# Effects of the estimated inversion strength and aerosols on monthly variations of subtropical marine stratocumulus to the west of Africa

Waren Dale Soriano, Tra Dinh, Gilles Bellon

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Waren Dale Soriano, Tra Dinh, Gilles Bellon. Effects of the estimated inversion strength and aerosols on monthly variations of subtropical marine stratocumulus to the west of Africa. Atmospheric Research, 2025, 321, pp.108078. 10.1016/j.atmosres.2025.108078. hal-05273046

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# Effects of the estimated inversion strength and aerosols on monthly variations of subtropical marine stratocumulus to the west of Africa

Waren Dale Soriano<sup>a</sup>, Tra Dinh<sup>a</sup>, Gilles Bellon<sup>b,*</sup>

## ARTICLE INFO

Keywords:

Marine stratocumulus

Low-cloud cover

Estimated inversion strength

Dust aerosols

Biomass-burning aerosols

West Africa

## ABSTRACT

Satellite and reanalysis data over the 2003–2022 period are used to study how the monthly variations of marine stratocumulus (MSc) to the west of Canary Islands and Namibia are influenced by thermodynamic variables and aerosols. Although the estimated inversion strength (EIS) has been thought to be the main predictor of low-cloud cover (LCC), the seasonal cycles of EIS and LCC differ significantly in the Canary MSc region. In linear regression models, adding dust aerosol (DA) optical depth as a predictor improves the prediction of LCC across all seasons and almost everywhere in the region compared to using EIS alone. In boreal summer, large amount of DAs are transported from the Sahara to the Canary MSc region. Analysis of the regression models shows that DAs contribute more than EIS to the Canary summer LCC maximum, reducing the error by up to 11 %. Biomass-burning aerosols (BBAs) also improve the prediction of LCC in the Canary MSc region, but less so than DAs, suggesting that BBAs are less effective as cloud condensation nuclei. Other known thermodynamic predictors of LCC (sea surface temperature, horizontal surface temperature advection, near-surface wind speed, and free-tropospheric relative humidity and vertical velocity) also improve the prediction, but much less effectively than the aerosols. In the Namibia MSc region, there are virtually no DAs but BBAs are abundant. There, EIS alone captures well the seasonal cycle of LCC. In this region, the effect of BBAs on LCC is hidden behind that of EIS, potentially because BBA optical depth and EIS coincidentally have similar seasonal cycles.

## 1. Introduction

Marine stratocumulus (MSc) clouds are low-level clouds that have the largest areal coverage compared to other cloud types (Klein and Hartmann, 1993). Their extent is a result of a stable atmospheric condition where moisture and low clouds are trapped by a sharp temperature inversion that acts as a boundary between the marine boundary layer and the free troposphere. Their optically thick structure and extent make them a good reflector of incoming shortwave radiation and their altitude minimizes their impact on outgoing longwave radiation (Liou, 1992). Overall, they provide the Earth a net cooling (Wood, 2012).

How MSc low-cloud cover (LCC) will change in the future is uncertain. General circulation models (GCMs) do not provide robust projections, given that the micro-physical processes of clouds are on scales too small to be resolved by GCMs (Turner et al., 2012). For this reason, sub-grid cloud parameterization is still necessary, whereby resolved meteorological variables, such as the estimated inversion strength (EIS, Wood and Bretherton, 2006), are used to estimate LCC (Huang et al.,

2014). The EIS is a metric of low-level stability which estimates the temperature inversion that caps the boundary layer. It is calculated as

$$EIS = LTS - I_{m}^{850}(z_{700} - z_{LCL}) \quad (1)$$

(Wood and Bretherton, 2006), where LTS is the lower-tropospheric stability defined as the difference in potential temperature between the 700 hPa isobaric level and the surface at 1000 hPa, $I_{m}^{850}$ is the moist-adiabatic lapse rate at 850 hPa calculated using the average temperature between 700 hPa and the surface, $z_{700}$ is the height of the 700 hPa isobaric level, and $z_{LCL}$ is the height of the lifting condensation level, computed using the method by Romps (2017).

MSc clouds strive in a stable atmospheric condition characterized by a large EIS value, which keeps the marine boundary layer shallow and moist. On the other hand, with a weak inversion strength characterized by a small EIS value, entrainment of dry air from the free troposphere into the marine boundary layer reduces LCC (Brioude et al., 2009). This makes the EIS a very good predictor of LCC (Wood and Bretherton, 2006). Fig. 1 demonstrates that, to first order, EIS is indicative of the

<footnote><sup>a</sup> Department of Physics, The University of Auckland, Bldg 303, 38 Princes St., Auckland Central, Auckland 1010, New Zealand</footnote>

<footnote><sup>b</sup> Centre National de Recherches Météorologiques, CNRS, Météo France, Université de Toulouse, 42 Avenue Gaspard Coriolis, Toulouse 31057, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: wsor330@aucklanduni.ac.nz (W.D. Soriano), tdinh@princeton.edu (T. Dinh), gilles.bellon@meteo.fr (G. Bellon).</footnote>
observed LCC in the MSc regions to the west of Africa. Here, EIS is
calculated using data from the fifth generation reanalysis (ERA5) of the
European Centre for Medium-Range Weather Forecasts (ECMWF) and
LCC is from the Moderate Resolution Imaging Spectroradiometer
(MODIS) dataset.

However, given the simplicity of EIS as defined in Eq. (1), it is ex-
pected that there are limitations to EIS as a predictor of LCC. Upon a
closer inspection of Fig. 1, we see that there are differences between the
geographical patterns of EIS and LCC. In particular, the largest values of
EIS occur in the coastal regions, whereas LCC is maximum in the open
ocean. For example, during boreal winter in the Canary MSc region, the
LCC maximum is situated far off the coast where surface moisture supply
is readily available (Wood, 2012). On the other hand, the EIS maximum
is near the coast where the marine boundary layer is intrinsically shal-
lower (Xiao et al., 2011).

In addition, temporal differences between EIS and LCC exist. It has
been pointed out that a significant fraction of LCC variability cannot be
explained by EIS (Wang et al., 2023). Fig. 1 also illustrates this point. In

the Canary MSc region, EIS is similar during boreal spring and summer,
while LCC is clearly much higher in magnitude and spatially more
extensive during boreal summer. In fact, the seasonal cycle of LCC is
only weakly correlated to EIS in the Canary MSc region (Fig. 2a). There,
the summer LCC maximum is not at all matched by an EIS maximum. In
contrast, in the Namibia MSc region, the seasonal cycles of EIS and LCC
agree very well with each other (Fig. 2b).

Recent studies have been searching for improved predictors of LCC. These include the estimated cloud-top entrainment index (ECTEI; Kawai et al., 2017), which is a modification of the EIS with additional information on cloud-top entrainment processes due to the difference in the humidity below and above the inversion. Additionally, the profile-based EIS (EIS<sub>p</sub>; Wang et al., 2023) tries to improve EIS biases associated with the assumption that the boundary layer is well mixed. The EIS<sub>p</sub> is calculated as the inversion of the layer over the maximum vertical stability rather than over all layers below 700 hPa. Finally, other studies (Norris and Leovy, 1994; Brueck et al., 2015; Andersen et al., 2017; Scott et al., 2020) resorted to the use of multiple predictors which include sea

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Maps of the seasonal averages over the 2003–2022 period of (a)–(d) LCC from MODIS and (e)–(h) EIS from ERA5. EIS values less than –3 K have been omitted. LCC is shown additionally by the black contours in all panels. Arrows depict wind vectors at 700 hPa from ERA5. The boxes with white, dashed borders show our regions of study off the coasts of Canary and Namibia. The four rows correspond to the four seasons, which are December–January–February (DJF), March–April–May (MAM), June–July–August (JJA), and September–October–November (SON).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Climatological seasonal cycles of LCC, EIS, dust aerosol optical depth (DAOD), biomass burning aerosol optical depth (BBAOD) in the (a) Canary and (b) Namibia MSc regions.</figcaption></figure>

surface temperature (SST), horizontal surface temperature advection ($T_{adv}$), wind speed at 1000 hPa (WS$_{1000}$), free-tropospheric relative humidity and vertical velocity ($RH_{700}$ and $\omega_{700}$) in addition to EIS. These five secondary thermodynamic predictors (excluding EIS) will be referred to hereafter as the 5STPs. How each of the 5STPs affects LCC has been explained in detail by Scott et al. (2020).

Aerosols have also been known to affect LCC through aerosol-radiation interactions and aerosol-cloud interactions (Rosenfeld et al., 2014; Li et al., 2017; Rosenfeld et al., 2019; Redemann et al., 2021).

Aerosol-radiation interactions refer to the scattering and absorption of solar radiation by aerosols. Light-colored aerosols, such as mineral dust aerosols, predominantly scatter solar radiation, effectively reflecting it back into space and cooling the surface (Haywood et al., 2008; Takemura et al., 2002). This in turn increases the stability of the boundary layer (which would increase LCC) while decreases moisture in the boundary layer (which would decrease LCC). The increased atmospheric stability results from a greater difference in potential temperature between the 700 hPa isobaric level and the surface at 1000 hPa. The reduced moisture supply is due to decreased evaporation caused by surface cooling. Aerosols' absorption of solar radiation has varied effects on LCC depending on the vertical distribution of the aerosols (Johnson et al., 2004; Brioude et al., 2009; Wilcox, 2010; Costantino and Bréon, 2013; Zuidema et al., 2016; Mallet et al., 2020; Cochrane et al., 2022). When the aerosols are within the boundary layer, their absorption of solar radiation warms the air, reducing the relative humidity, liquid water path, and cloud amount there. On the other hand, when the aerosols are above the boundary layer, their absorption of solar radiation increases the 700 hPa air temperature and thus increases EIS and LCC.

Aerosol-cloud interactions involve aerosols acting as cloud condensation nuclei (CCN, Hudson, 1993). An abundance of CCN can reduce cloud droplet size and increase cloud droplet concentration, which increases cloud albedo (Twomey, 1991). Additionally, the reduced cloud droplet size can both increase and decrease cloud lifetime. The increase in cloud lifetime is attributed to rainfall suppression (Albrecht, 1989). On the other hand, the decrease in cloud lifetime is attributed to the higher likelihood of smaller cloud droplets evaporating (Small et al., 2009).

By and large, the aerosol-radiation effects on LCC through modification of the boundary layer's inversion are captured within the EIS.

However, the aerosol-cloud effects on LCC due to aerosols acting as CCN are not.

Several field campaigns have been conducted to study and characterize aerosols in west Africa, such as the “Dust and Biomass-Burning Experiment” (DABEX, Haywood et al., 2008) and the “Aerosols, Radiation and Clouds in Southern Africa” campaign (AEROCLO-SA, Formenti et al., 2019). Indeed, the Canary and Namibia MSc regions provide the best opportunity to investigate the impact of aerosols on LCC since they are next to the African continent which emits the greatest amount of aerosols. The Saharan desert is the largest source of mineral dust aerosols (DAs) on the globe (Knippertz and Todd, 2012; Grogan et al., 2022); it is located next to the Canary MSc region and its aerosols affect the Canary MSc region but not the Namibia MSc region (Fig. 3a–d). The extensive biomass burning in equatorial Africa (Van der Werf et al., 2010; Redemann et al., 2021) is a large source of biomass burning aerosols (BBAs), which contribute to significant BBA optical depth (OD) in both the Canary and Namibia MSc regions (Fig. 3e–h). These BBAs are specifically described as smoke or black carbon (Dubovik et al., 2002; Chauvigné et al., 2021).

DAs and BBAs have different micro-physical and optical properties. Freshly emitted DAs are good CCN because they contain soluble materials with hygroscopic properties (Koehler, 2008; Twohy et al., 2009; Karydis et al., 2011; Gaston, 2020). Additionally, DAs are better at reflecting incoming shortwave radiation (having a higher single-scattering albedo) due to the light colour of their surfaces (Haywood et al., 2008; Noh, 2014; Takemura et al., 2002). As a result, DAs have a net radiative cooling effect (Amiri-Farahani et al., 2017). In contrast, Dusek et al. (2006); Henning et al. (2012); Maskey et al. (2017) found that, for pure black carbon (BBAs) with no soluble coating compounds, CCN activation is near zero. Additionally, BBAs are great absorbers of incoming shortwave radiation due to their dark surfaces, which can lead to a net warming (Johnson et al., 2004; Brioude et al., 2009; Wilcox, 2010; Zuidema et al., 2016; Mallet et al., 2020; Cochrane et al., 2022).

Given the abundance of aerosols in the Canary and Namibia MSc regions, this study investigates whether the aerosols contribute, together with EIS, to drive the seasonal cycles of LCC in these regions. Previous studies (Klein and Hartmann, 1993; Myers and Norris, 2016; Andersen et al., 2017; Scott et al., 2020) showed that atmospheric stability or EIS is the dominant signal that best explains LCC variability (total variability, encompassing both seasonal and interannual fluctuations).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Maps of satellite retrievals of (a)–(d) DAOD and (e)–(h) BBAOD. Arrows depict wind vectors at 700 hPa from ERA5. The black contours are MODIS LCC. The boxes with white, dashed borders show our study regions in the Canary and Namibia MSc regions. The four rows correspond to the four seasons.</figcaption></figure>

However, the difference between the seasonal cycles of EIS and LCC, especially in the Canary MSc region, has often been overlooked. The role of aerosols as CCN in the eastern Atlantic has been established (Mahowald and Kiehl, 2003; Twohy et al., 2009; Koren et al., 2010; Doherty and Evan, 2014; Ridley et al., 2014; Van Der Does et al., 2016; Amiri-Farahani et al., 2017). However, the contribution of aerosols to driving the seasonal cycle of LCC has not been contrasted to that of EIS, and this topic is addressed here.

The remainder of this article is organized as follows. Section 2 describes the data and method used in our analysis. Section 3 presents and discusses the results of the analysis. Section 4 is the summary and conclusion.

## 2. Data and method

Our study locations are the MSc regions to the west of Canary Islands and Namibia, which are indicated by the boxes with white, dashed borders in Fig. 1. Hereafter, these two regions are referred to simply as Canary and Namibia. We choose the size of the regions to be 20° in

latitude and 35° in longitude, large enough to accommodate the seasonal migration of the LCC maxima, but small enough to not overlap other cloud types (e.g. convective clouds around the Inter Tropical Convergence Zone). Based on our assessment, LCC dominates our study area and appears unaffected by high-level (Figure S1 in the supplementary information) or mid-level cloud cover (not shown).

The LCC is from the monthly mean dataset of low-cloud mask fractions below 680 hPa from MODIS retrievals (Pincus et al., 2020). The meteorological variables are from the ERA5 monthly averaged data (Hersbach et al., 2023a, 2023b).

The aerosol optical depths (AOD) are retrieved from the Copernicus Climate Change Service (2023). In this archive, there are a number of combinations of sensors and retrieval algorithms for each AOD, and we base our choice on their Product User Guide and Specification. For consistency, we use the AOD measurements at 550 nm wavelength, which is the same as previous studies (Yuan et al., 2008; Gryspeedt et al., 2016; Efraim et al., 2020; Cao et al., 2021). For DAs, we use the dataset from the Infrared Atmospheric Sounding Interferometer sensor (on board the METOP-A satellite), which specifically measures mineral
DAOD. For this dataset, the Université Libre de Bruxelles algorithm provides the lowest uncertainty compared to other algorithms. For BBAs, which are associated with fine-mode AOD (Dubovik et al., 2002; Chauvigné et al., 2021), we use the dataset from the Advanced Along-Track Scanning Radiometer sensor (on board the ENVISAT satellite). It provides the most refined and extensive algorithmic development. For this dataset, the Swansea University algorithm is of the highest quality compared to other algorithms.

All datasets have been retrieved with a horizontal resolution of 1° × 1° and interpolated to the same grid. The LCC MODIS data is available in 2003–2022, which ERA5 can match. The DAOD data is available in 2008–2021, while the BBAOD data is available in 2003–2012. Calculations that require data from multiple sources are limited to the overlapping time period for which all required data are available. For example, the regression model of LCC on EIS and DAOD uses data in the 2008–2021 period, and the regression model of LCC on EIS and BBAOD uses data in the 2003–2012 period. We do not consider the regression model of LCC on EIS, DAOD, and BBAOD because the overlapping period of the DAOD and BBAOD datasets (2008–2012) is not sufficiently long to give reliable results.

Simple ordinary least-square linear regression with the monthly data is used to statistically model or predict LCC as follows:

$$LCC_{i,t}^{\text{predicted}} = \alpha_i \text{EIS}_{i,t} + C_{i,t}, \quad (2)$$

$$LCC_{i,t}^{\text{predicted}} = \alpha_i \text{EIS}_{i,t} + \sum_{j=1}^{n} \beta_{i,j} SP_{j,i,t} + C_{i,t}, \quad (3)$$

where the subscript 'i' points to a spatial grid point, the subscript 't' denotes a (monthly) time step, SP_j refers to the jth secondary predictor, α and β are respectively the coefficients of EIS and each secondary predictor, and C is the intercept. The magnitude of each coefficient (α or β) indicates how strongly LCC responds to the corresponding predictor. The regression analysis is applied at each spatial grid point over the whole length of available time to capture the spatial and temporal variations of LCC and the predictors. Eq. (2) is a one-variable regression and it is applied with either EIS, ECTEI or EIS_p as a first-order predictor. Eq. (3) is a multiple-variable regression including one or more n number of secondary predictors together with EIS. We perform regressions using EIS as the primary predictor, with secondary predictors comprising (i) each of the 5STPs, (ii) each AOD, (iii) combinations of the 5STPs with each AOD (seven-variable regression), and (iv) combinations of the 5STPs with each AOD, except one variable (six-variable regression) to isolate the influence of each secondary predictor while accounting for shared variance among predictors.

First, we perform linear regression on the raw data. Then, we also perform linear regression on the deseasonalized data, which is obtained using the following expression:

$$X_t^{\text{deseasonalized}} = X_t - \bar{X}_t, \quad (4)$$

where X_t is the value of the variable X at time t and $\bar{X}_t$ denotes the climatological average (across a period, e.g. 2008–2021) of X for the month that t corresponds to. Our calculation indicates that, in the Canary MSc region, the average variance of the raw LCC data is 175.44%$^2$, and the average variance of the deseasonalized LCC data is 48.03%$^2$. In the Namibia MSc region, the corresponding numbers are 257.62%$^2$ and 59.14%$^2$. Thus, the total variability of LCC is dominated by the seasonal variability in both MSc regions. Although our focus is the seasonal variations, we perform regression analysis on both the raw and deseasonalized data to check for robustness of the results.

The prediction skill is assessed using the root mean square error (RMSE) and the coefficient of determination ($R^2$). A prediction is more skilled with a smaller RMSE and a larger $R^2$. The RMSE and $R^2$ are calculated at each grid point following

$$RMSE_i = \sqrt{\frac{1}{T} \sum_{t}^{T} (LCC_{i,t}^{\text{observed}} - LCC_{i,t}^{\text{predicted}})^2}, \quad (5)$$

$$R_i^2 = 1 - \frac{\sum_{t}^{T} (LCC_{i,t}^{\text{observed}} - LCC_{i,t}^{\text{predicted}})^2}{\sum_{t}^{T} (LCC_{i,t}^{\text{observed}} - \overline{LCC_i^{\text{observed}}})^2}, \quad (6)$$

where

$$\overline{LCC_i^{\text{observed}}} = \frac{\sum_{t}^{T} LCC_{i,t}^{\text{observed}}}{T}, \quad (7)$$

is the temporal average of the observed LCC at location i and T is the number of time steps. The spatial averages of RMSE and $R^2$ in each region of study are calculated as

$$RMSE_{\text{avg}} = \frac{\sum_{i}^{N} RMSE_{i}}{N}, \quad (8)$$

$$R_{\text{avg}}^{2} = \frac{\sum_{i}^{N} R_{i}^{2}}{N}, \quad (9)$$

where N is the number of grid points in each box.

## 3. Results and discussion

The results of our analysis are presented and discussed below. Section 3.1 evaluates EIS as a predictor for LCC in Canary and Namibia. Section 3.2 evaluates other predictors of LCC in Canary. Section 3.3 describes how the effect of DAs on LCC in Canary is EIS-independent. Section 3.4 compares the effects of BBAs and DAs on LCC in Canary. Section 3.5 compares the roles of BBAs in Canary and Namibia.

### 3.1. Evaluation of EIS as the sole predictor of LCC

Fig. 4a shows that, in the Canary region, the LCC predicted using only EIS fails to reproduce the seasonal cycle of LCC. The prediction significantly underestimates the observed summer LCC maximum and slightly overestimates the observed LCC during the other seasons. The spatial profile of the errors is shown in Fig. 5. The largest error is during boreal summer with RMSE up to 27 % near the coast and 20–25 % at the location of the LCC maximum. While EIS is slightly lower in JJA than in MAM, LCC is clearly higher and spatially more extensive in JJA (Fig. 1). The result suggests that a significant LCC amount (such as the summer LCC maximum) can be potentially supported by a relatively weak EIS if other cloud-controlling factors are favourable for cloud formation. The second largest error is during boreal winter with RMSE up to 18 % near the coast. In this season, the EIS-based prediction significantly overestimates LCC near the coastal region, where EIS is sufficiently high (compared to the other seasons) and yet LCC is comparatively the lowest. This suggests that there are other factors that limit LCC in boreal winter. Over the Canary region, the EIS-based prediction has an average RMSE of 10.1 % (which is large relative to the mean LCC of about 40 %) and an average $R^2$ of only 37.5 % (Fig. 6a).

As described in the introduction, the ECTEI and EIS_p try to improve the EIS by capturing the refined thermodynamic structure of the boundary layer. However, Fig. 4a shows that the predicted seasonal cycle of LCC based on either ECTEI or EIS_p barely improves the EIS-based prediction in the Canary MSc region. In particular, neither the ECTEI nor the EIS_p provides a better prediction of the observed LCC summer maximum than the EIS. Fig. 6a shows that the ECTEI-based prediction is generally worse than the EIS-based prediction, while the EIS_p-based prediction is only slightly better. These results show that the limitations of the EIS-based predictions cannot be remediated by improving the metric of the inversion strength. They support our
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Climatological seasonal cycles of the observed LCC compared with the predicted LCC from the various linear regressions in (a)–(c) Canary and (d) Namibia. The single-variable regressions are respectively on EIS, ECTEI, and EIS<sub>p</sub> and the multiple-variable regressions are on EIS and other secondary predictors.</figcaption></figure>

hypothesis that other independent cloud-controlling factors are responsible for a significant part of LCC variability in Canary.

During periods of LCC maxima, the LCC predicted by the ECTEI is slightly lower than that predicted by the EIS, though the differences are minimal. While small, this indicate that the EIS missed information regarding entrainment of drier air from the free troposphere into the boundary layer, which can lead to the dissipation of clouds. This implies that the influence of inversion strength is even less significant, while other factors play a larger role.

In contrast, in the Namibia region, the LCC predicted by EIS alone exhibits nearly the same seasonal cycle as the observed LCC (see Fig. 4d). Additionally, the maximum RMSE is only 10–15 % with a small spatial extent across all seasons (Fig. 5). This leaves very little room for improving on the prediction of LCC. We assess that EIS successfully captures the seasonal cycle of LCC in this region.

## 3.2. Evaluation of additional predictors of LCC in Canary

In this section, we consider additional predictors of LCC in the Canary region, given that the seasonal cycle of LCC in this region is poorly predicted by EIS alone. The additional predictors are DAOD, BBAOD, and the 5STPs.

For the raw data which is dominated by the seasonal variability, one-variable regressions show that no single STP can predict LCC as well as EIS but DAOD alone can predict LCC almost as well as EIS (Fig. 7a). In two-variable regressions using EIS and an additional predictor, the predictions based on EIS and DAOD, and EIS and BBAOD provide the

lowest average RMSEs of 8.0 % and 8.5 %, respectively, as well as the highest $R^2$ of 60 % and 55 %, respectively (Fig. 6a). Moreover, the AODs (especially the DAOD) capture the summer LCC maximum much better than each of the 5STPs (Fig. 4b–c). The importance of aerosols is further highlighted by the statistical significance ($p$-value < 0.05) of the regression coefficients as shown in Table 1. In Canary, the regression coefficients of the aerosols are statistically significant nearly everywhere (> 95%), while those of the 5STPs are significant in 70–80 % of the region (see the fourth column in Table 1).

The combination of EIS, 5STPs, and DAOD or BBAOD in seven-variable regressions further reduces the average error and improves the average $R^2$ of the predicted LCC (Fig. 6a). However, the improvements from the 5STPs may not be robust. In the seven-variable regression on EIS, 5STPs and DAOD, the coefficients from the 5STPs are statistically significant for only 30–66 % of the Canary region (Table 2). In contrast, the coefficients from DAOD are statistically significant nearly everywhere (99.7 %) in the Canary region. Moreover, the addition of the 5STPs to EIS and DAOD can sometimes introduce a slight error. For example, during the month of May, the combination of EIS, DAOD, and 5STPs slightly overestimates the observed LCC compared to just EIS and DAOD (Fig. 4b).

The thermodynamic predictors co-vary and provide similar statistical information, unlike the aerosols. Fig. 4c shows that the seasonal cycle predicted by two-variable regressions including any of the 5STPs are all similar to the EIS-based prediction. This suggests that EIS largely captures the variability associated with other thermodynamic predictors. Additionally, removing one STP from the seven-variable
<figure><img src="image_8.png" /><figcaption>Fig. 5. Maps of the RMSE of the predicted LCC using EIS alone in the Canary and Namibia MSc regions in the four seasons. The thick black contours indicate the seasonal-mean observed LCC.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. Average RMSE (circle markers) and associated $R^2$ (cross markers) for the various linear regressions of the observed LCC in the Canary MSc region. Regression analyses are conducted on the (a) raw data and (b) the deseasonalized data. The single-variable regressions use EIS, ECTEI, or $EIS_p$ as the sole predictor, and the multiple-variable regressions use EIS and other secondary predictors.</figcaption></figure>

regressions (EIS, 5STPs, and DAOD or BBAOD) only marginally reduces the prediction's average $R^2$ while removing DAOD or BBAOD does reduce it significantly (Fig. 7a).

To verify that the effect of the aerosols on LCC is statistically independent from other predictors in Canary, we compute the correlation coefficients between the aerosols and the thermodynamic predictors

(EIS and 5STPs). In the Canary region, the average correlation coefficient between DAOD and EIS is 0.24, and that between BBAOD and EIS is 0.13 (Table 3). Similarly, we observe only minor collinearity between the aerosols and the 5STPs in the Canary region. The magnitudes of the corresponding correlation coefficients are no more than 0.26. An additional collinearity metric, the Variance Inflation Factor, is documented
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Average $R^2$ of the various linear regressions of the observed LCC in the Canary region. Each single-variable regression (1VR, circle markers) uses EIS or each of the other predictors. Each six-variable regression (6VR, triangle marker) uses EIS and five other predictors, excluding one of the 5STPs, DAOD, or BBAOD. Each seven-variable regression (7VR, triangle marker) uses EIS, 5STPs, and either DAOD or BBAOD. Upside-down triangle marker represents analysis using DAOD, while right-side-up triangle marker represents analysis using BBAOD. Regression analyses are conducted on (a) the raw data which contain the total variability, and (b) the deseasonalized data.</figcaption></figure>

**Table 1**

Percentage (%) of each MSc region in which the regression coefficients of EIS (third and fifth columns) and a secondary predictor (SP, fourth and sixth columns) are statistically significant (p-value < 0.05) in two-variable regressions (2VR) with EIS and the secondary predictor. Regressions using DAOD are not performed for Namibia due to the minimal presence of DAs there. Analyses are conducted on the raw data which contain the total variability (third and fourth columns) and the deseasonalized data (fifth and sixth columns).

<table><thead><tr><th rowspan="2">Location</th><th rowspan="2">2VR</th><th>EIS</th><th>SP</th><th>EIS</th><th>SP</th></tr><tr><th>Total</th><th>Total</th><th>Deseasonalized</th><th>Deseasonalized</th></tr></thead><tbody><tr><td rowspan="6">Canary</td><td>EIS, DAOD</td><td>98.6</td><td>99.7</td><td>85.9</td><td>98.0</td></tr><tr><td>EIS, BBAOD</td><td>99.4</td><td>95.3</td><td>69.0</td><td>64.7</td></tr><tr><td>EIS, SST</td><td>98.5</td><td>73.9</td><td>72.6</td><td>45.0</td></tr><tr><td>EIS, T<sub>adv</sub></td><td>98.9</td><td>75.9</td><td>94.0</td><td>75.0</td></tr><tr><td>EIS, WS<sub>sfc</sub></td><td>99.6</td><td>81.3</td><td>89.8</td><td>85.2</td></tr><tr><td>EIS, RH<sub>700</sub></td><td>100.0</td><td>82.3</td><td>92.5</td><td>27.7</td></tr><tr><td rowspan="6">Namibia</td><td>EIS, ω<sub>700</sub></td><td>100.0</td><td>70.3</td><td>92.6</td><td>40.5</td></tr><tr><td>EIS, BBAOD</td><td>100.0</td><td>34.5</td><td>97.3</td><td>29.8</td></tr><tr><td>EIS, SST</td><td>90.6</td><td>78.5</td><td>93.5</td><td>69.7</td></tr><tr><td>EIS, T<sub>adv</sub></td><td>100.0</td><td>77.8</td><td>98.4</td><td>65.7</td></tr><tr><td>EIS, WS<sub>sfc</sub></td><td>100.0</td><td>82.0</td><td>93.6</td><td>90.4</td></tr><tr><td>EIS, RH<sub>700</sub></td><td>100.0</td><td>77.4</td><td>100.0</td><td>83.1</td></tr><tr><td></td><td>EIS, ω<sub>700</sub></td><td>100.0</td><td>33.8</td><td>99.7</td><td>14.8</td></tr></tbody></table>

in the supplementary information.

The improvement from DAOD as an additional predictor alongside EIS is also visible in the deseasonalized data. For the deseasonalized data (Fig. 7b), one-variable regressions show that DAOD alone can predict LCC almost as well as EIS. In two-variable regressions, including DAOD or BBAOD as a predictor of LCC alongside EIS decrease the average RMSE as well as increase the average $R^2$ Fig. 6b). The regression coefficient of DAOD is statistically significant nearly everywhere (96–98 %) in both the two-variable regressions (Table 1) and seven-variable regressions (Table 2). The contribution of BBAOD to the seasonalized variability of LCC is less robust, being statistically significant in 65 % of the Canary region in the two-variable regressions (Table 1) and 54 % in the seven-variable regressions (Table 2). These results indicate that the contributions of DAOD and BBAOD to LCC variability are not exclusively

**Table 2**

Percentage (%) of each MSc region in which the regression coefficients of the predictors are statistically significant (p-value < 0.05) in seven-variable regressions which include EIS, 5STPs, and one type of AOD. The third and fifth columns correspond to the regressions with DAOD as the seventh predictor, while the fourth and sixth columns correspond to the regressions with BBAOD as the seventh predictor. Regressions using DAOD are not performed for Namibia due to the minimal presence of DAs there. Analyses are conducted on the raw data which contain the total variability (third and fourth columns) and the deseasonalized data (fifth and sixth columns).

<table><thead><tr><th rowspan="2">Location</th><th rowspan="2">Variable</th><th>DAOD</th><th>BBAOD</th><th>DAOD</th><th>BBAOD</th></tr><tr><th>Total</th><th>Total</th><th>Deseasonalized</th><th>Deseasonalized</th></tr></thead><tbody><tr><td rowspan="6">Canary</td><td>EIS</td><td>90.6</td><td>90.9</td><td>53.2</td><td>49.1</td></tr><tr><td>DAOD</td><td>99.7</td><td>-</td><td>95.9</td><td>-</td></tr><tr><td>BBAOD</td><td>-</td><td>83.2</td><td>-</td><td>53.9</td></tr><tr><td>SST</td><td>50.1</td><td>40.4</td><td>17.5</td><td>12.3</td></tr><tr><td>T<sub>adv</sub></td><td>33.0</td><td>22.7</td><td>39.2</td><td>27.5</td></tr><tr><td>WS<sub>sfc</sub></td><td>66.0</td><td>61.5</td><td>70.5</td><td>61.2</td></tr><tr><td rowspan="6">Namibia</td><td>RH<sub>700</sub></td><td>30.2</td><td>36.1</td><td>24.1</td><td>13.1</td></tr><tr><td>ω<sub>700</sub></td><td>32.7</td><td>23.3</td><td>16.8</td><td>6.4</td></tr><tr><td>EIS</td><td>-</td><td>91.2</td><td>-</td><td>85.8</td></tr><tr><td>BBAOD</td><td>-</td><td>16.8</td><td>-</td><td>17.0</td></tr><tr><td>SST</td><td>-</td><td>38.3</td><td>-</td><td>10.3</td></tr><tr><td>T<sub>adv</sub></td><td>-</td><td>24.2</td><td>-</td><td>14.5</td></tr><tr><td rowspan="6">Namibia</td><td>WS<sub>sfc</sub></td><td>-</td><td>70.0</td><td>-</td><td>57.3</td></tr><tr><td>RH<sub>700</sub></td><td>-</td><td>62.0</td><td>-</td><td>46.5</td></tr><tr><td>ω<sub>700</sub></td><td>-</td><td>16.5</td><td>-</td><td>17.0</td></tr></tbody></table>

seasonal, even though they are most apparent in the seasonal cycle.

It is interesting that, for the deseasonalized data, the near-surface wind speed (WS<sub>sfc</sub>) gives a prediction with the highest $R^2$ among all the predictors in the Canary region (Fig. 7b). However, the contribution of WS<sub>sfc</sub> to the deseasonalized variability of LCC is less widespread than that of DAOD. In two-variable regressions, the regression coefficient of WS<sub>sfc</sub> is statistically significant in 85 % of the Canary region, while the corresponding number for DDAOD is 98 % (Table 1). Similarly, in the seven-variable regression using EIS, 5STPs and DAOD, the regression coefficient of WS<sub>sfc</sub> is statistically significant in 71 % of the region, while the corresponding number for DAOD is 96 % (Table 2).
<table><caption>**Table 3**
Average correlation coefficient between aerosols and thermodynamic variables in the Canary and Namibia regions.</caption><thead><tr><th>Location</th><th>Variable</th><th>DAOD</th><th>BBAOD</th></tr></thead><tbody><tr><td rowspan="5">Canary</td><td>EIS</td><td>0.24</td><td>0.13</td></tr><tr><td>RH<sub>700</sub></td><td>0.26</td><td>0.16</td></tr><tr><td>SST</td><td>0.06</td><td>0.06</td></tr><tr><td>T<sub>adv</sub></td><td>-0.11</td><td>-0.15</td></tr><tr><td>ω<sub>700</sub></td><td>-0.11</td><td>-0.05</td></tr><tr><td rowspan="6">Namibia</td><td>WS<sub>sfc</sub></td><td>0.1</td><td>0.19</td></tr><tr><td>EIS</td><td>-</td><td>0.45</td></tr><tr><td>RH<sub>700</sub></td><td>-</td><td>-0.19</td></tr><tr><td>SST</td><td>-</td><td>-0.52</td></tr><tr><td>T<sub>adv</sub></td><td>-</td><td>-0.14</td></tr><tr><td>ω<sub>700</sub></td><td>-</td><td>0.01</td></tr><tr><td></td><td>WS<sub>sfc</sub></td><td>-</td><td>0.21</td></tr></tbody></table>

Moreover, when WS<sub>sfc</sub> is removed from the seven-variable regression, the reduction in R<sup>2</sup> is less than that associated with removing DAOD (Fig. 7b). This suggests that the deseasonalized variability of WS<sub>sfc</sub> correlates with those of the other STPs, but not with that of DAOD. In

fact, for the deseasonalized data, the correlation coefficients between WS<sub>sfc</sub> and RH<sub>700</sub>, SST, T<sub>adv</sub>, and ω<sub>700</sub> are respectively -0.08, -0.43, -0.44, and 0.22. In comparison, the correlation coefficients between WS<sub>sfc</sub> and DAOD or BBAOD are only 0.08. An analogous comparison between WS<sub>sfc</sub> and BBAOD can be carried out (see Table 1, Table 2, and Fig. 7b). Though, when WS<sub>sfc</sub> is removed from the seven-variable regression, the reduction in R<sup>2</sup> is greater than that associated with removing BBAOD (Fig. 7b). This confirms our earlier findings that the contribution of BBAOD is less robust, while the shared variance among thermodynamic predictors remains consistent. Overall, we find that the effect of WS<sub>sfc</sub> on the deseasonalized variability of LCC is less robust than that of DAOD, but more robust than that of BBAOD.

In the remaining of the article, we return to our focus on the seasonal variability and the analysis of the raw data.

### 3.3. How DAs affect the seasonal cycle of LCC in Canary

This section describes in detail the influence of DAs on LCC in the Canary Msc region, given that DAOD is the most effective of all the secondary predictors there. Fig. 8 shows that adding DAOD as a

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Maps of RMSE between observed and predicted LCC of (a)–(d) EIS alone and (e)–(h) two-variable regression with EIS and DAOD in the Canary region. The black contour lines are MODIS seasonal-mean LCC. The crosses indicate statistically insignificant coefficients (p-value ≥ 0.05).</figcaption></figure>
predictor alongside EIS improves the LCC prediction almost everywhere in Canary across the seasons. The RMSE decreases by up to 11 % as a result. The improvement is largest during boreal winter and summer, when the prediction based on EIS alone produces the largest errors. The degree of underestimation and overestimation in the region can be inferred from the seasonal cycle shown in Fig. 4, with additional support provided by the map of residuals (the difference between observed and predicted LCC; Figure S2 in the supplementary information).

During winter (DJF), LCC is overestimated by EIS-based prediction, and including DAOD as a predictor reduces the overestimation (Fig. 4b and Fig. 8). It appears that in this season, LCC is limited not by EIS but by the availability of DAs as CCN. Fig. 9 further shows that the influence of DAs on LCC is spatially distinct from that of EIS. The LCC anomaly is negative in most of the Canary MSc region, but the EIS anomaly is negative in the northern side and positive in the southern side (Fig. 9a). The negative LCC anomaly pattern matches much better with that of the DAOD (Fig. 9e).

During summer (JJA), LCC is severely underestimated by the EIS-based prediction (Fig. 4b and Fig. 8), and including the DAOD significantly reduces the large extent of underestimation on both the LCC maximum and near the coastal region (Fig. 8c, g). The LCC maximum is observed in this season given that a large amount of DAs is available

even though EIS is not strong (about 2.5 K only). The maximum DAOD and LCC anomalies are co-located while the maximum EIS anomaly is situated to their north (Fig. 9c, g). Similarly, during spring (MAM, Fig. 9b, f) and autumn (SON, Fig. 9d, h), the spatial pattern of LCC anomaly agrees better with that of DAOD rather than EIS.

The RMSE improvement brought by including DAOD as a predictor is consistent with the study by Doherty and Evan (2014), who found that LCC increases by up to 10 % due to high DA loading in boreal summer over the tropical north Atlantic. Indeed, Fig. 3a–d shows that in the Canary MSc region, DAOD is largest in summer reaching 0.5, twice the maximum spring value. Over the Sahara, DAs are most abundant also in boreal summer and spring, with DAOD of 1.1 and 0.7, respectively. From the figure, we surmise that the Sahara is the source of DAs in the Canary MSc region. The transport of DAs from the Sahara to the Atlantic ocean can be attributed to the African Easterly Jet (AEJ, Jones et al., 2003; Haywood et al., 2008; Knippertz and Todd, 2012; Grogan and Thorn-croft, 2019; Grogan and Nathan, 2021). Fig. 3 displays the wind vectors at 700 hPa, showing the AEJ as a band of strong (mostly) zonal easterly wind across the Atlantic ocean from the western African continent. The AEJ induces updraughts over land, which allows the aerosols to be transported more efficiently (Adebiyi and Zuidema, 2016). During summer, the combination of the strongest AEJ of up to 11 ms⁻¹ and the

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Maps of the seasonal anomalies (local climatological mean removed) of (a)–(d) EIS and (e)–(h) DAOD. Contour lines are the seasonal anomalies of MODIS LCC; solid and dashed lines indicate positive and negative anomalies of LCC.</figcaption></figure>
large concentration of DAs over the Sahara results in maximum aerosol transport and maximum DAOD over the Canary Msc region.

In summary, the analysis in this section suggests that DAs affect LCC independently from EIS. This makes sense since EIS as defined in Eq. (1) does not capture any information about the availability of CCN, while the aerosols do. Indeed, Twohy et al. (2009) found that DAs frequently act as CCN in the Canary MSc region. Further supporting the hypothesis that DAs' effect on LCC is EIS-independent, Fig. 10a shows that LCC increases with DAOD within each bin of EIS. In this analysis, the effects of EIS and aerosols have been separated by grouping the data into bins of different EIS values. Each bin covers a small range of EIS (±1 K), allowing EIS to be considered fixed within each bin. This allows the variability of LCC within each bin to be attributed to the CCN effects of aerosols, independent of EIS. The conclusion does not change when we consider a smaller EIS range (±0.5 K) for each bin (not shown).

### 3.4. Comparison between BBAs' and DAs' effects on LCC in Canary

In Canary, BBAOD as a secondary predictor also provides an overall improvement (a decrease in RMSE and an increase in $R^2$) to predicting LCC, but at a degree less than DAOD (Fig. 6a). Additionally, Fig. 10a–b shows that (for each bin of EIS, where EIS is held constant) there is more LCC per AOD value for DAs than BBAs. This distinction between DAs and BBAs as CCN may, in part, be attributed to discrepancies between AOD and CCN concentrations, as suggested by Bellouin et al. (2020). Additionally, it can also be attributed to the distinct hygroscopic properties

and vertical distribution of these aerosols. This is consistent with previous studies by Dusek et al. (2006); Henning et al. (2012); Maskey et al. (2017); Twohy et al. (2009); Karydis et al. (2011), who found that DAs provide more effective CCN than BBAs due to better hygroscopic properties. The warmer smoke (BBAs) generally rises above the cooler DA layer (Haywood et al., 2008; Osborne et al., 2008), so that CCN activation from both DAs and BBAs contribute to the total LCC but from different heights.

A question arises whether the influence of BBAOD on LCC is statistically distinct from that of DAOD. This cannot be reliably determined through a regression of LCC using both predictors, given insufficient time overlap between the datasets of BBAOD and DAOD. For now, we posit that the influences of DAs and BBAs on LCC are indeed independent because their vertical distributions are different.

### 3.5. Comparison of BBAs' effect on LCC between Canary and Namibia

The BBAOD as a secondary predictor does not perform as well in Namibia compared to Canary. In Namibia, BBAOD does not consistently improve the LCC prediction. Additionally, only 35 % of the Namibia region exhibits statistical significance (Table 1). This could be due to over-fitting since EIS by itself is already an excellent predictor of LCC in the Namibia region. In other words, the contribution of BBAs to LCC's monthly variations in Namibia is not distinct from that of EIS.

Fig. 10b–c however shows that LCC increases with BBAOD within each bin of EIS in both Canary and Namibia. While the effect of EIS on

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** LCC as a function of AOD for bins of different EIS values for (a) DAOD in Canary, (b) BBAOD in Canary, and (c) BBAOD in Namibia. Within each bin of EIS, the LCC at a particular AOD is shown only if there are more than 100 points of data to avoid under-sampling.</figcaption></figure>
LCC is clearly dominant, the effect of BBAs on LCC is also evident in both regions. Given that BBAs can act as CCN anywhere, why do we see BBAs contributing to LCC’s monthly variations in Canary but not in Namibia?

To answer this question, notice that EIS and BBAOD coincidentally have similar seasonal cycles in Namibia (see Fig. 2b), so that the EIS and BBAOD monthly time series are highly correlated to each other there. In Namibia, the correlation coefficient between EIS and BBAOD is 0.45 (Table 3). As a result, the time series of BBAs does not contain distinct statistical information from that of EIS in Namibia. In contrast, the seasonal cycles of EIS and BBAOD in Canary are clearly out of phase: the maximum of EIS (March) lags behind the maximum of BBAOD (July) by four months (Fig. 2a). In Canary, the correlation coefficient between EIS and BBAOD is only 0.13 (Table 3).

## 4. Summary and conclusion

In this article, we have investigated key controlling factors of LCC in the MSc regions to the west of Canary and Namibia. While EIS is considered to be the main predictor of LCC, notable discrepancies exist between the seasonal cycles of LCC and EIS in the Canary MSc region. In this region, EIS as the sole predictor underestimates the observed summer LCC maximum and overestimates the observed LCC in the other seasons. These errors can be as much as 10–29 % in LCC in a significant area of the Canary MSc region. These errors are very large considering that the mean LCC in the Canary MSc region is about 40 %. In contrast, in the Namibia MSc region, EIS is an excellent predictor of the seasonal cycle of LCC.

More accurate metrics of lower-tropospheric stratification, such as ECTEI and EIS<sub>p</sub>, are no closer to explaining the observed summer LCC maximum in Canary. We therefore posit that the monthly variations of LCC in Canary are also driven by other factors besides EIS. In linear regression models, the combination of EIS and either DAOD or BBAOD provides significant improvement compared to just EIS alone in Canary. Other known predictors of LCC (sea surface temperature, horizontal surface temperature advection, near-surface wind speed, and free-tropospheric relative humidity and vertical velocity) also provide improvements but they are less impactful and less robust than the aerosols. In particular, the predicted LCC clearly has a maximum in July (the same as the observed LCC) only when either DAOD or BBAOD is included as a secondary predictor in the regression models.

Of all the secondary predictors, DAOD provides the largest improvement to the prediction of LCC in Canary. Including DAOD reduces both the overestimation of LCC by EIS during winter and the underestimation of LCC by EIS during summer. These improvements are associated with up to 11 % RMSE reductions. Additionally, the coefficient of DAOD in the regression of LCC on EIS and DAOD is statistically significant nearly everywhere (99 %) in the Canary region.

In the Canary MSc region, BBAOD is the second best secondary predictor after DAOD. The smaller effect of BBAs compared to DAs on LCC is attributed to BBAs being less hygroscopic and therefore less effective as CCN than DAs. In the Namibia MSc region, on the other hand, including BBAOD as a secondary predictor alongside EIS does not improve the prediction of LCC. This is because EIS and BBAOD coincidentally have similar seasonal cycles in Namibia. In other words, the monthly variations of BBAs are not statistically distinct from those of EIS in Namibia, unlike in Canary.

In conclusion, our study illustrates the necessity to take into account aerosols, alongside EIS, in the physical or statistical modelling of LCC, especially in regions with high dust activity such as the Canary MSc region. While the Canary region is shaped by unique environmental factors, such as the Saharan Desert, the largest source of dust aerosols, and the African Easterly Jet, which drives significant dust transport, the physical processes identified in this study, including the role of dust aerosols as CCN, are broadly applicable. This emphasizes the importance of integrating aerosol dynamics into cloud cover models across regions with varying levels of aerosol activity.

In the future, a more detailed analysis could complement this study. This includes examining other Sc decks, such as the Californian and Australian Sc regions, which exhibit discrepancies between EIS and LCC; aerosols do not mitigate these discrepancies, whereas the 5STP provides partial improvement (Figure S3 in the supplementary information). The inability to fully explain the observed LCC in these regions points to the influence of additional unidentified mechanisms. These mechanisms will require further investigation beyond the scope of the present study. Furthermore, the concentration of aerosols is only part of the equation; their vertical distribution is equally important. For instance, studies by Van Der Does et al. (2016); Amiri-Farahani et al. (2017) found that DA concentrations are often situated at higher altitudes during summer due to their intersection with the ITCZ. This has significant implications for future climate change. If the ITCZ weakens and shifts toward the equator and outside our study region, as projected by Byrne et al. (2018); Mamalakis et al. (2021), more DAs will likely reside at lower altitudes during summer. This shift would enhance the already significant impact of dust aerosols on LCC in the Canary region by increasing their interaction with cloud formation processes. Finally, while GCMs do not simulate LCC very well (Konsta et al., 2022, e.g.), and this study suggests that a proper representation of aerosols-cloud effects might be necessary to correct simulation of Canary LCC.

### CRediT authorship contribution statement

Waren Dale Soriano: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Tra Dinh: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Gilles Bellon: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Waren Dale Soriano reports financial support was provided by Royal Society of New Zealand Marsden Fund. Tra Dinh reports financial support was provided by Royal Society of New Zealand Marsden Fund. Gilles Bellon reports financial support was provided by Royal Society of New Zealand Marsden Fund. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This research was carried out as part the project titled “Memory in the Clouds”, supported by a Marsden Grant (numbered 3718724) administered by the Royal Society of New Zealand. Gilles Bellon thanks Marc Mallet (CNRM) for an enlightening conversation. ERA5 and aerosol data were downloaded from https://cds.climate.copernicus.eu/cdsapp#!/home. MODIS data were downloaded from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD06COSP_M3_MODIS.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosres.2025.108078.

### Data availability

Data used in this research is acknowledged in the methods section
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