Abstract
Abstract. We extend upon previous work to examine the relationship between low-level cloud amount (LCA) and various proxies for LCA – estimated low-level cloud fraction (ELF), lower tropospheric stability (LTS), and estimated inversion strength (EIS) – by low-level cloud type (CL) over the globe using individual surface and upper-air observations. Individual CL has its own distinct environmental structure, and therefore our extended analysis by CL can provide insights into the strengths and weaknesses of various proxies and help to improve them. Overall, ELF performs better than LTS and EIS in diagnosing the variations in LCA among various CLs, indicating that a previously identified superior performance of ELF compared to LTS and EIS as a global proxy for LCA comes from its realistic correlations with various CLs rather than with a specific CL. However, ELF, LTS, and EIS have a problem in diagnosing the changes in LCA when noCL (no low-level cloud) is reported and also when Cu (cumulus) is reported over deserts where background stratus does not exist. This incorrect diagnosis of noCL as a cloudy condition is more clearly seen in the analysis of individual CL frequencies binned by proxy values. If noCL is excluded, ELF, LTS, and EIS have good inter-CL correlations with the amount when present (AWP) of individual CLs. In the future, an advanced ELF needs to be formulated to deal with the decrease in LCA when the inversion base height is lower than the lifting condensation level to diagnose cumulus updraft fraction, as well as the amount of stratiform clouds and detrained cumulus, and to parameterize the scale height as a function of appropriate environmental variables.
Highlights
During the past few decades, there have been extensive efforts to quantify the impact of low-level clouds on the Earth’s climate
PS19 showed that estimated low-level cloud fraction (ELF) is superior to lower tropospheric stability (LTS), estimated inversion strength (EIS), and estimated cloud-top entrainment index (ECTEI) in diagnosing the spatial and temporal variations in the seasonal level cloud amount (LCA) over both the ocean and land, including the marine stratocumulus deck, and explains approximately 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 %)
We extended the previous work of Park and Shin (2019) to examine the relationship between various proxies (i.e., LTS, EIS, ECTEI, and ELF) and LCA of individual low-level cloud types (CLs)
Summary
During the past few decades, there have been extensive efforts to quantify the impact of low-level clouds on the Earth’s climate. Park and Shin (2019) (PS19 hereafter) found that these proxies are not strongly correlated with the observed LCA when the analysis domain is extended over the entire globe and suggested an estimated low-level cloud fraction (ELF) as a new proxy for the analysis of the spatiotemporal variations in the global LCA. PS19 showed that ELF is superior to LTS, EIS, and ECTEI in diagnosing the spatial and temporal variations in the seasonal LCA over both the ocean and land, including the marine stratocumulus deck, and explains approximately 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 %).
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