Interactive Cloud Formation and Climatic Temperature Perturbations

Abstract

A one-dimensional climate model with an interactive cloud formation program is developed to investigate its effects on temperature perturbations due to various radiative forcings including doubling of CO2, a 2% increase of the solar constant and the increase of the cirrus IR emissivity. By virtue of the K-theory for turbulence transfer of sensible and latent heat flux we demonstrate that the model may be described by a set of partial differential equations governing the thermodynamic energy balance, water vapor transport, vertical velocity in the cloudy region and cloud cover. In particular, we illustrate that the climatic temperature perturbation experiment may be carried out as a boundary value problem. Moreover, in order to effectively incorporate interactive cloud formation and radiative transfer programs in the model, we have designed a cloud compaction scheme based on statistical and stochastic procedures for the estimate of cloud covers, thicknesses, heights and positions for high, middle and low clouds. We show that, overall, the interactive cloud formation program reduces the sensitivity of temperature increases caused by positive radiative forcings and therefore generates a negative feedback in reference to the fixed cloud program. Low and high cloud formations lead to negative feedbacks as a result of the increased low cloud cover and thickness and decreased high cloud cover and thickness caused by temperature increases. The former strengthens the solar albedo effects, whereas the latter weakens the IR greenhouse effects. On the other hand, the middle cloud formation exhibits a positive feedback because of the reduction in cloud cover and thickness. Further, we show that the cloud cover variation alone will produce a larger reduction in temperature increases. In light of these experimental results, it appears physically appropriate to conclude that an interactive cloud formation program with radiative transfer coupling in the context of a model setting will lead to a negative feedback in temperature sensitivity analyses.