# Thin Clouds Control the Cloud Radiative Effect Along the Sc-Cu Transition

Goutam Choudhury, Tom Goren

► To cite this version:

Goutam Choudhury, Tom Goren. Thin Clouds Control the Cloud Radiative Effect Along the Sc-Cu Transition. Journal of Geophysical Research: Atmospheres, 2024, 129 (10), 10.1029/2023jd040406. hal-04682601

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Distributed under a Creative Commons Attribution 4.0 International License
# JGR Atmospheres

## RESEARCH ARTICLE

10.1029/2023JD040406

**Key Points:**

* Thin clouds dim cloud brightness by up to 65 percent at a fixed cloud cover

* Thin clouds exert a greater influence on cloud albedo than the combined effects of cloud water and droplet size under constant cloud cover

* Thin clouds reduce longwave warming by up to 52 percent and modulate the balance between longwave warming and shortwave cooling

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

G. Choudhury,
[goutam.choudhury@biu.ac.il](mailto:goutam.choudhury@biu.ac.il)

**Citation:**

Choudhury, G., & Goren, T. (2024). Thin clouds control the cloud radiative effect along the Sc-Cu transition. *Journal of Geophysical Research: Atmospheres*, 129, e2023JD040406. https://doi.org/10.1029/2023JD040406

Received 8 NOV 2023

Accepted 26 APR 2024

**Author Contributions:**

**Conceptualization:** Goutam Choudhury, Tom Goren

**Data curation:** Goutam Choudhury

**Formal analysis:** Goutam Choudhury

**Funding acquisition:** Tom Goren

**Investigation:** Goutam Choudhury

**Methodology:** Goutam Choudhury, Tom Goren

**Project administration:** Tom Goren

**Software:** Goutam Choudhury

**Supervision:** Tom Goren

**Validation:** Goutam Choudhury

**Visualization:** Goutam Choudhury

**Writing – original draft:** Goutam Choudhury

**Writing – review & editing:** Goutam Choudhury, Tom Goren

© 2024. The Authors.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Thin Clouds Control the Cloud Radiative Effect Along the Sc-Cu Transition

Goutam Choudhury¹ ID and Tom Goren¹,² ID

¹Department of Geography and Environment, Bar-Ilan University, Ramat Gan, Israel, ²Institute for Meteorology, Leipzig University, Leipzig, Germany

**Abstract** In situ and spaceborne studies reveal the prevalence of thin clouds in the major Stratocumulus-to-Cumulus Transition (SCT) regions. Using instantaneous satellite and reanalysis data, this study investigates the properties of thin clouds in the Southeast Pacific Ocean and their impact on the cloud radiative effect (CRE). Our findings demonstrate that thin clouds are intrinsic to the SCT. The overcast stratocumulus-dominated regime exhibits a minimal presence of thin clouds, which become notably prominent after the clouds breakup into the cumulus-dominated regime. The regime dependence of the occurrence of thin clouds is also observed in terms of the marine cold-air outbreak parameter and the sea surface temperature. Thin clouds at a given cloud cover significantly modulate the shortwave (SW) and longwave (LW) components of CRE. SW CRE decreases by 46%–65% with increasing thin cloud cover. They account for a larger variance in cloud albedo than the combined influence of the liquid water path and effective radius. Furthermore, LW CRE decreases by about 12%–52% with thin cloud cover. An increase in the fraction of thin clouds also leads to a larger fraction of negative SW CRE offset by positive LW CRE at a given cloud cover. This LW compensation ranges from approximately 8% at overcast cloud cover to as much as 19% at about 50% cloud cover. These findings elucidate the crucial role of thin clouds, and thus cloud morphology, in modulating CRE and underscore the necessity of their accurate representation in climate models.

**Plain Language Summary** The eastern regions of major global oceans are primarily covered by low-altitude clouds. These bright clouds overlay the dark ocean surface, reflecting incoming solar radiation and cooling the Earth. Concurrently, they act as a barrier to the energy emitted by the ocean surface, preventing it from escaping the Earth and thereby contributing to the Earth's warming. Satellite observations reveal the prevalence of thin clouds in these oceanic regions. In contrast to their thicker counterparts, thin clouds are less effective in reflecting solar radiation and blocking surface emissions. This study investigates the impact of thin clouds on radiation using data from various sources. We show that the presence of thin clouds significantly reduces cloud brightness by approximately 46%–65%, even when the cloud cover remains constant. Thin clouds have a more substantial effect on a cloud's ability to reflect solar radiation compared to the combined influence of cloud water and cloud droplet size. Furthermore, since thin clouds allow some surface emissions to pass through, their presence at a constant cloud cover reduces their warming effect by up to 52%. These findings emphasize the crucial role of thin clouds in shaping the contribution of clouds to Earth's energy balance.

## 1. Introduction

Shallow marine clouds are amongst the most crucial components of Earth's radiative budget. Particularly significant are the bright marine stratocumulus (Sc) cloud decks that typically occur over the colder eastern sectors of the Pacific and Atlantic Oceans (Muhlbauer et al., 2014; Wood, 2012), covering approximately 20% of the Earth's dark ocean on an annual scale (Hahn & Warren, 2007; Warren et al., 1988). Due to their high albedo and extensive global coverage, specifically in regions of high solar influx, Sc clouds exert a substantial impact on the global cloud radiative effect (CRE; Hartmann et al., 1992; Hartmann & Short, 1980). With a top-of-the-atmosphere net negative annual CRE of –8.3 W m⁻² (L'Ecuyer et al., 2019), they contribute significantly to cooling the climate. Understanding how the radiative properties of these clouds will evolve in a warmer future climate is critical to reducing uncertainties in climate predictions (Sherwood et al., 2020; Zelinka et al., 2020).

Cloud fraction ($f_c$), representing the horizontal extent of clouds, constitutes a fundamental component of CRE (Y. C. Chen et al., 2014; Y. Chen et al., 2022; Goren & Rosenfeld, 2014; Rosenfeld et al., 2019; Christensen et al., 2020; Wall et al., 2023). Its variations within a warming climate have been investigated to better understand and constrain
the shortwave (SW) and longwave (LW) cloud feedback (Klein et al., 2017; Qu et al., 2015; Zelinka et al., 2012, 2016). However, the CRE of shallow clouds exhibit substantial variations that cannot be solely explained by the binary cloud fraction (i.e., either cloudy or clear). Variations in SW CRE have been mainly linked to differences in cloud properties, such as cloud water content and droplet concentration (Bender et al., 2016; Christensen et al., 2020; Engström et al., 2015; Zelinka et al., 2016; Y. Chen et al., 2022). The occurrence of diverse cloud morphology patterns exhibiting distinct horizontal and vertical cloud-water distributions may also complicate the CRE-$f_c$ relationship (Alinaghi et al., 2023; Dauhut et al., 2023; Denby, 2023; McCoy et al., 2017, 2023; Mohrmann et al., 2021; Muhlbauer et al., 2014; Stevens et al., 2020; Zhou et al., 2021). For instance, McCoy et al. (2017) highlight significant albedo (and thus SW CRE) variations among closed, open, and disorganised mesoscale cloud morphology patterns, even at fixed cloud cover. This difference in CRE has been linked to the occurrence of optically thin clouds associated with these morphology patterns (McCoy et al., 2023).

LW CRE is predominantly a function of cloud-top temperature (CTT) and $f_c$ (Zelinka et al., 2012, 2016, 2020). Although shallow marine clouds have a relatively small temperature difference between their tops and the ocean surface underneath, optically thin clouds, due to their semi-transparency, can allow a portion of the surface thermal emission to penetrate through them and thus affect the LW CRE (Arouf et al., 2022; Guzman et al., 2017; Vaillant de Guélis, Chepfer, Noel, Guzman, Dubuisson, et al., 2017; Vaillant de Guélis, Chepfer, Noel, Guzman, Winker, et al., 2017). This suggests that optically thin clouds influence the entire spectrum of radiative effects associated with shallow clouds.

A prominent feature observed in Sc-dominant regions across the globe is the transition of overcast Sc cloud decks to deeper and broken cumulus (Cu) clouds (the so-called Sc-Cu transition, hereby abbreviated as SCT) (Breherton & Wyant, 1997; Wood, 2012; Wood & Bretherton, 2004). Along the transition, the overcast Sc clouds gradually thin out as they become decoupled from the surface moisture supply and are eventually replaced by the Cu clouds that develop underneath (Wood, 2012). These Cu clouds are frequently associated with the presence of geometrically and optically thin clouds that exhibit a veil-like appearance (Wood et al., 2018). Satellite observations indicate a significant presence of thin clouds within the SCT regions across the globe, accounting for approximately 30%–50% of the annual cloud cover (Leahy et al., 2012; Guzman et al., 2017; O et al., 2018).

In-situ observations demonstrate that thin veil clouds form within the detrained flow of Cu clouds as they spread horizontally at the top of the boundary layer (Wood et al., 2018; O et al., 2018). These veil clouds are usually associated with a low cloud droplet number concentration ($N_d$; less than $10^{-3}$) and a large droplet radius (>15 µm) (Wood et al., 2018; O et al., 2018). Such a low $N_d$ is also a key reason for their low optical thickness (usually less than 3) (Wood et al., 2018). Simulations from a Lagrangian adiabatic parcel model suggest that the collision-coalescence scavenging in the active Cu updraft is primarily responsible for such low droplet concentrations in the veil clouds (O et al., 2018). This hypothesis is further supported by a satellite-derived negative correlation between the fraction of optically thin clouds and $N_d$ (O et al., 2018).

The SCT displays a diverse array of cloud morphology across a wide range of cloud cover, starting with overcast Sc in the shallow boundary layer, transitioning to Cu in the deeper boundary layer, and eventually dissipating in later stages. Thin cloud cover may also vary at such stages and therefore impact the SW (McCoy et al., 2017, 2023) and LW components of CRE differently (Guzman et al., 2017; Vaillant de Guélis, Chepfer, Noel, Guzman, Dubuisson, et al., 2017). Given the prevalence of thin clouds in the SCT region (Leahy et al., 2012; Guzman et al., 2017; O et al., 2018), quantifying their radiative effects is critical to furthering our understanding of the role of clouds in the global radiative budget. In this paper, we use simultaneous satellite and reanalysis data to investigate the properties of thin clouds and untangle their contribution to the radiative effects of shallow clouds. We further discuss the key role of cloud morphology in cloud-radiation and aerosol-cloud-radiation interactions. The manuscript is organized as follows: Section 2 describes the data and methods used in the study; Section 3 presents the results, which are discussed in Section 4 and summarized in Section 5.

## 2. Data and Methods

Spaceborne lidar measurements over global oceans indicate a dominant presence of optically thin clouds to the west of Sc-dominant regions, with their highest occurrence over the Southeast Pacific Ocean (SEP) (Leahy et al., 2012; O et al., 2018). Therefore, we select a domain over the SEP bounded by latitudes ranging from 10°S to 30°S and longitudes from 80°W to 110°W, as shown in Figure 1a. The data sets considered in this work include cloud observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (Platnick
<figure><img src="image_3.png" /></figure>

MODIS AQUA, DATE: 08-Mar-2015 20:07:02, LAT: -22.5, LON: -83.5, f_c: 0.588

<caption>**Figure 1.** Defining thin clouds using optical thickness ($\tau$) and reflectance ($R$). Panel (a) depicts a MODIS true-color image, highlighting the study domain and a zoomed-in 1° by 1° scene, whose properties are shown in the remaining panels. Panels (b) and (c) show $R$ at 0.64 µm and $\tau$, respectively. Black pixels in Panel (d) represent the thin clouds identified using $R$ and $\tau$. Panel (e) shows the thin cloud pixels identified using only $\tau$. The location and time of the observation are provided at the bottom of the figure. (Panel (a) credits: https://worldview.earthdata.nasa.gov/).</caption>

et al., 2015), radiation measurements from the Clouds and the Earth's Radiant Energy System (CERES) sensor (Doelling et al., 2013), and meteorological properties from the European Centre for Mid-range Weather Forecasting ERA5 reanalysis data (“The ERA5 global reanalysis”, 2020). The MODIS, CERES, and ERA5 data are considered over a span of 6 years, from 2015 to 2020. The data products, along with the post-processing methods, are described in the subsequent sections.

## 2.1. MODIS Cloud Observations

For inferring instantaneous cloud properties, we use the MODIS Aqua level-2 collection 6.1 cloud product (Platnick et al., 2015), which includes cloud optical and microphysical properties at a nadir resolution of 1 km by 1 km. Parameters used in this study include the corrected reflectance at 0.64 µm ($R$), cloud fraction ($f_c$), cloud optical thickness ($\tau$), liquid water path ($\mathcal{L}$), CTT and cloud effective radius ($R_c$). We filter the MODIS scenes to only include single-layered liquid-phase clouds. Single-layered clouds are identified using the MODIS cloud multi-layer flag, and liquid clouds are filtered using the MODIS cloud-phase metric. We exclude pixels with sensor and solar zenith angles greater than 41°, as they may have retrieval-related issues (Grosvenor & Wood, 2014). The filtered cloud properties are then gridded into a uniform latitude and longitude grid of 1° by 1°. As thin clouds mostly occur at the edges of cloud cores (Eytan et al., 2020; Koren et al., 2007; Wood et al., 2018; O et al., 2018; Mieslinger et al., 2022), their CTT may not be reliable due to partial pixel filling. Therefore, assuming a uniform cloud top height within a scene, we use the core CTT as a representative of the CTT of the entire cloud within the scene. Cores are defined as the pixels corresponding to the top 10 percentile of $R$ (indicating the brightest pixels) within a scene (Goren & Rosenfeld, 2014). Additionally, we classify scenes with a mean $R_c$ larger than 14 µm as precipitating (Fan et al., 2020; Goren & Rosenfeld, 2014; Rosenfeld et al., 2012). To quantify the heterogeneity in the distribution of cloud water within a scene, we use the dispersion ratio ($D$) of $\mathcal{L}$, defined as the ratio between the standard deviation and the mean of $\mathcal{L}$ (Feingold et al., 2022; Wood, 2006).
Studies based on spaceborne lidar suggest that thin veil clouds, which do not completely attenuate the lidar signal, usually have a $\tau \le 3$ (Leahy et al., 2012; O et al., 2018). Following this, McCoy et al. (2023) used the MODIS cloud product and defined thin clouds as those pixels that have a $\tau \le 3$. However, this way of defining thin cloud cover ($f_{c,thin}$) using $\tau$ alone excludes those cloudy pixels that do not have a valid $\tau$ retrieval (King et al., 1997; Platnick et al., 2015) and may underestimate $f_{c,thin}$ (see Figures 1b and 1c). To overcome this issue, we use a combination of MODIS-derived $R$ and $\tau$ to identify thin clouds. We define $f_{c,thin}$ for a scene as the fraction of pixels with $R$ less than or equal to the $R$ matched to a $\tau$ of 3 ($R_{\tau=3}$). $R_{\tau=3}$ is estimated for each scene by fitting a second-degree polynomial regression between $\tau$ and $R$. As shown for an example case in Figures 1d and 1e, combining $\tau$ and $R$ yields a better estimate of $f_{c,thin}$, which would be otherwise underestimated by about 55% if only $\tau$ was used. The fraction of thin cloud cover ($f_{thin}$) is defined as the ratio between $f_{c,thin}$ and $f_c$ ($f_{thin} = f_{c,thin}/f_c$).

Note that the spatial variation of $f_{thin}$ over the considered study domain (see Fig. S1a in Supporting Information S2) is found to be consistent with previous spaceborne lidar-based results (Leahy et al., 2012; O et al., 2018). A limitation of defining veil clouds in terms of cloud optical properties is that it may also include optically thin shallow Cu and stratus clouds. Because our focus is on thin detrained clouds located at the uppermost part of the decoupled boundary layer, typically associated with an active cloud core (Wood et al., 2018; O et al., 2018), we restrict our analysis to cloudy scenes with a core $L$ greater than 100 g m$^{-2}$. This criterion allows to exclude scenes that are primarily dominated by thin Cu and stratus clouds.

## 2.2. CERES Radiation Measurements

For inferring information on cloud radiative properties, we use the CERES SYN Edition 4A product (Doelling et al., 2013, 2016), which includes radiative fluxes and albedo for cloudy and clear-sky conditions at a uniform latitude-longitude resolution of 1° by 1° and a temporal resolution of 1 hr. Radiative fluxes and albedo are interpolated temporally to MODIS observations. The LW and SW CRE at the top of the atmosphere (TOA) are estimated by subtracting the corresponding fluxes for clear-sky and all-sky conditions. Similarly, cloud albedo ($A_c$) is estimated by subtracting the albedo corresponding to all-sky and clear-sky conditions.

## 2.3. ERA5 Reanalysis Data

SST, temperature profile, and pressure profile are derived from ERA5 hourly data, available at a uniform latitude-longitude resolution of 0.25° by 0.25° (Hersbach et al., 2023a, 2023b). We use these properties to estimate the marine cold-air outbreak parameter ($M$) (Fletcher et al., 2016; Kolstad et al., 2009), a measure of lower tropospheric stability defined as

$$M = \theta_s - \theta_{800}, \quad (1)$$

where $\theta_s$ and $\theta_{800}$ represents the potential temperatures at the surface and 800 hPa, respectively. Note that the surface here refers to the sea surface and not the near-surface layer in the atmosphere. $M$ is strongly correlated with the boundary layer height (BLH; McCoy et al., 2023; Naud et al., 2018, 2020), which has previously been found to have a strong correlation with the thin cloud fraction (O et al., 2018). The $M$ index and SST are then re-gridded to a uniform latitude-longitude grid of 1° by 1° and interpolated temporally to match the MODIS observations.

# 3. Results

## 3.1. Non-Monotonic Relationship Between $f_{c,thin}$ and $f_c$

In SCT regions, the climatological progression of Sc development involves the formation of overcast clouds ($f_c = 1$) over colder SSTs and shallow boundary layers near the coastal region, which breakup ($f_c < 1$) as they move towards warmer SSTs and deeper boundary layers (Sandu & Stevens, 2011; Wood, 2012; Wood & Bretherton, 2004). This cloud breaking is also associated with the formation of precipitation and the development of Cu clouds (Diamond et al., 2022; Feingold et al., 2010; Rosenfeld et al., 2006; Wang & Feingold, 2009; Yamaguchi et al., 2017). The variation in $f_{c,thin}$ with $f_c$ for the study region is shown in Figure 2. It can be seen that overcast clouds are associated with the least coverage of thin clouds. This is consistent with the in-situ study of Wood et al. (2018), which report a lower likelihood of the occurrence of thin clouds in the overcast Sc regime.

The variation in $f_{c,thin}$ with $f_c$ is non-monotonic, featuring three distinct linear phases (I, II, and III). In Phase I ($0.85 < f_c \le 1$), there is a steep increase in $f_{c,thin}$ along with a decrease in $f_c$, reaching a maximum at an $f_c$ of about
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Joint histogram of cloud cover ($f_c$) and thin cloud cover ($f_{c,thin}$) depicting the conditional probabilities of the occurrence of $f_{c,thin}$ for the range of $f_c$. A three-segmented linear regression following Bogartz (1968) is fitted to the $f_{c,thin}$ values corresponding to a maximum probability in each $f_c$ bin to highlight the non-monotonic relationship between $f_c$ and $f_{c,thin}$ m represents the slope of the linear fit and $\rho$ is the correlation coefficient. The filled circles at the top indicate the fraction of scenes with a cloud effective radius ($R_c$) greater than 14 µm. Subpanels at the top and right represent the distribution of the data with respect to $f_c$ and $f_{c,thin}$, respectively.</figcaption></figure>

0.85. Additionally, this increase is accompanied by a rise in the fraction of precipitating scenes (defined as those with $R_e$ > 14 µm). A similar negative correlation between $f_c$ and $f_{c,thin}$ at high cloud cover is also observed in trade wind Cu clouds, albeit to a lesser degree (Mieslinger et al., 2022). Below an $f_c$ of 0.85 (Phase II), we observe a correlation reversal, with decreasing $f_{c,thin}$ along with $f_c$. However, $f_{c,thin}$ decreases slower than $f_c$, implying that thin clouds become more prominent with decreasing $f_c$ in this phase. The fraction of precipitating scenes remains consistently high, at about 85 %. In Phase III ($f_c$ < 0.44), $f_{c,thin}$ declines steeply, approaching the identity line at low $f_c$. The fraction of precipitating scenes also decreases as one approaches low $f_c$. Upon analysing random cloudy scenes in Phase-III, we observe that scenes with low $f_c$ often consist of small scattered Cu clouds that are optically thin (e.g., the top right panel of Figure 3a). These small clouds are most likely in their dissipating stage, which may account for the decrease in the fraction of precipitating scenes. A similar pattern of correlation reversal is also observed in the geographical variation of the correlation coefficient between $f_{c,thin}$ and $f_c$ (see Fig. S1b in Supporting Information S2), where a negative correlation close to the coast gradually shifts to a positive correlation as one moves offshore.

It is important to note that this analysis focuses on the relationship between $f_{c,thin}$ and $f_c$ rather than the previously explored relationship between $f_{thin} (f_c = \frac{f_{c,thin}}{f_c})$ and $f_c$ (Leahy et al., 2012; McCoy et al., 2023). The latter fails to identify the three phases shown in Figure 2 and only reports a strong negative correlation. A negative correlation is expected, as $f_{thin}$, by definition ($f_{thin} = \frac{f_{c,thin}}{f_c}$), is inversely proportional to $f_c$.

<figure><img src="image_5.png" /><figcaption>**Figure 3.** (a) Example snapshots of MODIS true-color 1° by 1° scenes illustrating the variation in the fraction of thin cloud cover ($f_{thin}$) at different cloud cover ($f_c$). Panel (b) depicts the variation in the relative dispersion of liquid water path ($D_L$) with $f_{thin}$ at different $f_c$.</figcaption></figure>
## 3.2. $f_{\text{thin}}$ and Cloud Heterogeneity

In order to explore the relationship between the fraction of thin clouds at a given cloud cover and cloud water heterogeneity, we use the relative dispersion of $L(D_L)$. The relative dispersion was shown to be an effective measure of cloud heterogeneity (Feingold et al., 2022; Wood, 2006).

Figure 3b illustrates the variation in $D_L$ with $f_{\text{thin}}$ at different cloud cover. Quantitatively, Figure 3b shows a strong positive correlation between $f_{\text{thin}}$ and $D_L$, irrespective of the $f_c$. It can be seen that the extent of cloud heterogeneity at a given $f_c$ is constrained by the degree of variability in $f_{\text{thin}}$. For example, at $f_c = 1$, we observe an $f_{\text{thin}}$ range of 0–0.3, which limits $D_L$ variations to between 45 % and 95 %. As $f_c$ decreases, the $f_{\text{thin}}$ range shifts towards higher values, initially undergoing a significant shift (from 0 to 0.3 under overcast conditions to 0.2–0.6 at an $f_c$ of 0.85) followed by a more gradual increase. The degree of variability in $D_L$ also exhibits a similar increasing pattern as $f_{\text{thin}}$ with decreasing $f_c$. A plausible reason for the initial significant increase in cloud heterogeneity after cloud breakup (i.e., $f_c < 1$ in Figure 3b) could be the shift in cloud regime from Sc-dominated overcast scenes, which are characterized by a homogeneous cloud distribution (Wood, 2006), to a relatively heterogeneous Cu-dominated regime (Goren et al., 2023; Zheng et al., 2018). This is also evident in the MODIS true-color example snapshots depicted in Figure 3a, which shows that scenes with a lower $f_{\text{thin}}$ appear more clustered and uniform than those with a higher $f_{\text{thin}}$ at all $f_c$. The results are consistent with the observations of McCoy et al. (2023), which report a higher fraction of thin clouds in disorganised mesoscale convective cells compared to relatively more organized open and closed cells.

## 3.3. Cloud Properties in $F_c-f_{\text{thin}}$ Space

In this section, we investigate the variations in cloud properties in $f_c-f_{\text{thin}}$ space. The analysis is limited to cases with $f_c > 0.45$ because of three primary reasons: (a) there are potential retrieval issues related to cloud properties in scenes with low $f_c$ and high $f_{\text{thin}}$, arising from high sub-pixel heterogeneity, an increased occurrence of partially filled cloudy pixels, and an increased likelihood of pixels corresponding to failed retrievals (Cho et al., 2015; Painemal & Zuidema, 2011; Seethala & Horváth, 2010; Zhang & Platnick, 2011); (b) less than 15 % of the cloudy scenes correspond to $f_c \le 0.45$, and this percentage further decreases significantly when binned with $f_{\text{thin}}$ at a given $f_c$ (see Fig. S2 in Supporting Information S2), which may introduce sampling bias; (c) there is a high likelihood of occurrence of small scattered shallow Cu clouds at low cloud covers, which may be classified as thin clouds and are not the subject of this study (e.g., the top right panel of Figure 3a).

Figures 4a–4d) depicts the mean cloud properties in $f_c-f_{\text{thin}}$ space. We observe an increase in $R_e$ with $f_{\text{thin}}$ at a given cloud cover across the entire cloud cover range (Figure 4a). $R_e$ values are typically larger than 14 µm for broken clouds and can increase up to 22 µm at high $f_{\text{thin}}$, implying that most cloudy scenes associated with high $f_{\text{thin}}$ are precipitating. These results are consistent with the in-situ findings of Wood et al. (2018), which report a volume median radii of thin veil clouds ranging between 15 and 30 µm. $\tau$, on the other hand (Figure 4b), decreases with increasing $f_{\text{thin}}$ at a given cloud cover. Such an inverse correlation is expected, given the definition of optically thin clouds as those that have a $\tau \le 3$.

The relationship between $L$ and $f_{\text{thin}}$ at a fixed $f_c$ (Figure 4c) exhibits more complexity. For $f_c < 0.8$, $L$ first increases with $f_{\text{thin}}$, attains a maximum, and then decreases with a further increase in $f_{\text{thin}}$. This non-monotonic variation is contrary to what one would expect since thin clouds typically have low $L$ (approximately 25 g m⁻²) (Wood et al., 2018), and their addition at fixed $f_c$ should reduce the mean $L$. A possible explanation of the non-linear relationship between $L$ and $f_{\text{thin}}$ for $f_c < 0.8$ relies on the results of Goren et al. (2023), who demonstrated that the deeper the cloud cores, the thinner their anvils. These observations also aligns with our results of increase in cloud heterogeneity with $f_{\text{thin}}$ (as discussed previously in Section 3.2). Following these results, the contribution of deepening cloud cores overtakes the accompanying increase in $f_{\text{thin}}$ dominating the scene mean $L$ at low $f_{\text{thin}}$ values, explaining the initial increase in the scene mean $L$ with $f_{\text{thin}}$. At high $f_{\text{thin}}$ (ranging from 0.4 to 0.65 depending on $f_c$; see Fig. S3 in Supporting Information S2), the presence of thin anvils likely becomes predominant, leading to a decrease in the scene-mean $L$ with $f_{\text{thin}}$. At higher cloud covers ($f_c > 0.8$), $L$ is more spatially homogenous (Figure 3b), and the increase of $f_{\text{thin}}$ has a dominant effect leading to a decrease in $L$.

The CTT is a crucial parameter that governs the upward thermal emissions of the cloud. Figure 4d depicts the core CTT of the scene in $f_c-f_{\text{thin}}$ space. We observe a decrease in core CTT with increasing $f_{\text{thin}}$ at a given $f_c$ for most of the $f_c$ range. We will discuss more about the implications of this variation for LW CRE in Sect. 3.5.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Scene mean cloud and meteorological properties in $f_c$-$f_{\text{thin}}$ space depicting cloud effective radius $R_c$ (a), cloud optical thickness $\tau$ (b), liquid water path $\mathcal{L}$ (c), core cloud top temperature CTT (d), marine cold-air outbreak parameter $M$ (e), and sea surface temperature SST (g). Panels (f) and (h) represent the deviation of $M$ ($\Delta M$) and SST ($\Delta \text{SST}$) from their corresponding means in each $f_c$ bin, respectively.</figcaption></figure>

### 3.4. *M* and *SST* in $F_c$-$f_{\text{thin}}$ Space

Using long-term monthly mean cloud and meteorological data, O, Wood and Tseng (2018) identified a statistically significant positive correlation between BLH and $f_{\text{thin}}$ within most SCT regions, except for the SEP region (our study's focus). Here, we use the *M* index as a proxy for BLH, given its strong correlation with BLH (McCoy et al., 2023; Naud et al., 2018, 2020), to study its relationship with $f_{\text{thin}}$ values. Figures 4e and 4f illustrate the variations in *M* index in the $f_c$-$f_{\text{thin}}$ space. The parameter $\Delta M$ in Figure 4f is defined within each $f_{\text{thin}}$ bin at a given $f_c$ as the difference from the mean *M* at that $f_c$. We observe a statistically significant positive correlation between $f_{\text{thin}}$ and *M*, with a Pearson correlation coefficient ($\rho$) of 0.46 (refer to Fig. S4a in Supporting Information S2), which was not identified in O, Wood and Tseng (2018) for the SEP region. Additionally, this positive correlation remains consistent even at a constant cloud cover (Figure 4f), with $\rho$ values varying between 0.26 and 0.4, depending on the cloud cover (see Fig. S4c in Supporting Information S2).

In a Cu-dominated regime, the variability of Cu cloud thickness is primarily governed by the BLH since the Cu base height tends to exhibit less variations compared to the BLH (Daub & Lareau, 2022; Lareau et al., 2018; Lenhardt et al., 2024; Wood, 2006). Consequently, the positive correlation between $f_{\text{thin}}$ and *M* (and thus BLH) supports the earlier-discussed positive association between deeper Cu clouds and a high fraction of thin clouds at a given cloud cover (see Sect. 3.3).

The variation of SST in the $f_c$-$f_{\text{thin}}$ space is shown in Figures 4g and 4h. Overall, $f_{\text{thin}}$ shows a statistically significant positive correlation with SST with a $\rho$ of 0.28 (Fig. S4b in Supporting Information S2), which is less pronounced compared to *M*. Also, the variation in SST with $f_{\text{thin}}$ at a given $f_c$ is minimal (see Figure 4h), with the correlation coefficient being somewhat significant only at overcast $f_c$ ($\rho$ of 0.21; see Fig. S4c in Supporting Information S2).

From the collective results of Figures 4e and 4g, we can infer that overcast $f_c$ with the lowest $f_{\text{thin}}$ are associated with the lowest SSTs and *M* values. As the *M* parameter is correlated to BLH (Naud et al., 2020), this suggests that shallow boundary layers and colder SSTs, which are usually favorable for the development of overcast SC decks, are not favorable for the formation of thin clouds. Consequently, warmer SSTs and higher *M* values, conducive to the development of deeper Cu clouds, are associated with lower $f_c$ and higher $f_{\text{thin}}$. These results are consistent with the in-situ findings of Wood et al. (2018) and the hypothesis of O, Wood and Bretherton (2018).
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Scene mean CRE (W m⁻²) at the top of the atmosphere in *f*<sub>c</sub>–*f*<sub>thin</sub> space for SW (a) and LW (c) radiation. Panels (b) and (d) represent their percentage deviation from the mean in each *f*<sub>c</sub> bin, respectively. Panel (e) depicts the percentage of SW cooling compensated by LW warming.</figcaption></figure>

**3.5. Effects of f<sub>thin</sub> on CRE**

Optically thin clouds, due to their semi-transparent nature, may interact differently with SW and LW radiation compared to an equivalent amount of optically thick clouds. Results from Figures 2 and 3 highlight that the occurrence of thin clouds is strongly dependent on the cloud cover. Overcast Sc-dominated cloud covers have the least occurrence of thin clouds, which increases drastically as the cloud cover decreases below unity, where heterogeneous Cu clouds are dominant (see Sections 3.2 and 3.4). These variations in the prevalence of thin clouds across different cloud covers may lead to a corresponding cloud cover dependence of thin cloud impacts on CRE. This may have significant implications for a future warmer climate, in which studies indicate a faster Sc-Cu transition due to cleaner boundary layer (Goren et al., 2022), or changes in cloud morphologies to those associated with greater amounts of thin clouds throughout the cloud cover range due to environmental cloud controlling factors (Chemke & Polvani, 2021; McCoy et al., 2023). In the following section, we quantify this cloud cover dependence on *f*<sub>thin</sub>-CRE relationship by separating the analysis into changes in the SW and LW components of the CRE.

**3.5.1. Effects of f<sub>thin</sub> on SW CRE**

SW CRE is primarily governed by the changes in total cloud cover and cloud optical thickness (King, 1987; Platnick & Twomey, 1994; Twomey, 1977; Zelinka et al., 2012, 2016). Since the presence of thin clouds at a given cloud cover reduces the scene cloud optical thickness (see Figure 4b), they can significantly reduce the SW albedo and therefore the magnitude of SW CRE (Goren et al., 2023; McCoy et al., 2017, 2023). Figure 5a shows the CRE at the TOA for SW radiation (CRE<sub>SW,TOA</sub>) in *f*<sub>c</sub>–*f*<sub>thin</sub> space. It can be seen that the CRE<sub>SW,TOA</sub> reduces significantly with a decrease in *f*<sub>c</sub>, from an average of about –321.2 W m<sup>-2</sup> at overcast conditions to about –69.4 W m<sup>-2</sup> at *f*<sub>c</sub> = 0.5. The figure also demonstrates that the cooling extent of CRE<sub>SW,TOA</sub> at a given cloud cover shows a substantial reduction with an increase in *f*<sub>thin</sub>. ΔCRE<sub>SW,TOA</sub> in Figure 5b within each *f*<sub>thin</sub> bin at a given *f*<sub>c</sub> represents the percentage change in CRE<sub>SW,TOA</sub> from the mean CRE<sub>SW,TOA</sub> at that *f*<sub>c</sub>. For overcast scenes, the reduction in SW cooling due to an increase in *f*<sub>thin</sub> is approximately 46 % over the entire range of *f*<sub>thin</sub>. This can also be seen in the example case presented in Figure 3a (bottom row), where the increase in *f*<sub>thin</sub> dims the overcast scene. Furthermore, the relative extent of cloud dimming increases with decreasing *f*<sub>c</sub>, reaching about 65 % at an *f*<sub>c</sub>
of 0.55. This increase in the percentage of cloud dimming due to thin clouds with a decrease in $f_c$ may be attributed to the high amounts and extent of variation in $f_{thin}$, which are minimum for Sc-dominated overcast cloud regime and increases coherently with scene heterogeneity as $f_c$ decreases in the Cu-dominated regime. Such a wide range of possible variations in $CRE_{SW,TOA}$, even at fixed cloud cover, highlights the dominant role of thin clouds in controlling the SW radiative effect (Goren et al., 2023; McCoy et al., 2017, 2023).

### 3.5.2. Effects of $f_{thin}$ on LW CRE

For optically thick clouds, the $CRE_{LW,TOA}$ is primarily controlled by CTT, assuming that their emissivity is approximately 1 (Vaillant de Guélis, Chepfer, Noel, Guzman, Dubuisson, et al., 2017). However, for partially-transparent optically thin clouds, the $CRE_{LW,TOA}$ depends on the combination of thin-cloud radiative emission and the fraction of surface thermal emission that passes through the thin clouds (Arouf et al., 2022; Vaillant de Guélis, Chepfer, Noel, Guzman, Dubuisson, et al., 2017; Vaillant de Guélis, Chepfer, Noel, Guzman, Winker, et al., 2017). The thermal emission of thin clouds depends on the CTT and emissivity, and is relatively lower compared to that of optically thick clouds with the same CTT. The amount of surface thermal emission transmitted through the thin clouds depends on the cloud transmissivity, SST, and surface emissivity. Optically thick clouds are opaque to surface emission, and do not easily transmit the surface emission (Vaillant de Guélis, Chepfer, Noel, Guzman, Dubuisson, et al., 2017). More details regarding the outgoing LW flux of a cloudy scene composed of optically thin and thick clouds are provided in Text S1 in Supporting Information S1.

$CRE_{LW,TOA}$ variations in $f_c-f_{thin}$ space is depicted in Figure 5c. Similar to SW, the $CRE_{LW,TOA}$ reduces significantly with a decrease in cloud cover, from an average of about $14.1 \text{ W m}^{-2}$ under overcast conditions to about $7.2 \text{ W m}^{-2}$ at $f_c = 0.5$. Figure 5d illustrates the percentage change in the warming extent of $CRE_{LW,TOA}$ from the mean for various $f_c$ bins due to a change in $f_{thin}$. The figure shows a slight initial increase in $CRE_{LW,TOA}$ with $f_{thin}$ for overcast scenes. At such high $f_c$ that are associated with a low fraction of thin clouds, optically-thick opaque clouds are likely the primary contributors to $CRE_{LW,TOA}$. As shown in Figure 4d, the CTT at a given cloud cover decreases with increasing $f_{thin}$, implying that less LW radiation is emitted out into space, which may explain the slight increase in $CRE_{LW,TOA}$.

For $f_c < 0.9$, LW CRE decreases with $f_{thin}$ at a given $f_c$ (Figure 5d). Since these $f_c$ are associated with relatively higher $f_{thin}$ amounts (between 0.2 and 0.8; see Figure 3b), thin clouds likely exert significant control over $CRE_{LW,TOA}$ via LW transmission through the clouds. Increasing $f_{thin}$ at a given $f_c$ leads to higher transmissivity of the scene, allowing more thermal emission from the ocean surface to escape through the thin clouds into space. As a result, $CRE_{LW,TOA}$ decreases with $f_{thin}$ at a given $f_c$. The variability in LW warming at a given $f_c$ due to changes in $f_{thin}$ can range from approximately 12% for nearly overcast scenes to as high as 52% at an $f_c$ of about 0.6 (Figure 5d). Note that these percentage variations are estimated separately for each $f_c$ bin, and the absolute magnitude of $CRE_{LW,TOA}$ variations is relatively lower at low $f_c$ compared to high $f_c$. These results suggest a notable role of thin clouds in modulating the $CRE_{LW,TOA}$ of shallow marine clouds in the SCT region, particularly in the Cu-dominated regime.

### 3.5.3. $f_{thin}$ and the Balance Between LW and SW CRE

We also assess the balance between the instantaneous positive LW CRE (warming) and negative SW CRE (cooling). We find an average $CRE_{LW,TOA}$ of $9.8 \text{ W m}^{-2}$, which is approximately 7.1% of the average $CRE_{SW,TOA}$ (mean of -138.2 W m$^{-2}$). We also investigate how $f_{thin}$ modulates the balance between the LW warming and SW cooling. Figure 5e shows the percentage of SW cooling compensated by LW warming calculated as $100 \times \frac{CRE_{LW,TOA}}{|CRE_{SW,TOA}|}$ (hereafter termed LW compensation) in $f_c-f_{thin}$ space. At a given $f_c$, the LW compensation increases with $f_{thin}$ at all $f_c$. At high $f_{thin}$, this compensation can range from 8% for overcast scenes to 19% at a cloud cover of 0.5. For the considered range of cloud cover in Figure 5e, about 13% of the cloudy scenes correspond to a LW compensation of 15% or more. This percentage of scenes increases to 21% for a minimum LW compensation of 12%. While not as prominent as in tropical anvil clouds (Hartmann & Berry, 2017), the instantaneous LW compensation due to thin clouds can nevertheless be non-negligible, depending on their lifetime. Therefore, future research to quantify the longevity of thin veil clouds is required to accurately assess their overall contribution to the radiative effects of low marine clouds.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** (a) Predicting *A*<sub>c</sub> using multiple linear regression (MLR) with the predictors: *f*<sub>c</sub> (magenta); *f*<sub>c</sub>, *R*<sub>e</sub>, and *L* (blue); *f*<sub>c</sub>, *R*<sub>e</sub>, *L*, and *f*<sub>thin</sub> (red). The legend provides the R-squared and p-value of the regressions. (b) Variations in *A*<sub>c</sub> explained by different cloud properties at a given cloud cover (*f*<sub>c</sub>). The median and 90th percentile ranges of *A*<sub>c</sub> are indicated by the black dashed line and the grey polygon, respectively. At a given *f*<sub>c</sub>, the width of the yellow polygon represents the R-squared value for the linear regression between *f*<sub>thin</sub> and *A*<sub>c</sub> scaled to the 90th percentile range of *A*<sub>c</sub>, indicating the fraction of variance in *A*<sub>c</sub> explained by *f*<sub>thin</sub>. Similarly, the magenta polygon indicates the fraction of variance in *A*<sub>c</sub> explained by combined cloud effective radius (*R*<sub>e</sub>) and liquid water path (*L*).</figcaption></figure>

3.6. *f*<sub>thin</sub>, Cloud Morphology, and Cloud Albedo Predictability

To identify the relative influence of key cloud parameters on A_c, we have constructed multiple linear regression models to predict A_c using three sets of predictors: (a) only f_c; (b) f_c, R_c, and L; and (c) f_c, R_c, L, and f_thin. Note that the model assumes linearity between each of the cloud properties and A_c. The advantages of this assumption lie in identifying the key factors that control albedo variability, due to which it has been employed in multiple studies on cloud-radiation interaction (for instance, McCoy et al. (2023); Wall et al. (2023)). Figure 6a illustrates and compares the performance of these models in predicting A_c. We find that f_c alone explains 69 % of the variability in A_c, as indicated by the R-squared value. Including R_c and L in the regression improves the explained variance to 81 %. Adding f_thin to the regression model further improves the explained variance to approximately 89 %.

We further repeat a similar regression analysis for cloud cover intervals of 2.5 % ranging from 45 % to 100 %. Figure 6b illustrates the amount of variance in A_c explained by f_thin and combined R_c and L at those f_c intervals. The figure shows that R_c and L together explain about 64 % of the variance in A_c for overcast scenes, which reduces to about 20 % at a cloud cover of 0.45. In contrast, f_thin explains about 56 % of the A_c variability for overcast scenes, which comes down to about 39 % at low f_c. For overcast scenes that are dominated by homogeneous cloud morphology (see Figures 3b and 3c) and are usually associated with less variations in f_thin, R_c and L explain greater amounts of variance in A_c than f_thin. However, as the cloud cover decreases and cloud heterogeneity increases, the variance in A_c explained by f_thin becomes larger than that of R_c and L.

*f*<sub>thin</sub> is associated with the distribution of *L* within a scene (see Figure 3b). An increase in *f*<sub>thin</sub> at a given cloud cover indicates a relative reduction in the horizontal extent of cloud water corresponding to optically thick clouds, which are comparatively more efficient at reflecting the incoming solar radiation back into space. Therefore, *f*<sub>thin</sub> at a given cloud cover can be considered a geometrical constraint that governs the interaction between *L* and insolation. This impact is shown in Figure 7, which shows the relationship between A<sub>c</sub> and *L* for various *f*<sub>thin</sub> intervals for cloud cover ranging from 0.7 to 0.8. The figure demonstrates that *f*<sub>thin</sub> regulates the sensitivity of A<sub>c</sub> to variations in *L*, with higher sensitivity (indicated by a steeper linear slope) observed at low *f*<sub>thin</sub> amounts. An increase in *f*<sub>thin</sub> leads to a decrease in *L*–A<sub>c</sub> sensitivity. Since thin clouds restrict the extent of optically thick cloud water at a given cloud cover, high amounts of *f*<sub>thin</sub> within a scene limit the increase in *L* to a smaller horizontal extent, potentially explaining the low *L*–A<sub>c</sub> sensitivity.

The morphological constraint imposed by $f_{\text{thin}}$ is expected to be more pronounced at low cloud cover, given that the variability in $f_{\text{thin}}$ increases as cloud cover decreases below 1. This could account for the relatively higher proportion of variance in $A_c$ explained by $f_{\text{thin}}$ at lower cloud cover (Figure 6b), thereby contributing to an improved R-squared value when $f_{\text{thin}}$ is incorporated into the multiple linear regression model alongside $L$ and $R_e$ (Figure 6a). According to Bender et al. (2016), the primary source of variability in $A_c$ in subtropical marine clouds, at a given cloud cover, originates from variations in $L$. However, as shown in Figure 7, it is evident that
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Variation of cloud albedo ($A_c$) with liquid water path ($\mathcal{L}$) for different intervals of $f_{\text{thin}}$ for cloud cover ($f_c$) between 0.7 and 0.8. m is 1,000 times the slope of the linear fit. Each $f_{\text{thin}}$ interval consists of 17,763 cloudy scenes.</figcaption></figure>

even with constant cloud cover and $\mathcal{L}$, a change in $f_{\text{thin}}$ can modulate $A_c$ by 63%–69%, depending on $\mathcal{L}$, highlighting the significant influence of $f_{\text{thin}}$ (and thus cloud morphology) on $A_c$.

## 4. Discussion

Climate models indicate a weakening of subtropical subsidence and an increase in SST in response to global warming (Chemke & Polvani, 2021; Webb et al., 2013). These changes are likely to result in a deeper boundary layer, which, based on our findings and previously established correlations (O et al., 2018), may lead to a higher occurrence of optically thin clouds throughout the entire range of cloud cover in the SCT regions (McCoy et al., 2023). Model evaluation studies have highlighted the underestimation of cloud cover and the overestimation of cloud brightness in the tropics, often referred to as the "too few, too bright" bias (Konsta et al., 2022; Nam et al., 2012), which may occur due to an inaccurate representation of thin clouds in simulations (Konsta et al., 2022). Our findings, which elucidate the significant impact of thin clouds on the SW and LW components of CRE, further embolden the necessity for their accurate representation in climate models.

Recently, McCoy et al. (2023) reported a global positive SW cloud feedback between 0.04 and 0.07 W m⁻² K⁻¹ due to a shift in mesoscale low cloud morphology, driven by changes in optically thin clouds (McCoy et al., 2023). Our results further show that a change in thin cloud fraction also impacts the LW component of CRE. This LW CRE can subdue about 8%–19% of the SW CRE in the SCT region. It is essential to note that the LW contributions reported in our study are based on instantaneous CERES measurements and are likely to compensate for larger amounts of SW CRE when viewed at a daily resolution, considering the negligible SW CRE at night (Allan, 2011).

Also, we do not explicitly consider the LW contribution from the cloud-twilight zone present at the vicinity of cloud boundaries (Eytan et al., 2020), that can be considered as an extension to the veil clouds. The LW contribution from this zone is expected to further add to the total LW CRE. These factors highlight the importance of quantifying the LW component in cloud morphology feedback.

Although our study does not consider the influence of aerosols, they can influence the occurrence of thin clouds. Studies have shown that an increase in aerosol concentration can postpone cloud breakup in SCT regions by delaying the onset of precipitation (Christensen et al., 2020; Goren et al., 2019, 2022). Our results show a prevalence of thin clouds after cloud breakup. Therefore, in a cleaner climate, where the overcast clouds may precipitate and breakup sooner, the occurrence of thin clouds may increase substantially. Thin clouds can also modulate the aerosol-cloud interactions. Studies have found that, for a given scene-mean cloud albedo, homogeneous cloudy scenes display a larger albedo enhancement due to aerosols (Feingold et al., 2022; Goren et al., 2023). Given the intrinsic positive association between thin clouds and cloud heterogeneity (Figure 3b), high occurrence of thin clouds may dampen the susceptibility of the albedo to anthropogenic aerosols, and thus, the negative radiative forcing due to aerosol-cloud interactions.

## 5. Summary

In-situ and spaceborne studies (Leahy et al., 2012; Wood et al., 2018) have highlighted the significant presence of thin clouds in major SCT regions. In this study, we use simultaneous MODIS, CERES, and re-analysis data to study thin clouds over the Southeast Pacific Ocean. We find a non-monotonic relationship between $f_{c,thin}$ and $f_c$ (Figure 2). Sc-dominated overcast scenes are associated with the least $f_{c,thin}$. As $f_c$ decreases, $f_{c,thin}$ first increases steeply, reaches a maxima at $f_c = 0.85$, and thereafter decreases. An increase in $f_{thin}$ (=$\frac{f_{c,thin}}{f_c}$) is accompanied by an increase in the scene cloud-water heterogeneity (Figure 3b) and the occurrence of precipitating scenes (Figures 2 and 4a), indicating a transition to Cu-dominated regime.
The cloud regime dependence of $f_{thin}$ is also observed in terms of SST and M. Both M and SST show positive correlations with $f_{thin}$. The minimum of $f_{thin}$ is associated with minimum values of M and cold SST, conditions favorable for overcast clouds near the coastal region (McCoy et al., 2023). The maximum of $f_{thin}$ is associated with high values of M and warm SSTs. The positive correlation is more significant in the case of M ($\rho$ = 0.46) than SST ($\rho$ = 0.29). Furthermore, the correlation between M and $f_{thin}$ remains consistent even with constant cloud cover, throughout the cloud cover range, suggesting that the depth of the boundary layer exerts significant control over $f_{thin}$ during all stages of SCT.

$f_{thin}$ significantly influences SW CRE at all $f_c$. At a given $f_c$, increase in $f_{thin}$ leads to a substantial reduction in SW CRE by approximately 46 % for overcast scenes and about 65 % at 50 % $f_c$, across the entire $f_{thin}$ range. Although Bender et al. (2016) showed that albedo variations at a given $f_c$ are mainly controlled by $L$, our findings indicate that variations of approximately 63%–69 % in albedo can occur even with constant $f_c$ and $L$, owing to changes in $f_{thin}$. We further use multiple linear regressions to identify key parameters that influence cloud albedo. We show that integrating $f_{thin}$ in the regression that already incorporates $L$, $R_e$, and $f_c$ as predictors, improves the explained variance in cloud albedo from 81 % to 89 %. Furthermore, at a given non-overcast cloud cover, $f_{thin}$ explains greater variability in cloud albedo than the combined effect of $L$ and $R_e$. These findings underscore the crucial role of thin clouds in regulating the SW cooling of clouds in SCT region.

The LW CRE is found to be non-negligible for low clouds in the SCT region. We find that at a given cloud cover, the LW warming increases with $f_{thin}$ for overcast scenes, while for other $f_c$, it decreases with $f_{thin}$. This variation likely results from the balance between the thermal emission by opaque clouds near the cloud top (which depends on CTT) and the portion of transmitted surface LW emission through the thin clouds. The degree of variation in LW CRE due to $f_{thin}$ is noteworthy, with an approximately 12 % increase observed for near-overcast scenes and a substantial 52 % decrease at 50 % cloud cover.

The positive LW CRE is also shown to offset a portion of SW CRE. This LW compensation is shown to increase with $f_{thin}$. For overcast scenes, the relative increase in LW compensation with $f_{thin}$ is minimal, at about 8 % due to low occurrence of thin clouds. However, it increases to approximately 19 % at a cloud cover of 50 %. Approximately 13 % of the total cloudy scenes within the study region correspond to a LW compensation of 15 % or more. These results imply a non-negligible contribution of the LW component to the total CRE, specifically for scenarios with high $f_{thin}$.

This study aims to enhance our understanding of thin clouds and their influence on cloud radiative effects. Our results demonstrate that thin clouds are intrinsic characteristics of the Sc-Cu transition and play a significant role in modulating the cloud-radiation interaction. Consequently, this research emphasizes the need for a more comprehensive representation of cloud morphology in climate models (Konsta et al., 2022; Nam et al., 2012).

## Data Availability Statement

All data sets used in this work are open source. MODIS instantaneous cloud observations (Platnick et al., 2015) were acquired from the Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC) accessible at https://ladsweb.modaps.eosdis.nasa.gov/ (last accessed on 11 May 2024). CERES hourly radiation data (NASA/LARC/SD/ASDC, 2017) can be accessed at https://ceres.larc.nasa.gov/data/ (last accessed on 11 May 2024). ERA5 single level (Hersbach et al., 2023b) and pressure level (Hersbach et al., 2023a) data were obtained from Copernicus Climate Change Service (C3S) Climate Data Store accessible at https://cds.climate.copernicus.eu/ (last accessed on 11 May 2024).

## Acknowledgments

GC and TG acknowledge startup funds from Bar-Ilan University. TG also acknowledges funding from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG; GZ QU 311/27-1) for the project “CDNC4ACI”. We acknowledge the use of imagery from the Worldview Snapshots application (https://wvs.earthdata.nasa.gov), part of the Earth Observing System Data and Information System (EOSDIS).

## References

Alinaghi, P., Janssens, M., Choudhury, G., Goren, T., Siebesma, A. P., & Glassmeier, F. (2023). Shallow cumulus cloud fields are optically thicker when they are more clustered. https://doi.org/10.48550/arXiv.2309.08346

Allan, R. P. (2011). Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere. *Meteorological Applications*, 18(3), 324–333. https://doi.org/10.1002/met.285

Arouf, A., Chepfer, H., Vaillant de Guélis, T., Chiriaco, M., Shupe, M. D., Guzman, R., et al. (2022). The surface longwave cloud radiative effect derived from space lidar observations. *Atmospheric Measurement Techniques*, 15(12), 3893–3923. https://doi.org/10.5194/amt-15-3893-2022

Bender, F. A., Engström, A., & Karlsson, J. (2016). Factors controlling cloud albedo in marine subtropical stratocumulus regions in climate models and satellite observations. *Journal of Climate*, 29(10), 3559–3587. https://doi.org/10.1175/jcli-d-15-0095.1

Bogartz, R. S. (1968). A least squares method for fitting intercepting line segments to a set of data points. *Psychological Bulletin*, 70(6, Pt.1), 749–755. https://doi.org/10.1037/h0026713
Bretherton, C. S., & Wyant, M. C. (1997). Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers. *Journal of the Atmospheric Sciences*, 54(1), 148–167. https://doi.org/10.1175/1520-0469(1997)054<0148:mtltsa>2.0.co;2

Chemke, R., & Polvani, L. M. (2021). Elucidating the mechanisms responsible for Hadley cell weakening under 4 × CO₂ forcing. *Geophysical Research Letters*, 48(3), e2020GL090348. https://doi.org/10.1029/2020GL090348

Chen, Y., Haywood, J., Wang, Y., Malavelle, F., Jordan, G., Partridge, D., et al. (2022). Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover. *Nature Geoscience*, 15(8), 609–614. https://doi.org/10.1038/s41561-022-00991-6

Chen, Y. C., Christensen, M. W., Stephens, G. L., & Seinfeld, J. H. (2014). Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds. *Nature Geoscience*, 643–646. https://doi.org/10.1038/ngeo2214

Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., et al. (2015). Frequency and causes of failed modis cloud property retrievals for liquid phase clouds over global oceans. *Journal of Geophysical Research: Atmospheres*, 120(9), 4132–4154. https://doi.org/10.1002/2015JD023161

Christensen, M. W., Jones, W. K., & Stier, P. (2020). Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition. *Proceedings of the National Academy of Sciences of the United States of America*, 117(30), 17591–17598. https://doi.org/10.1073/pnas.1921231117

Daub, B. J., & Lareau, N. P. (2022). Observed covariations in boundary layer and cumulus cloud-layer processes. *Journal of Applied Meteorology and Climatology*, 61(10), 1497–1508. https://doi.org/10.1175/JAMC-D-21-0213.1

Dauhut, T., Couvreux, F., Bouniol, D., Beucher, F., Volkmer, L., Pörtge, V., et al. (2023). Flower trade-wind clouds are shallow mesoscale convective systems. *Quarterly Journal of the Royal Meteorological Society*, 149(750), 325–347. https://doi.org/10.1002/qj.4409

Denby, L. (2023). Charting the realms of mesoscale cloud organisation using unsupervised learning. https://doi.org/10.48550/arXiv.2309.08567

Diamond, M. S., Saide, P. E., Zuidema, P., Ackerman, A. S., Doherty, S. J., Fridlind, A. M., et al. (2022). Cloud adjustments from large-scale smoke-circulation interactions strongly modulate the southeastern atlantic stratocumulus-to-cumulus transition. *Atmospheric Chemistry and Physics*, 22(18), 12113–12151. https://doi.org/10.5194/ACP-22-12113-2022

Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D., Nguyen, C., et al. (2013). Geostationary enhanced temporal interpolation for cereals flux products. *Journal of Atmospheric and Oceanic Technology*, 30(6), 1072–1090. https://doi.org/10.1175/JTECH-D-12-00136.1

Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O., Keyes, D. F., & Mlynczak, P. E. (2016). Advances in geostationary-derived longwave fluxes for the cereals synoptic (syn1deg) product. *Journal of Atmospheric and Oceanic Technology*, 33(3), 503–521. https://doi.org/10.1175/JTECH-D-15-0147.1

Engström, A., Bender, F. A.-M., Charlson, R. J., & Wood, R. (2015). The nonlinear relationship between albedo and cloud fraction on near-global, monthly mean scale in observations and in the cmip5 model ensemble. *Geophysical Research Letters*, 42(21), 9571–9578. https://doi.org/10.1002/2015GL066275

Eytan, E., Koren, I., Altaratz, O., Kostinski, A. B., & Ronen, A. (2020). Longwave radiative effect of the cloud twilight zone. *Nature Geoscience*, 13(10), 669–673. https://doi.org/10.1038/s41561-020-0636-8

Fan, C., Wang, M., Rosenfeld, D., Zhu, Y., Liu, J., & Chen, B. (2020). Strong precipitation suppression by aerosols in marine low clouds. *Geophysical Research Letters*, 47(7), e2019GL086207. https://doi.org/10.1029/2019GL086207

Feingold, G., Goren, T., & Yamaguchi, T. (2022). Quantifying albedo susceptibility biases in shallow clouds. *Atmospheric Chemistry and Physics*, 22(5), 3303–3319. https://doi.org/10.5194/ACP-22-3303-2022

Feingold, G., Koren, I., Wang, H., Xue, H., & Brewer, W. A. (2010). Precipitation-generated oscillations in open cellular cloud fields. *Nature*, 466(7308), 849–852. https://doi.org/10.1038/nature09314

Fletcher, J., Mason, S., & Jakob, C. (2016). The climatology, meteorology, and boundary layer structure of marine cold air outbreaks in both hemispheres. *Journal of Climate*, 29(6), 1999–2014. https://doi.org/10.1175/JCLI-D-15-0268.1

Goren, T., Feingold, G., Gryspeerdt, E., Kazil, J., Kretzschmar, J., Jia, H., & Quaas, J. (2022). Projecting stratocumulus transitions on the albedo—Cloud fraction relationship reveals linearity of albedo to droplet concentrations. *Geophysical Research Letters*, 49(20), e2022GL101169. https://doi.org/10.1029/2022GL101169

Goren, T., Kazil, J., Hoffmann, F., Yamaguchi, T., & Feingold, G. (2019). Anthropogenic air pollution delays marine stratocumulus breakup to open cells. *Geophysical Research Letters*, 46(23), 14135–14144. https://doi.org/10.1029/2019GL085412

Goren, T., & Rosenfeld, D. (2014). Decomposing aerosol cloud radiative effects into cloud cover, liquid water path and Twomey components in marine stratocumulus. *Atmospheric Research*, 138, 378–393. https://doi.org/10.1016/j.atmosres.2013.12.008

Goren, T., Sourdeval, O., Kretzschmar, J., & Quaas, J. (2023). Spatial aggregation of satellite observations leads to an overestimation of the radiative forcing due to aerosol-cloud interactions. *Geophysical Research Letters*, 50(18), e2023GL105282. https://doi.org/10.1029/2023GL105282

Grosvenor, D. P., & Wood, R. (2014). The effect of solar zenith angle on modis cloud optical and microphysical retrievals within marine liquid water clouds. *Atmospheric Chemistry and Physics*, 14, 7291–7321. https://doi.org/10.5194/acp-14-7291-2014

Guzman, R., Chepfer, H., Noel, V., Vaillant de Guélis, T., Kay, J. E., Raberanto, P., et al. (2017). Direct atmosphere opacity observations from calipso provide new constraints on cloud-radiation interactions. *Journal of Geophysical Research: Atmospheres*, 122(2), 1066–1085. https://doi.org/10.1002/2016JD025946

Hahn, C. J., & Warren, S. G. (2007). A gridded climatology of clouds over land (1971–96) and ocean (1954–97) from surface observations worldwide. Oak Ridge National Laboratory, Carbon Dioxide Information Analysis Center. https://doi.org/10.3334/CDIAC/cli.ndp026e

Hartmann, D. L., & Berry, S. E. (2017). The balanced radiative effect of tropical anvil clouds. *Journal of Geophysical Research: Atmospheres*, 122(9), 5003–5020. https://doi.org/10.1002/2017JD026460

Hartmann, D. L., Ockert-Bell, M. E., & Michelsen, M. L. (1992). The effect of cloud type on earth's energy balance: Global analysis. *Journal of Climate*, 5(11), 1281–1304. https://doi.org/10.1175/1520-0442(1992)005<1281:teocto>2.0.co;2

Hartmann, D. L., & Short, D. A. (1980). On the use of earth radiation budget statistics for studies of clouds and climate. *Journal of the Atmospheric Sciences*, 37(6), 1233–1250. https://doi.org/10.1175/1520-0469(1980)037<1233:otuober>2.0.co;2

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., et al. (2023a). ERA5 hourly data on pressure levels from 1940 to present. [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.2438/cds.bd0915c6

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., et al. (2023b). ERA5 hourly data on single levels from 1940 to present. [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.2438/cds.adbb2d47

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. *Quarterly Journal of the Royal Meteorological Society*, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

King, M. D. (1987). Determination of the scaled optical thickness of clouds from reflected solar radiation measurements. *Journal of the Atmospheric Sciences*, 44(13), 1734–1751. https://doi.org/10.1175/1520-0469(1987)044<1734:dotsot>2.0.co;2
King, M. D., Tsay, S.-C., Platnick, S. E., Wang, M., & Liou, K.-N. (1997). Cloud retrieval algorithms for modis: Optical thickness, effective particle radius, and thermodynamic phase. *MODIS Algorithm Theoretical Basis Document*.

Klein, S. A., Hall, A., Norris, J. R., & Pincus, R. (2017). Low-cloud feedbacks from cloud-controlling factors: A review. *Surveys in Geophysics*, 38(6), 1307–1329. https://doi.org/10.1007/s10712-017-9433-3

Kolstad, E. W., Bracegirdle, T. J., & Seierstad, I. A. (2009). Marine cold-air outbreaks in the north Atlantic: Temporal distribution and associations with large-scale atmospheric circulation. *Climate Dynamics*, 33(2–3), 187–197. https://doi.org/10.1007/s00382-008-0431-5

Konsta, D., Dufresne, J. L., Chepfer, H., Vial, J., Koshiro, T., Kawai, H., et al. (2022). Low-level marine tropical clouds in six cmip6 models are too few, too bright but also too compact and too homogeneous. *Geophysical Research Letters*, 49(11), e2021GL097593. https://doi.org/10.1029/2021GL097593

Koren, I., Remer, L. A., Kaufman, Y. J., Rudich, Y., & Martins, J. V. (2007). On the twilight zone between clouds and aerosols. *Geophysical Research Letters*, 34(8). https://doi.org/10.1029/2007GL029253

Lareau, N. P., Zhang, Y., & Klein, S. A. (2018). Observed boundary layer controls on shallow cumulus at the arm southern great plains site. *Journal of the Atmospheric Sciences*, 75(7), 2235–2255. https://doi.org/10.1175/JAS-D-17-0244.1

Leahy, L. V., Wood, R., Charlson, R. J., Hostetler, C. A., Rogers, R. R., Vaughan, M. A., & Winker, D. M. (2012). On the nature and extent of optically thin marine low clouds. *Journal of Geophysical Research*, 117(D22). https://doi.org/10.1029/2012JD017929

L'Ecuyer, T. S., Hang, Y., Matus, A. V., & Wang, Z. (2019). Reassessing the effect of cloud type on earth's energy balance in the age of active spaceborne observations. part i: Top of atmosphere and surface. *Journal of Climate*, 32(19), 6197–6217. https://doi.org/10.1175/JCLI-D-18-0753.1

Lenhardt, J., Quaas, J., & Sejdinovic, D. (2024). Marine cloud base height retrieval from modis cloud properties using machine learning. *EGUsphere*, 2024, 1–26. https://doi.org/10.5194/egusphere-2024-327

McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., & Bender, F. A.-M. (2023). The role of mesoscale cloud morphology in the shortwave cloud feedback. *Geophysical Research Letters*, 50(2). https://doi.org/10.1029/2022GL101042

McCoy, I. L., Wood, R., & Fletcher, J. K. (2017). Identifying meteorological controls on open and closed mesoscale cellular convection associated with marine cold air outbreaks. *Journal of Geophysical Research: Atmospheres*, 122, 11678–11702. https://doi.org/10.1002/2017JD027031

Mieslinger, T., Stevens, B., Kölling, T., Brath, M., Wirth, M., & Buehler, S. A. (2022). Optically thin clouds in the trades. *Atmospheric Chemistry and Physics*, 22(10), 6879–6898. https://doi.org/10.5194/acp-22-6879-2022

Mohrmann, J., Wood, R., Yuan, T., Song, H., Eastman, R., & Oreopoulos, L. (2021). Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications. *Atmospheric Chemistry and Physics*, 21(12), 9629–9642. https://doi.org/10.5194/acp-21-9629-2021

Muhlbauer, A., McCoy, I. L., & Wood, R. (2014). Climatology of stratocumulus cloud morphologies: Microphysical properties and radiative effects. *Atmospheric Chemistry and Physics*, 14(13), 6695–6716. https://doi.org/10.5194/acp-14-6695-2014

Nam, C., Bony, S., Dufresne, J.-L., & Chepfer, H. (2012). The 'too few, too bright' tropical low-cloud problem in cmip5 models. *Geophysical Research Letters*, 39(21). https://doi.org/10.1029/2012GL053421

NASA/LARC/SD/ASDC. (2017). CERES and GEO-enhanced TOA, within-atmosphere and surface fluxes, clouds and aerosols 1-hourly terra-aqua Edition4A. [Dataset]. NASA Langley Atmospheric Science Data Center DAAC. https://doi.org/10.5067/TERRA+AQUA/CERES/SYN1DEG-1HOUR_L3.004A

Naud, C. M., Booth, J. F., Lamer, K., Marchand, R., Protat, A., & McFarquhar, G. M. (2020). On the relationship between the marine cold air outbreak parameter and low-level cloud heights in the midlatitudes. *Journal of Geophysical Research: Atmospheres*, 125(13), e2020JD032465. https://doi.org/10.1029/2020JD032465

Naud, C. M., Booth, J. F., & Lamraoui, F. (2018). Post cold frontal clouds at the arm eastern north atlantic site: An examination of the relationship between large-scale environment and low-level cloud properties. *Journal of Geophysical Research: Atmospheres*, 123(21). https://doi.org/10.1029/2018JD029015

O. K.-T., Wood, R., & Bretherton, C. S. (2018). Ultraclean layers and optically thin clouds in the stratocumulus-to-cumulus transition. part ii: Depletion of cloud droplets and cloud condensation nuclei through collision-coalescence. *Journal of the Atmospheric Sciences*, 75(5), 1653–1673. https://doi.org/10.1175/JAS-D-17-0218.1

O. K.-T., Wood, R., & Tseng, H.-H. (2018). Deeper, precipitating pbls associated with optically thin veil clouds in the sc-cu transition. *Geophysical Research Letters*, 45(10), 5177–5184. https://doi.org/10.1029/2018GL077084

Painemal, D., & Zuidema, P. (2011). Assessment of modis cloud effective radius and optical thickness retrievals over the southeast pacific with vocals-rex in situ measurements. *Journal of Geophysical Research*, 116(D24). https://doi.org/10.1029/2011JD016155

Platnick, S., Ackerman, S. A., King, M. D., Meyer, K., Menzel, W. P., Holz, R. E., et al. (2015). MODIS atmosphere L2 cloud product (06_L2). [Dataset]. NASA MODIS Adaptive Processing System, Goddard Space Flight Center. https://doi.org/10.5067/MODIS/MYD06_L2.061

Platnick, S., & Twomey, S. (1994). Determining the susceptibility of cloud albedo to changes in droplet concentration with the advanced very high resolution radiometer. *Journal of Applied Meteorology and Climatology*, 33(3), 334–347. https://doi.org/10.1175/1520-0450(1994)033<0334: dtsoca>2.0.co;2

Qu, X., Hall, A., Klein, S. A., & DeAngelis, A. M. (2015). Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. *Geophysical Research Letters*, 42(18), 7767–7775. https://doi.org/10.1002/2015GL065627

Rosenfeld, D., Kaufman, Y. J., & Koren, I. (2006). Switching cloud cover and dynamical regimes from open to closed benard cells in response to the suppression of precipitation by aerosols. *Atmospheric Chemistry and Physics*, 6(9), 2503–2511. https://doi.org/10.5194/ACP-6-2503-2006

Rosenfeld, D., Wang, H., & Rasch, P. J. (2012). The roles of cloud drop effective radius and lwp in determining rain properties in marine stratocumulus. *Geophysical Research Letters*, 39(13). https://doi.org/10.1029/2012GL052028

Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., & Yu, S. (2019). Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds. *Science*, 363(6427), eav0566. https://doi.org/10.1126/science.aav0566

Sandu, I., & Stevens, B. (2011). On the factors modulating the stratocumulus to cumulus transitions. *Journal of the Atmospheric Sciences*, 68(9), 1865–1881. https://doi.org/10.1175/2011JAS3614.1

Seethala, C., & Horváth, A. (2010). Global assessment of amsr-e and modis cloud liquid water path retrievals in warm oceanic clouds. *Journal of Geophysical Research*, 115(D13). https://doi.org/10.1029/2009JD012662

Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., et al. (2020). An assessment of earth's climate sensitivity using multiple lines of evidence. *Reviews of Geophysics*, 58(4), e2019RG000678. https://doi.org/10.1029/2019RG000678

Stevens, B., Bony, S., Brogniez, H., Hentgen, L., Hohenegger, C., Kiemle, C., et al. (2020). Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds. *Quarterly Journal of the Royal Meteorological Society*, 146(726), 141–152. https://doi.org/10.1002/qj.3662

Twomey, S. (1977). The influence of pollution on the shortwave albedo of clouds. *Journal of the Atmospheric Sciences*, 34(7), 1149–1152. https://doi.org/10.1175/1520-0469(1977)034<1149:tiopot>2.0.co;2
Vaillant de Guélis, T., Chepfer, H., Noel, V., Guzman, R., Dubuisson, P., Winker, D. M., & Kato, S. (2017). The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated. *Atmospheric Measurement Techniques*, 10(12), 4659–4685.
https://doi.org/10.5194/amt-10-4659-2017

Vaillant de Guélis, T., Chepfer, H., Noel, V., Guzman, R., Winker, D., & Plougonven, R. (2017). Using space lidar observations to decompose longwave cloud radiative effect variations over the last decade. *Geophysical Research Letters*, 44, 11994–12003. https://doi.org/10.1002/2017GL074628

Wall, C. J., Storelvmo, T., & Possner, A. (2023). Global observations of aerosol indirect effects from marine liquid clouds. *Atmospheric Chemistry and Physics*, 23(20), 13125–13141. https://doi.org/10.5194/acp-23-13125-2023

Wang, H., & Feingold, G. (2009). Modeling mesoscale cellular structures and drizzle in marine stratocumulus. part i: Impact of drizzle on the formation and evolution of open cells. *Journal of the Atmospheric Sciences*, 66(11), 3237–3256. https://doi.org/10.1175/2009JAS3022.1

Warren, S. G., Hahn, C. J., London, J., Chervin, R. M., & Jenne, R. L. (1988). Global distribution of total cloud cover and cloud type amounts over the ocean (Vol. 212pp). NCAR Technical Note. https://doi.org/10.2172/5415329

Webb, M. J., Lambert, F. H., & Gregory, J. M. (2013). Origins of differences in climate sensitivity, forcing and feedback in climate models. *Climate Dynamics*, 40(3–4), 677–707. https://doi.org/10.1007/s00382-012-1336-x

Wood, R. (2006). Rate of loss of cloud droplets by coalescence in warm clouds. *Journal of Geophysical Research*, 111(D21), D21205. https://doi.org/10.1029/2006JD007553

Wood, R. (2012). Stratocumulus clouds. *Monthly Weather Review*, 140(8), 2373–2423. https://doi.org/10.1175/MWR-D-11-00121.1

Wood, R., & Bretherton, C. S. (2004). Boundary layer depth, entrainment, and decoupling in the cloud-capped subtropical and tropical marine boundary layer. *Journal of Climate*, 17(18), 3576–3588. https://doi.org/10.1175/1520-0442(2004)017<3576:bldead>2.0.co;2

Wood, R., Kuan-Ting, O., Bretherton, C. S., Mohrmann, J., Albrecht, B. A., Zuidema, P., et al. (2018). Ultraclean layers and optically thin clouds in the stratocumulus-to-cumulus transition. Part i: Observations. *Journal of the Atmospheric Sciences*, 75, 1652. https://doi.org/10.1175/JAS-D-17-0213.1

Yamaguchi, T., Feingold, G., & Kazil, J. (2017). Stratocumulus to cumulus transition by drizzle. *Journal of Advances in Modeling Earth Systems*, 9(6), 2333–2349. https://doi.org/10.1002/2017MS001104

Zelinka, M. D., Klein, S. A., & Hartmann, D. L. (2012). Computing and partitioning cloud feedbacks using cloud property histograms. part i: Cloud radiative kernels. *Journal of Climate*, 25(11), 3715–3735. https://doi.org/10.1175/JCLI-D-11-00248.1

Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in cmip6 models. *Geophysical Research Letters*, 47(1), e2019GL085782. https://doi.org/10.1029/2019GL085782

Zelinka, M. D., Zhou, C., & Klein, S. A. (2016). Insights from a refined decomposition of cloud feedbacks. *Geophysical Research Letters*, 43(17), 9259–9269. https://doi.org/10.1002/2016GL069917

Zhang, Z., & Platnick, S. (2011). An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three modis spectral bands. *Journal of Geophysical Research*, 116(D20), D20215. https://doi.org/10.1029/2011JD016216

Zheng, Y., Rosenfeld, D., & Li, Z. (2018). Estimating the decoupling degree of subtropical marine stratocumulus decks from satellite. *Geophysical Research Letters*, 45(22), 12560–12568. https://doi.org/10.1029/2018GL078382

Zhou, X., Bretherton, C. S., Eastman, R., McCoy, I. L., & Wood, R. (2021). Wavelet analysis of properties of marine boundary layer mesoscale cells observed from amsr-e. *Journal of Geophysical Research: Atmospheres*, 126(14), e2021JD034666. https://doi.org/10.1029/2021JD034666