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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Jan 2020

Submitted as: research article | 02 Jan 2020

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Stratocumulus Cloud Clearings: Statistics from Satellites, Reanalysis Models, and Airborne Measurements

Hossein Dadashazar1, Ewan Crosbie2,3, Mohammad S. Majdi4, Milad Panahi5, Mohammad A. Moghaddam5, Ali Behrangi5, Michael Brunke5, Xubin Zeng5, Haflidi H. Jonsson6, and Armin Sorooshian1,5 Hossein Dadashazar et al.
  • 1Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
  • 2Science Systems and Applications, Inc., Hampton, VA, USA
  • 3NASA Langley Research Center, Hampton, VA, USA
  • 4Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA
  • 5Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
  • 6Naval Postgraduate School, Monterey, CA, USA

Abstract. This study provides a detailed characterization of stratocumulus clearings off the U.S. West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009–2018) of Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify the monthly frequency, growth rate of total area (GRArea), and dimensional characteristics of 306 total clearings. While there is interannual variability, the summer (winter) months experienced the most (least) clearing events with the lowest cloud fractions being along coastal topographical features along the central to northern coast of California including especially just south of Cape Mendocino and Cape Blanco. From 09:00 to 18:00 (PST), the median length, width, and area of clearings increased from 680 to 1231 km, 193 to 443 km, and ~67,000 to ~250,000 km2, respectively. Machine learning was applied to identify the most influential factors governing the GRArea of clearings between 09:00–12:00 PST, which is the time frame of most rapid clearing expansion. The results from Gradient Boosted Regression Tree (GBRT) modeling revealed that air temperature at 850 hPa (T850), specific humidity at 950 hPa (q950), sea surface temperature (SST), and meridional wind speed at 850 hPa (V850) were most impactful in enhancing GRArea. Clearings have distinguishing features such as an enhanced Pacific high shifted more towards northern California, offshore air that is warm and dry, stronger coastal surface winds, enhanced lower tropospheric static stability, and increased subsidence. Although clearings are associated obviously with reduced cloud fraction where they reside, the domain-averaged cloud albedo was actually slightly higher on clearing days as compared to non-clearing days. To validate speculated processes linking environmental parameters to clearing growth rates based on satellite and reanalysis data, airborne data from three case flights were examined. Measurements were compared on both sides of the clear-cloudy border of clearings at multiple altitudes in the boundary layer and free troposphere, with results helping to support links suggested by model simulations. More specifically, airborne data revealed extensive horizontal shear at cloud-relevant altitudes that promoted mixing between clear and cloudy air. Vertical profile data provide support for warm and dry air in the free troposphere additionally promoting expansion of clearings. Airborne data revealed greater evidence of sea salt in clouds on clearing days, pointing to a possible role for, or simply the presence of, this aerosol type in clearing areas coincident with stronger coastal winds.

Hossein Dadashazar et al.
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Hossein Dadashazar et al.
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Publications Copernicus
Short summary
Clearings in the MBL cloud deck of the northeast Pacific Ocean were studied. Remote sensing, reanalysis, and airborne data were used along with machine learning modeling to characterize the spatiotemporal nature of clearings and factor governing their growth. The most significant implications of our results are linked to the modeling of fog and MBL clouds, with implications for a range of societal and environmental issues such as climate, military operations, transportation, and coastal ecology.
Clearings in the MBL cloud deck of the northeast Pacific Ocean were studied. Remote sensing,...