Identifying Meteorological Controls on Open and Closed Mesoscale Cellular Convection Associated with Marine Cold Air Outbreaks. Issue 21 (3rd November 2017)

• Record Type: Journal Article
• Authors: McCoy, Isabel L.; Wood, Robert; Fletcher, Jennifer K.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 21(2017)
• Page Start: 11, 678
• Page End: 11, 702
• Publication Date: 2017-11-03
• Subjects: marine low clouds -- mesoscale cellular convection -- boundary layer clouds -- marine cold air outbreak -- radiation -- climate; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027031
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Mesoscale cellular convective (MCC) clouds occur in large‐scale patterns over the ocean and have important radiative effects on the climate system. An examination of time‐varying meteorological conditions associated with satellite‐observed open and closed MCC clouds is conducted to illustrate the influence of large‐scale meteorological conditions. Marine cold air outbreaks (MCAO) influence the development of open MCC clouds and the transition from closed to open MCC clouds. MCC neural network classifications on Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2008 are collocated with Clouds and the Earth's Radiant Energy System (CERES) data and ERA‐Interim reanalysis to determine the radiative effects of MCC clouds and their thermodynamic environments. Closed MCC clouds are found to have much higher albedo on average than open MCC clouds for the same cloud fraction. Three meteorological control metrics are tested: sea‐air temperature difference (Δ T ), estimated inversion strength (EIS), and a MCAO index ( M ). These predictive metrics illustrate the importance of atmospheric surface forcing and static stability for open and closed MCC cloud formation. Predictive sigmoidal relations are found between M and MCC cloud frequency globally and regionally: negative for closed MCC cloud and positive for open MCC cloud. The open MCC cloud seasonal cycle is well correlated with M, while the seasonality of closed MCC clouds is well correlated with M in the midlatitudes and EIS in the tropics and subtropics. M is found to best distinguish open and closed MCC clouds on average over shorter time scales. The possibility of a MCC cloud feedback is discussed. Key Points: A near‐global satellite data set of marine low cloud is used to determine meteorological drivers of mesoscale cellular convection (MCC) A marine cold air outbreak index combining both surface forcing and lower tropospheric stability is shown to be a good predictor of MCC MCC types have different albedo‐cloud fraction relationships indicating that mesoscale morphology is important for radiative impacts Plain Language Summary: Low clouds are essential to the Earth system energy balance as they trap heat (outgoing energy) from the surface and reflect sunlight (incoming energy) to space. Global models incompletely capture low‐cloud behavior, leading to large uncertainties in model predictions of future climate states. Improving predictions by reducing uncertainty is necessary for developing effective climate change adaptation/mitigation strategies. Our study investigates the environmental conditions influential to low‐cloud development, relating easily modeled quantities to key development mechanisms for improving low‐cloud model representations. Satellite‐measured low clouds are grouped by their large‐scale spatial structure using a pattern‐recognizing program. The two primary cloud‐type patterns are hexagonal with filled (closed) or empty (open) cells. Satellite and reanalysis data (models observationally constrained to real world) are used to examine their characteristics. Closed clouds reflect more sunlight than open clouds for equal cloud cover, indicating that energy balance contributions differ by cloud type and motivating the inclusion of low cloud by type in models. Energy from the surface and turbulence in the lower atmosphere influence open and closed cloud development. Large‐scale motions of air from the high latitudes are particularly conducive to these clouds. M, an easily modeled parameter that quantifies this air motion, predicts open and closed cloud occurrences effectively and may be used to include low cloud by type in models and reduce prediction uncertainty.