Abstract
The temporal variability of the 532-nm optical depth of cirrus clouds observed with a lidar at Observatory of Haute-Provence (43.9°N, 5.7°E, and 683-m altitude), has been analyzed. While advection dominates at the first order, variability of the optical depth on timescales of minutes can be related to spatial fluctuations of cloud properties on typical scales of a few kilometers. Log-normal distributions of the optical depth have been used to model the variability of the cirrus optical depth as observed by lidars. These investigations have been performed for three independent classes of cirrus. The log-normal distribution of the optical depth is applicable to the classes of thin clouds; however, for thick clouds, likely due to successive freezing/defreezing effects, the distribution is rather bimodal. This work compares the effects of visible solar light scattered by inhomogeneous cirrus to effects generated by homogeneous clouds having a constant geometrical thickness using the short-scale lidar observations of optical depth distribution and an analytical approach. In the case of thin cirrus, the scattering of solar light reaching the ground is stronger for inhomogeneous than homogeneous cirrus. In case of thick cirrus, multiple-scattering processes need to be considered. The conclusion is that log-normal distribution of the cirrus optical depth should be considered in any radiative calculation in case of model grids larger than a few kilometers whatever the cirrus type is.
Highlights
1.1 Cloudiness and Climate ModellingThe processes controlling the distribution of clouds inducing radiative effects are key factors in climate modelling to determine the rate and magnitude of climate changes
Gu and Liou[19] have tested the radiative effects of broken clouds and multilayer clouds using a 3-D radiative transfer model. They show that cirrus clouds with a maximum overlap tend to produce less shortwave heating than those with random overlap, and they show that broken clouds generate more shortwave heating and more infrared (IR) cooling compared to continuous cloud fields used in the simulation
The lidar ratio (LR) can be directly derived from the lidar extinction profile and backscatter coefficient, large uncertainties and biases concerning the retrieved values are likely obtained[35] in case of subvisible cirrus and thick clouds
Summary
The processes controlling the distribution of clouds inducing radiative effects are key factors in climate modelling to determine the rate and magnitude of climate changes. The cloud radiative force is still one of the major uncertainties for climate modeling as pointed out by the Intergovernmental Panel on Climate Change (IPCC) report.[1] As a consequence of the difficulty to correctly model the cloud effects, numerical climate simulations exhibit large discrepancies in terms of predicted mean temperature changes.[2]. Prognostic cloud schemes explicitly compute the average concentrations of water and ice in the cloudy part of each spatial resolution element (or grid cell), typically the boxes of few degrees in latitude and longitude. The large variability of water vapor covering almost all relevant atmospheric spatial and temporal scales is the main origin of cirrus inhomogeneity and in climate modelling difficulties, as these models use mean gridded values and do Journal of Applied Remote Sensing. This study is an attempt to derive information about the subgrid variability of the cirrus optical depth using lidar measurements
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