Subgrid Variations of the Cloud Water and Droplet Number Concentration Over Tropical Ocean: Satellite Observations and Implications for Warm Rain Simulation in Climate Models
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherCOPERNICUS GESELLSCHAFT MBH
CitationZhang, Z., Song, H., Ma, P.-L., Larson, V. E., Wang, M., Dong, X., and Wang, J.: Subgrid variations of the cloud water and droplet number concentration over the tropical ocean: satellite observations and implications for warm rain simulations in climate models, Atmos. Chem. Phys., 19, 1077-1096, https://doi.org/10.5194/acp-19-1077-2019, 2019.
Rights© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractOne 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 (E-q) 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 E-N 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 E-N is comparable to or even larger than E-q, 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 E-q center dot E-N. 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.
NoteOpen access journal
VersionFinal published version
SponsorsBiological and Environmental Research program in the US DOE, Office of Science [DE-SC0014641]; National Science Foundation [OAC-1730250]; US DOE, Office of Science, Biological and Environmental Research program; Regional and Global Model Analysis program; Battelle Memorial Institute [DE-AC05-76RL01830]; Climate Model Development and Validation - Biological and Environmental Research program in the US DOE, Office of Science [DE-SC0016287]; Ministry of Science and Technology of China [2017YFA0604001]; US National Science Foundation through the MRI program [CNS-0821258, CNS-1228778]; SCREMS program [DMS-0821311]; UMBC