but water vapor and clouds are removed. Trapping of the long-wave radiation due to the presence of clouds reduces the radiative forcing of the greenhouse gases compared to the clear-sky forcing. However, the magnitude of the effect due to clouds varies for different greenhouse gases. Relative to clear skies, clouds reduce the global mean radiative forcing due to CO₂ by about 15%, and that due to the halocarbons is reduced by up to 30%. Clouds remain one of the largest uncertainties in future projections of climate change by global climate models, owing to the physical complexity of cloud processes and the small scale of individual clouds relative to the size of the model computational grid.

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