Dominant Cloud Controlling Factors for Low‐Level Cloud Fraction: Subtropical Versus Extratropical Oceans

Abstract

To improve cloud feedback understanding and simulation, observations have been used to quantify the rate of change of cloud radiative properties as a function of specific environmental metrics (or cloud controlling factors; CCFs). The study focuses on low‐level cloud dominated regions during 2006–2010. For each ocean gridpoint, Spearman's rank correlation coefficients of daily mean observed cloud fraction versus (a) 10‐m wind, (b) sensible heat flux (SHF), (c) sea surface temperature, (d) estimated inversion strength (EIS), (e) 850 hPa vertical velocity, and (f) the M parameter () are sorted to identify the dominant CCFs in both extratropics (30°–60°N/S) and subtropics (30°S–30°N). A novel map for visualizing dominant CCFs for low‐level cloud fraction reveals that: dominates in the subtropical stratocumulus regions while 10‐m winds dominate in shallow cumulus regions but in the extratropics, a different inversion structure diagnostic (EIS) dominates, while SHF dominates in western boundary current areas.

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

“…We start by discussing the role of EIS, which has been shown to be an important cloud‐controlling factor of CF in the Southern Ocean (Naud et al., 2023). While EIS is associated with increases in ln τ for some cloud regimes, it also decreases ln τ for other cloud regimes (Figure 9a).…”

Meteorology Modulates the Impact of GCM Horizontal Resolution on Underestimation of Midlatitude Ocean Wind Speeds

“…This study compares the passive microwave‐derived U 10m , aggregated as part of the MultiSensor Advanced Climatology of Liquid Water Path (MAC‐LWP) data product (Elsaesser et al., 2017; Naud et al., 2023), to U 10m from CMIP6 GCMs. We focus on the midlatitudes (30°N–80°N and 30°S–60°S).…”

“…Passive microwave retrievals of U 10m are ancillary variables used in the MAC‐LWP climate data record (Elsaesser et al., 2017; Naud et al., 2023). MAC‐LWP utilizes data from the following satellite instruments: Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E), WindSat, Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Microwave Scanning Radiometer 2 (AMSR‐2), and Global Precipitation Measurement (GPM) Microwave Imager (GMI).…”

“…Daily‐mean U 10m from MAC‐LWP for a period of 27 years (1988–2014), set to agree with available GCM data, were used in this study. MAC‐LWP passive microwave wind speed retrievals are corrected for the drifting satellite overpass times and sample the entire diurnal cycle (Elsaesser et al., 2017; Trenberth et al., 2005; F. J. Wentz, 2015) thus ensuring that the daily estimate is an average of validated windspeed retrievals (Elsaesser et al., 2017; Mears et al., 2001; Meissner et al., 2001; O’Dell et al., 2008; F. J. Wentz, 1997) and comparable to other daily‐averaged cloud variables (Naud et al., 2023). Since Multi‐sensor Advanced Climatology (MAC) provides an estimate of the daily‐mean U 10m , as opposed to a retrieval at the satellite overpass time, this enables a direct comparison to CMIP6 GCM daily‐averaged data without the need to implement a satellite simulator that accounts for overpass time.…”

“…MAC wind data can be accessed at Naud et al. (2023). MERRA‐2 data are available at GES DISC Data set: MERRA‐2 inst1_2d_asm_Nx: 2d, 1‐Hourly, Instantaneous, Single‐Level, Assimilation, Single‐Level Diagnostics V5.12.4 (M2I1NXASM 5.12.4) (2015) and GES DISC Data set: MERRA‐2 inst3_3d_asm_Np: 3d, 3‐Hourly, Instantaneous, Pressure‐Level, Assimilation, Assimilated Meteorological Fields V5.12.4 (M2I3NPASM 5.12.4) (2015).…”