Affiliation:
1. Laboratoire de Météorologie Dynamique/IPSL CNRS Sorbonne Université École Normale Supérieure PSL Research University École Polytechnique Paris France
Abstract
AbstractMany 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 structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low‐level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential‐random overlap that represents 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 generate 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 underestimating 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.
Funder
Agence Nationale de la Recherche
Centre National d’Etudes Spatiales
Publisher
American Geophysical Union (AGU)
Subject
General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献