Journal Article
Atmospheric Chemistry and Physics, vol. 19, iss. 2, pp. 1077-1096, 2019
Authors
Zhibo Zhang, Hua Song, Po-Lun Ma, Vincent E. Larson, Minghuai Wang, Xiquan Dong, Jianwu Wang
Abstract
Abstract. One of the challenges in
representing warm rain processes in global climate models (GCMs) is related
to the representation of the subgrid variability of cloud properties, such as
cloud water and cloud droplet number concentration (CDNC), and the effect
thereof on individual precipitation processes such as autoconversion. This
effect is conventionally treated by multiplying the resolved-scale warm rain
process rates by an enhancement factor (Eq) which is derived from
integrating over an assumed subgrid cloud water distribution. The assumed
subgrid cloud distribution remains highly uncertain. In this study, we derive
the subgrid variations of liquid-phase cloud properties over the tropical
ocean using the satellite remote sensing products from Moderate Resolution
Imaging Spectroradiometer (MODIS) and investigate the corresponding
enhancement factors for the GCM parameterization of autoconversion rate. We
find that the conventional approach of using only subgrid variability of
cloud water is insufficient and that the subgrid variability of CDNC, as well
as the correlation between the two, is also important for correctly
simulating the autoconversion process in GCMs. Using the MODIS data which
have near-global data coverage, we find that Eq shows a strong
dependence on cloud regimes due to the fact that the subgrid variability of
cloud water and CDNC is regime dependent. Our analysis shows a significant
increase of Eq from the stratocumulus (Sc) to cumulus (Cu) regions.
Furthermore, the enhancement factor EN due to the subgrid variation of
CDNC is derived from satellite observation for the first time, and results
reveal several regions downwind of biomass burning aerosols (e.g., Gulf of
Guinea, east coast of South Africa), air pollution (i.e., East China Sea),
and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where the
EN is comparable to or even larger than Eq, suggesting an important
role of aerosol in influencing the EN. MODIS observations suggest that
the subgrid variations of cloud liquid water path (LWP) and CDNC are
generally positively correlated. As a result, the combined enhancement
factor, including the effect of LWP and CDNC correlation, is significantly
smaller than the simple product of Eq⋅EN. Given the importance
of warm rain processes in understanding the Earth's system dynamics and water
cycle, we conclude that more observational studies are needed to provide a
better constraint on the warm rain processes in GCMs.
