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

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dc.contributor.authorZhang, Zhibo
dc.contributor.authorSong, Hua
dc.contributor.authorMa, Po-Lun
dc.contributor.authorLarson, Vincent E.
dc.contributor.authorWang, Minghuai
dc.contributor.authorDong, Xiquan
dc.contributor.authorWang, Jianwu
dc.date.accessioned2020-07-29T16:38:33Z
dc.date.available2020-07-29T16:38:33Z
dc.date.issued2019-01-28
dc.description.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 (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.en_US
dc.description.sponsorshipZhibo Zhang acknowledges the financial support from the Regional and Global Climate Modeling program (grant no. DE-SC0014641) funded by the Biological and Environmental Research program in the US DOE, Office of Science. This work is also supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). Po-Lun Ma was support by the US DOE, Office of Science, Biological and Environmental Research program and Regional and Global Model Analysis program. The Pacific Northwest National Laboratory is operated for the DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. Vincent E. Larson is grateful for financial support from Climate Model Development and Validation (grant no. DE-SC0016287), which is funded by the Biological and Environmental Research program in the US DOE, Office of Science. Minghuai Wang was supported by the Ministry of Science and Technology of China (grant no. 2017YFA0604001). The computations in this study were performed at the UMBC High Performance Computing Facility (HPCF). The facility is supported by the US National Science Foundation through the MRI program (grant nos. CNS-0821258 and CNS-1228778) and the SCREMS program (grant no. DMS-0821311), with substantial support from UMBC.en_US
dc.description.urihttps://www.atmos-chem-phys.net/19/1077/2019/en_US
dc.format.extent20 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2kez9-2uop
dc.identifier.citationZhibo Zhang et al., 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, 2019 https://doi.org/10.5194/acp-19-1077-2019en_US
dc.identifier.urihttps://doi.org/10.5194/acp-19-1077-2019
dc.identifier.urihttp://hdl.handle.net/11603/19269
dc.language.isoen_USen_US
dc.publisherCopernicus Publicationsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.subjectUMBC Big Data Analytics Lab
dc.titleSubgrid variations of the cloud water and droplet number concentration over the tropical ocean: satellite observations and implications for warm rain simulations in climate modelsen_US
dc.typeTexten_US

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