Abstract. Based on the decoupling parameterization of the cloud-topped planetary
boundary layer, a simple equation is derived to compute the inversion height.
In combination with the lifting condensation level and the amount of water
vapor in near-surface air, we propose a low-level cloud suppression parameter
(LCS) and estimated low-level cloud fraction (ELF), as new proxies for the
analysis of the spatiotemporal variation of the global low-level cloud amount
(LCA). Individual surface and upper-air observations are used to compute LCS
and ELF as well as lower-tropospheric stability (LTS), estimated inversion
strength (EIS), and estimated cloud-top entrainment index (ECTEI), three
proxies for LCA that have been widely used in previous studies. The
spatiotemporal correlations between these proxies and surface-observed LCA
were analyzed. Over the subtropical marine stratocumulus deck, both LTS and EIS
diagnose seasonal–interannual variations of LCA well. However, their use as a global
proxy for LCA is limited due to their weaker and inconsistent relationship
with LCA over land. EIS is anti-correlated with the decoupling strength more
strongly than it is correlated with the inversion strength. Compared with LTS
and EIS, ELF and LCS better diagnose temporal variations of LCA, not only
over the marine stratocumulus deck but also in other regions. However, all
proxies have a weakness in diagnosing interannual variations of LCA in
several subtropical stratocumulus decks. In the analysis using all data, ELF
achieves the best performance in diagnosing spatiotemporal variation of LCA,
explaining about 60 % of the spatial–seasonal–interannual variance of the
seasonal LCA over the globe, which is a much larger percentage than those
explained by LTS (2 %) and EIS (4 %). Our study implies that accurate prediction of inversion base height and
lifting condensation level is a key factor necessary for successful
simulation of global low-level clouds in general circulation models (GCMs).
Strong spatiotemporal correlation between ELF (or LCS) and LCA identified in
our study can be used to evaluate the performance of GCMs, identify the
source of inaccurate simulation of LCA, and better understand climate
sensitivity.