A consistent representation of cloud overlap and cloud subgrid vertical heterogeneity
Une représentation cohérente du recouvrement et de l'hétérogénéité verticale sous-maille des nuages
Résumé
Many global climate models underestimate the cloud cover and overestimate the cloud albedo,
especially for low-level clouds. We determine how a correct representation of the vertical struc-
ture of clouds can fix part of this bias. We use the 1D McICA framework and focus on low-level
clouds. Using LES results as reference, we propose a method based on exponential-random
overlap (ERO) that represents both the cloud overlap between layers and the subgrid cloud
properties over several vertical scales, with a single value of the overlap parameter. Starting
from a coarse vertical grid, representative of atmospheric models, this algorithm is used to gen-
erate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it
on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud
cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if
the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We
confirm that the frequently used maximum-random overlap leads to a significant error by under-
estimating the low-level cloud cover with a relative error of about 50%, that can lead to an error
of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity
of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct.
We also show that the decorrelation lengths used with exponential-random overlap are highly
dependent on the vertical resolutions of models and observations, and we show how to address
this difficulty.