Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements

Abstract

Abstract. This study provides a detailed characterization of stratocumulus clearings off the US 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 in close proximity to 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, 193 to 443, 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 and 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 anomaly in mean sea level pressure (MSLPanom) were probably most impactful in enhancing GRArea using two scoring schemes. 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 this study's model simulations. More specifically, airborne data revealed the influence of the coastal low-level jet and 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.

Diel Profile of Hydroperoxymethyl Thioformate: Evidence for Surface Deposition and Multiphase Chemistry

“…The imagery in Figure b–d shows a clear diel profile in cloud cover, where at 700 PDT (Figure b) there is presence of stratocumulus clouds over Scripps Pier and most of southern CA. By 1400 PDT (Figure c), the clouds had mostly cleared from the measurement site, and by 1800 PDT (Figure d), the clouds had returned, displaying a diurnal profile that is typical of stratocumulus clearings in the summertime eastern Pacific. ,− …”

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

“…The gradient-boosted regression trees (GBRT) model, classified as a machine learning (ML) model, is used, consisting of several weak learners (i.e., regression trees with a fixed size) that are designed and subsequently trained to improve prediction accuracy by fitting the model’s trees on residuals rather than response values ( Hastie et al, 2009 ). Desirable characteristics of the GBRT model include both its capacity to capture non-linear relationships and being less vulnerable to overfitting ( Persson et al, 2017 ; Fuchs et al, 2018 ; Dadashazar et al, 2020 ). Two separate GBRT models were trained using daily CERES-MODIS N d data (1° × 1°) in winter (DJF) and summer (JJA) to reveal potential variables impacting N d .…”

“…This study makes use of the HU-25 Falcon data from the following instruments: fast cloud droplet probe (FCDP; D p ∼ 3-50 µm) (SPEC Inc.) aerosol and cloud droplet size distributions for quantification of cloud liquid water content (LWC), N d , and aerosol number concentrations with D p exceeding 3 µm in cloud-free air (termed FCDP-aerosol); two-dimensional stereo (2DS; D p ∼ 28.5-1464.9 µm) (SPEC Inc.) probe for estimation of rain water content (RWC) by integrating raindrop (D p ≥ 39.9 µm) size distributions; cloud condensation nuclei (CCN; DMT) counter for CCN number concentrations; laser aerosol spectrometer (LAS; TSI model 3340) and condensation particle counter (CPC; TSI model 3772) for aerosol number concentrations with D p between 0.1-1 µm and above 10 nm, respectively; highresolution time-of-flight aerosol mass spectrometer (AMS; Aerodyne) for submicrometer non-refractory aerosol composition (DeCarlo et al, 2008), operated in 1 Hz Fast-MS mode and averaged to 25 s time resolution; and turbulent air-motion measurement system (TAMMS) for winds and temperature (Thornhill et al, 2003).…”

Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors

“…To compare the traditionally used ordinary least squares regression (OLS) (Cesana & Del Genio, 2021;Klein et al, 2017;McCoy et al, 2017;Myers & Norris, 2016;Myers et al, 2021;Scott et al, 2020) and machine learning techniques that have gained relevance in the field lately, artificial neural networks (ANNs; Andersen et al, 2017) and extreme gradient boosting (XGB; Chen & Guestrin, 2016) are used. XGB is a gradient tree boosting method similar to the popular gradient boosting regression trees (GBRTs) that have been used in many aerosol and cloud related studies recently (Andersen et al, 2021;Dadashazar et al, 2020Dadashazar et al, , 2021Fuchs et al, 2018;Stirnberg et al, 2020Stirnberg et al, , 2021, with the advantages of a built-in regularization techniques and much shorter run times (Chen & Guestrin, 2016).…”

“…In this context, methodological innovation in how these sensitivities are quantified, which is traditionally done in region-specific multiple linear regression frameworks, may facilitate the relevant research (Ceppi & Nowack, 2021). As machine learning methods gain popularity in environmental sciences, these methods have also shown good performance when applied for the quantification of cloud sensitivities in recent studies (Andersen et al, 2017;Dadashazar et al, 2020Dadashazar et al, , 2021Fuchs et al, 2018;Pauli et al, 2020). Nonetheless, their potentials and limitations for CCF analyses have not been fully explored yet.…”