A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations

Xiquan Dong aDepartment of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona

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Xiaojian Zheng aDepartment of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona

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Baike Xi aDepartment of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona

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Shaocheng Xie bLawrence Livermore National Laboratory, Livermore, California

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Abstract

More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), and single-layered low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CFT, CFL, and CFH are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiquan Dong, xdong@arizona.edu.

Abstract

More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), and single-layered low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CFT, CFL, and CFH are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiquan Dong, xdong@arizona.edu.

1. Introduction

Although many improvements have been made in phase 6 of the Coupled Model Intercomparison Project (CMIP6) (Cherian and Quaas 2020; Jiang et al. 2021; Zheng et al. 2021), clouds, water vapor, precipitation, radiation, and their interactions are still a problem in climate models as concluded in the IPCC AR5 and AR6 (Flato et al. 2013; IPCC 2021) and illustrated in many studies (e.g., Jiang et al. 2012; Su et al. 2013; Stanfield et al. 2014, 2015; Dolinar et al. 2015, 2016), which have shown that even when modeled clouds and radiation agree well with observations on a global scale, large biases can occur at regional scales. Due to their complex interactions with incoming solar [shortwave (SW)] radiation and emitted terrestrial [longwave (LW)] radiation, clouds induce both warming and cooling effects on the Earth system (atmosphere and surface).

Clouds are the dominant modulators of radiation both at the surface and top of the atmosphere (TOA), and their impact on Earth’s radiation budget is often represented via bulk cloud properties such as the fraction of sky covered by clouds, cloud height, and cloud microphysical properties (Wielicki et al. 1995). Maritime low-level stratiform clouds, which cover vast areas of the eastern subtropical and midlatitude oceans, can strongly reflect incoming solar radiation and exert complex feedbacks on the climate system (Stephens 2005; Stephens et al. 2010; Wood 2012; Dong and Minnis 2022), making them a key component in Earth’s radiation budget (Klein and Hartmann 1993; Wood et al. 2015; Dong and Minnis 2022). Characterizing cloud effects on the surface radiation budget is a critical component for understanding the current climate and an important step toward simulating potential climate change. The clouds’ modulation of the radiant energy in the Earth–atmosphere system can be quantified as the cloud radiative effect (CRE; in W m−2). The CRE, the change in the net radiation budget due to clouds (Ramanathan et al. 1989; Dong et al. 2006, 2010), represents the bulk effects of clouds on the radiation budget. CRE is a simple but effective means of studying cloud–radiation interactions and diagnosing problems in climate models.

The first global TOA CREs were obtained from the results of the Earth Radiation Budget Experiment (ERBE; Barkstrom 1984) with a global SW CRE of −44.5 W m−2 (cooling effect), and a global LW CRE of 31.3 W m−2 (warming effect). Thus, clouds had a net cooling effect of −17 W m−2 on the earth (Ramanathan et al. 1989). Using the results of the Clouds and the Earth’s Radiant Energy System (CERES; Wielicki et al. 1998; Loeb et al. 2018; Dong and Minnis 2022), the recent studies of Loeb et al. (2018), Dolinar et al. (2015), and Stanfield et al. (2015) have obtained the similar CREs to those in the study of Ramanathan et al. (1989), indicating that there is no significant change in CREs from ERBE to CERES.

In addition to the global TOA CREs derived from satellite observations, the SW, LW, and net CREs at the surface have been calculated from some long-term ground-based observations. One of the examples is the comprehensive measurements of cloud and radiation parameters on a nearly continuous basis at a few permanent surface sites and more mobile sites operated by the Department of Energy Atmospheric Radiation Measurement (ARM; see Ackerman and Stokes 2003) Program. These long-term ground-based observations have been used by many studies to investigate the seasonal and diurnal variations of clouds and radiation budgets at the surface, as well as the impacts of different types of clouds on the surface radiation budgets over different climatological regions. For example, Dong et al. (2006) analyzed different types of clouds and their associated CREs using 6 years of ground-based observations at the ARM Southern Great Plains (SGP) Central Facility (SCF; 36.6°N, 97.5°W). Over the Arctic region, Dong et al. (2010) generated a 10-yr record of Arctic cloud fraction and CRE using data collected at the ARM North Slope of Alaska (NSA; 71.3°N, 156.6°W) site and the nearby NOAA Barrow Observatory (BRW) from June 1998 to May 2008.

To provide much needed maritime cloud and radiation measurements, the DOE ARM Eastern North Atlantic site (ENA; 39.09°N, 28.03°W; 30.48 m above mean sea level) was established on Graciosa Island in the Azores, Portugal (Wood et al. 2015; Dong et al. 2014). In addition to providing a climatology of single-layered marine boundary layer (MBL) cloud and drizzle properties from ARM ENA ground-based observations in the previous study (Wu et al. 2020), we also provide a climatology of cloud fractions (CFs) for all scene types, their impact on the surface radiation budget (SRB), and associated CREs in this study. These results will provide a great opportunity to improve the understanding of cloud–radiation interaction for maritime clouds, as well as to make a comparison of CFs, SRB, and CREs between the midlatitude ocean (ENA) and land (SGP) because these two sites are located at similar latitudes.

The ENA is a region of persistent but diverse subtropical maritime clouds (Dong et al. 2014; Wood et al. 2015). Dong et al. (2014) summarized the synoptic patterns over the Azores using MERRA-2 reanalysis and concluded that subsidence from a persistent high pressure system was dominant during the summer months, which resulted in fewer total clouds but more single-layered MBL clouds. In contrast, a low pressure system was dominant during the winter months, producing more clouds, including more multilayered clouds and deep frontal clouds associated with midlatitude cyclones. In this study, we entirely rely on a combination of radar and lidar/ceilometer measurements to identify clear sky, total cloud cover, and single-layered low and high cloud conditions; we further investigate their impact on the SRB, and calculate their corresponding CREs over the ARM ENA site during the period July 2015–September 2019. These results provide an invaluable data source for investigating the seasonal and diurnal variations of maritime clouds and radiative properties, advancing our understanding of various maritime clouds and their associated CREs, and enabling climate and forecast modelers to understand more of the evaluation of their simulations over the ENA region. The article is organized as follows: section 2 describes the dataset and methodology, and section 3 presents the results and discussions. The impacts of surface scene types (land vs ocean) on the calculated SW CREs, comparisons with the SGP results, and the sensitivities of CREs to cloud fraction are discussed in sections 46, respectively. The summary and conclusions are presented in section 7.

2. Data and methodology

In this section, we will briefly summarize the instruments and their corresponding measurements, as well as the methods of calculating CREs, at the ARM ENA site during the studying period, July 2015–September 2019, because these instruments and measurements are almost identical to those in Dong et al. (2006, 2010, 2014). Nearly the same instruments and observations at the ARM SGP and NSA sites, as well as the methods of calculating CREs, have been described comprehensively in Dong et al. (2006, 2010). Note that all data used in this study are temporally collocated at a 5-min resolution.

The ARM laser ceilometer and micropulse lidar (MPL) measurements are used to derive the cloud-base heights (Hbase) with uncertainties of ∼10 m (Morris 2016) and ∼30 m (Widener et al. 2012), respectively. The Ka-band ARM zenith radar (KAZR) measurements are used to derive the cloud-top heights (Htop) with an uncertainty of 30 m (Wu et al. 2020). Cloud fraction derived from the ARM point upward-looking radar–lidar (narrow field of view) pair of measurements should be treated differently from the satellite-derived CF, as discussed in Xi et al. (2010). It is the percentage of returns that are cloudy within a specified sampling time period (e.g., hour and month). That is, the CF is simply calculated by the ratio of the number of 5-min samples when both the radar and the lidar/ceilometer detected clouds to the total number of samples when all measurements were available (lidar/ceilometer and radar measurements, downwelling and upwelling SW and LW fluxes) at given time period. The monthly CF is therefore obtained with consideration of the diurnal cycles of CF during that month. There are a total of 33 473 h of all-sky samples, which is 90.7% of all possible data during 4-yr period, hence securing the samples to be representative enough that the mean values of both seasonal and diurnal variations are self-consistent. The total cloud fraction (CFT) and single-layered CFs are defined in Dong et al. (2006, 2010), and the detailed cloud type classification method is described in Xi et al. (2010). The term CFT is the fraction of time when a cloud is detected anywhere in the vertical column, and the single-layered low cloud fraction (CFL) is the fraction of time when low clouds (Htop < 3 km) occur without clouds above them, while the high cloud amount (CFH) is determined for clouds having Hbase higher than 6 km with no clouds underneath.

The clear-sky and cloudy precipitable water vapor (PWV) and cloudy LWP are retrieved from the microwave radiometer brightness temperatures measured at 23.8 and 31.4 GHz using a statistical retrieval method with an uncertainty of ∼10% (Liljegren et al. 2001). The cloud optical depth (COD) is retrieved from the multifilter rotating shadowband radiometer (MFRSR) measurements at a wavelength of 415 nm for single-layered clouds only (Turner et al. 2021). Note that the single-layered low-cloud CODs retrieved from MFRSR (listed later in Table 4) are similar to the results calculated from the parameterizations in Dong et al. (1998) as presented in Dong and Minnis (2022).

At the ARM ENA site, the downwelling and upwelling broadband shortwave (SW; 0.3–3 μm) and longwave (LW; 4–50 μm) fluxes at the surface were measured by the up- and down-looking standard Eppley precision spectral pyranometers (PSPs) and precision infrared pyrgeometers (PIRs), respectively. In this study, the SW and LW fluxes are obtained from the ARM Radiative Flux Analysis (RADFLUX) value-added product, which uses multiple instruments as input (Riihimaki et al. 2019, 2021). The typical calibration uncertainties of PSP and PIR are 3% and 2%, respectively (Andreas et al. 2018), or ∼10 and 4 W m−2 for SW and LW fluxes, respectively (Long and Shi 2008). The clear-sky SW fluxes are estimated using the approach of Long and Ackerman (2000) and are available in the RADFLUX product.

The method of calculating the cloud radiative effect was described in the studies of Ramanathan et al. (1989), Dong et al. (2006, 2010). The SW CRE (CRESW) and LW CRE (CRELW) at the surface are defined in Eqs. (1a) and (1b). They are calculated by the difference between the net surface fluxes (down minus up) when clouds are present (Q1 and F1) and when clouds are absent (Q0 and F0, CF = 0) as follows:
CRESW=Q1Q0 and
CRELW=F1F0
respectively. Note that Q0 and F0 are the same under clear sky in all CRE calculations, whereas Q1 and F1 will be calculated under all sky, total cloud, low clouds, and high clouds. The methods to calculate monthly and hourly mean fluxes and CREs were thoroughly described in Dong et al. (2006, 2010), and their CRE uncertainties were also discussed (equivalent to or less than their flux uncertainties).

In addition to the ARM ground-based measurements over the ENA site, the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5; hourly resolution) has been used to obtain the oceanic surface albedo and sea surface temperature (SST) surrounding the ENA site under all-sky conditions. The ERA5 data are averaged over a grid box of 0.56° × 0.56° centered at the ENA site. The oceanic SW albedo is used to calculate the oceanic surface upwelling SW flux with the ARM PSP measured downwelling SW flux, and the SST is used to calculate the oceanic surface upwelling LW flux based on the Stefan–Boltzmann law (oceanic surface is assumed as blackbody).

3. Results and discussion

a. Monthly variations

Figure 1 shows the monthly variations of total CF (CFT), low CF (CFL), and high CF (CFH), as well as the downwelling SW and LW fluxes at the surface during the clear-sky, all-sky, and total and low and high cloud conditions from July 2015 to September 2019. The monthly variations of CFT, CFL, and CFH in Fig. 1a are similar to the results of Dong et al. (2014), who used 19-month DOE ARM Mobile Facility (AMF) observations at the Azores. Monthly means of CFT decrease gradually from January (0.853) to August (0.672), and then climb up to 0.826 in October and level off in November–December. For CFL, they remain nearly constant (0.310) from January to May, reach a maximum in July (0.494), and then gradually decrease to December (0.188). The monthly variation of CFH almost mirrors that of CL with a local minimum (0.07) during June–July and a local maximum (0.187) in December. The annual averages of CFT, CFL, and CFH in this study are 0.785, 0.342, and 0.123, respectively, slightly higher than the results (0.702, 0.271, and 0.106, from June 2009 to December 2010) of Dong et al. (2014). Dong et al. (2006) did a sensitivity study and concluded that a minimum 3-yr dataset is required to have statistical results for each scene type. Thus the results in Dong et al. (2014) may not provide enough samples to represent the climatology of the Azores, but the results in this study are. As discussed in Dong et al. (2014), the more CFL (and less CFT) during summer months primarily resulted from a persistent high pressure system over the Azores, whereas a low pressure system was dominant during winter months, producing more multilayered and deep frontal clouds (more CFT and less CFL).

Fig. 1.
Fig. 1.

Seasonal variations of cloud fractions derived from ARM radar–lidar–ceilometer measurements and observed downwelling fluxes at ARM ENA site during the period 17 Jul 2015–30 Sep 2019. (a) Monthly mean total (CFT) and single-layered low (CFL, Ztop ≤ 3 km) and high (CFH, Zbase ≥ 6 km) cloud fractions. (b) Downwelling shortwave (SW) and (c) longwave (LW) fluxes measured by upward PSP and PIR.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

Monthly means of downwelling SW and LW fluxes are shown in Figs. 1b and 1c for clear sky, all sky, and total, low, and high clouds, respectively. All monthly means of SW fluxes gradually increase from January to June when the summer solstice occurs, and then decrease smoothly toward December (the winter solstice). The smooth variations in all scene types have demonstrated that the monthly means of downwelling SW fluxes are determined primarily by seasonal variations in the intensity and duration of insolation and are only partially dependent on CF and cloud optical depth (COD). As expected, the downwelling SW fluxes during clear skies reach the maximum, followed by high clouds, all sky, low and total clouds. These results are very reasonable because both CFL and CFT are higher, and their CODs are thicker (low cloud CODs ∼ 5–30; Dong and Minnis 2022; Table 4) than the high cirrus clouds (CODs < 2; Sassen and Comstock 2001; Comstock et al. 2007). Moreover, the CODs retrieved from MFRSR measurements used in this study also show that the single-layered low cloud CODs are much higher than those for single-layered high clouds (figure not shown). In addition to the intensity and duration of insolation, both CF and COD are the two most important parameters for determining downwelling SW fluxes at the surface.

In opposition to their SW counterparts, clear sky and low clouds have the lowest and greatest downwelling LW fluxes, respectively. Different from the downwelling SW fluxes, the cloudy downwelling LW fluxes are governed primarily by their cloud liquid water path (LWP), cloud-base temperature, and emissivity (Dong et al. 2006, 2010; Shupe and Intrieri 2004). Dong et al. (2014) found that most MBL clouds have a higher LWP (∼100 gm−2) and uniform cloud emissivity (ε ∼ 1), and thus cloud-base temperature plays an important role in determining downwelling LW flux at the surface. Low cloud-base temperatures are warm (277–290 K) with liquid phase and uniform cloud emissivity at the Azores. Therefore, downwelling LW fluxes for low clouds are the highest among all scene types, ranging from 352.4 W m−2 in December to 402.1 W m−2 in August with an annual average of 372.3 W m−2. Conversely, high clouds with cold cloud-base temperature and low cloud emissivity, as well as the water vapor absorption between the cloud base and the surface, produce a small downwelling LW radiation. Consequently, the high-cloud-emitted downwelling LW radiation at the surface is the smallest (337 W m−2) among all scene types, and only slightly greater than the clear sky (322.5 W m−2). Clear-sky values are determined primarily by the atmospheric temperature profile and precipitable water vapor (PWV) (Dong et al. 2006). On top of cloud-base temperature and emissivity, the seasonal variations of downwelling LW radiation are also determined by the seasonal variations in cloud-base temperature and atmospheric PWV with the maxima during summer months and the minima during winter months (Dong et al. 2014).

The monthly means of the LW, SW, and NET CREs for all scene types are illustrated in Fig. 2, and their seasonal and annual averages are summarized in Table 1. LW and SW CREs are governed mainly by the differences in downwelling LW and SW fluxes between cloudy and clear-sky conditions because their upwelling flux differences are very small compared to their downwelling counterparts, especially for LW flux. For instance, the downwelling SW flux difference between low clouds and clear sky is −93.1 W m−2 (148.9 − 242), while the upwelling SW difference is ∼−19.0 W m−2 (−93.1 × 0.202 for all-sky albedo) with a net difference of −74.1 W m−2. This value is almost the same as low cloud SW CRE (−73.7 W m−2 in Fig. 2b). The small difference comes from different surface albedos between cloudy (0.211) and clear sky (0.199) calculated from ARM ENA upward PSP measurements. This argument (CRE mainly depends on downwelling fluxes) is more valid for LW CREs, as demonstrated in Figs. 1 and 2. The downwelling LW flux difference between low clouds and clear sky is 49.8 W m−2 (372.3 − 322.5), its upwelling LW flux difference is ∼−1.4 W m−2 (409.5 − 410.9; Fig. 5b), and its CRE is 51.4 W m−2 which is determined almost entirely by the downwelling LW flux difference. Note that the monthly means of SW and LW CREs in Fig. 2 and Table 1 are based on PSP and PIR measurements at ARM ENA site, which actually represent the island surface albedo and surface temperature (upwelling LW flux) since the ENA site is located on top of the northwestern cliff of the Graciosa Island, not right at the ocean surface. Therefore, to examine the CREs under a representative oceanic surface condition, we modify these values using the ENA ocean surface albedo and sea surface temperature obtained from the ERA5 reanalysis. The updated results are listed in Table 2 and are discussed in the next section.

Fig. 2.
Fig. 2.

Seasonal variations of cloud radiative effects (CREs) at ARM ENA site during the period 17 Jul 2015–30 Sep 2019: (a) LW, (b) SW, and (c) NET.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

Table 1

Seasonal and annual averages of SW, LW, and NET CREs at the ARM ENA site (W m−2).

Table 1
Table 2

Modified CRE based on the ERA5 oceanic albedo (W m−2). LW CREs remain the same as in Table 1.

Table 2

Figure 2a shows the monthly means of the LW CREs for all scene types where they all remain nearly invariant (less than 10 W m−2) through the course of the year due to their similar seasonal variations in downwelling LW fluxes (Fig. 1c). The highest CRE is low clouds (51.4 W m−2), whereas high clouds have the lowest LW CRE (13.9 W m−2), consistent with their downwelling LW flux differences; others fall between them. For seasonal variations, both low and total clouds have the greatest LW CREs in March and June, and the smallest LW CREs in September and December. Conversely, the SW CREs for low and total clouds have the largest negative values (−73.7 and −76.6 W m−2), especially during warm months (∼−110 W m−2 during May–August), and the smallest negative values (from −40 to −50 W m−2) during December–January. The monthly means of SW CREs for high clouds are nearly constant, with an annual average of −26.8 W m−2, ranging from −21.9 W m−2 in December to −32.2 W m−2 in May. NET CREs, the sum of SW and LW CREs, are determined primarily by SW CREs throughout most of the year. During winter months, however, the negative SW CREs and positive LW CRFs nearly cancel out each other, resulting in slight positive NET CREs. The annual averages of NET CREs are −12.9, −19.0, −22.2, and −28.0 W m−2, respectively, for high clouds, all sky, low, and total clouds.

b. Diurnal cycle

The hourly means of CFs and downwelling SW and LW fluxes were calculated from all samples in the local time (LT) hour during the 4-yr period in order to investigate the cloud diurnal cycles and their impact on the SRB. As shown in Fig. 3a, hourly means of CFT and CFL are relatively invariant, and the variation of CFT basically follows CFL. For example, both CFT and CFL occur more from middle night to early morning (2300–1000 LT) than afternoon (1300–2000 LT) with only a few percent variations (ΔCF ∼ 0.05). That is, both CFT and CFL maxima occur during middle night and early morning and minima occur during the afternoon. The CFL maxima resulted from mixing driven by nocturnal LW radiative cooling at the cloud top (e.g., Wood 2012; Dong et al. 2014). The CFL minima were caused by strong solar radiation absorption near the cloud top (warming cloud layer), which partially offsets the LW radiative cooling and suppresses turbulence and cloud formation within MBL. The CFH values nearly mirror those of CFT and CFL, monotonically increasing from ∼0.08 at 0800 LT to 0.16 at 2000 LT due to strong convection during late afternoon.

Fig. 3.
Fig. 3.

As in Fig. 1, but for the diurnal variations.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

To investigate the impact of the diurnal variations of CFs on the SRB, we plot hourly means of downwelling SW and LW fluxes in Figs. 3b and 3c. The relative magnitudes of the hourly means are the same as their monthly counterparts (i.e., the maximum downwelling SW and minimum downwelling LW fluxes occur in clear skies, and vice versa for low clouds). The hourly means of downwelling SW fluxes basically follow the solar zenith angle for all scene types. Note that the local noon at the Azores is ∼1250–1300 LT, and thus the hourly means of downwelling SW fluxes in Fig. 3b are not perfectly symmetric. The nearly symmetric distributions for all scene types have demonstrated that there are enough samples for each scene type per hour. Single-layered middle clouds are not included in this study due to insufficient samples. The nearly symmetric distributions also reveal that the downwelling SW fluxes for all scene types are not significantly impacted by the small diurnal variations of their CFs.

The hourly means of clear-sky downwelling LW fluxes gradually increase from the middle of the night (318 W m−2 during 0100–0200 LT) to local noon (328 W m−2 during 1200–1400 LT) due to increased atmospheric effective temperature and precipitable water vapor. High clouds have a similar variation to clear sky, increasing from 332.8 W m−2 during the period 0100–0500 LT to 342.5 W m−2 at 1300–1500 LT. Hourly means of downwelling LW fluxes for all sky and low clouds are nearly invariant with ranges of ΔLW = 3.8 and 2.1 W m−2, respectively, through the course of the day, which is consistent with the small variation (ΔTbase ∼ 1.5 K) of low cloud-base temperature (Dong et al. 2014). For total clouds, their LW fluxes vary slightly, with minima of 367.4 W m−2 during the period 2000–0300 LT and maxima of 372.2 W m−2 during the period 0800–1200 LT.

Figure 4 shows the hourly means of clear-sky net (down–up) SW, LW, and NET fluxes and the corresponding CREs for all scene types. The hourly means of clear-sky net LW flux (Fig. 4a) change only 58.3 W m−2 with a minimum (−125.1 W m−2; upwelling greater than downwelling) at local noon and maximum (−66.8 W m−2) during the period 2000–0500 LT. The net SW fluxes basically follow their downwelling counterparts (max = 618.7 W m−2 at local noon). Despite the negative clear-sky NET flux during the nighttime hours, the daily NET flux, determined by net SW flux, is a positive (downward), 104.6 W m−2, on average, over the course of the day. These clear-sky values are used as a reference (Q0 or F0) in calculating SW and LW CREs for all scene types.

Fig. 4.
Fig. 4.

Diurnal variations of (a) net SW and LW fluxes (down–up) at the surface, as well as their sum (NET). (b) LW CRE, (c) SW CRE, and (d) NET CRE.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

Using Eqs. (1a) and (1b), as well as clear-sky net SW and LW fluxes, LW and SW CREs are calculated and presented in Figs. 4b and 4c. The hourly means of LW CREs basically mirror their corresponding hourly mean net LW fluxes (Fig. 4a) due to their small diurnal variations in downwelling LW fluxes. As expected, the hourly means of LW CREs for low clouds are the highest among all scene types, increasing from 42.5 W m−2 during the period 2200–0500 LT to 64.4 W m−2 at 1100–1200 LT. High clouds basically follow the trend of low clouds with a range of 7.8–21.0 W m−2, and have the lowest mean LW CRE. All sky and total cloud LW CREs fall between low and high clouds. The magnitudes of negative SW CREs (Fig. 4c) during the daylight can be up to four times larger than their LW counterparts, primarily resulting from their higher clear-sky net SW fluxes (Fig. 4a). As a result, the SW cooling effect surpasses the LW warming effect, which results in net cooling effects throughout the day for all scene types.

4. Impact of surface scene types (land vs ocean) on calculated CREs

In this section, we will investigate the uncertainties in SW and LW CREs presented in Figs. 2 and 4 due to different surface albedos and upwelling LW fluxes by land and ocean. The upwelling SW flux was measured by down-looking PSP at ARM ENA site, which represents the surface condition of the Graciosa Island, not ocean, whereas this study focuses on the maritime clouds and associated SRB and CREs. Thus, we use the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) provided ocean surface albedo (for all sky) to represent the typical ocean surface at the nearest grid point of the ARM ENA site. This oceanic surface albedo is used in Eq. (1a) to revise the SW CREs in Table 1 and the modified CREs are listed in Table 2.

Figure 5a shows monthly means of surface albedos derived from the PSP measurements at ARM ENA during clear sky, all sky, and total clouds, as well as from the ERA5 reanalysis under all-sky condition. The annual averages of surface albedos measured by PSP are 0.211, 0.202, and 0.199 for clear sky, all sky, and total clouds with a maximum in March and a minimum in July. The annual differences for different sky conditions are very small (Δ = 0.211 − 0.199 = 0.012) where direct solar radiation is dominant during clear sky, whereas it is almost entirely diffuse solar radiation for cloudy conditions. These surface albedos represent the typical grassland, and are very similar to the results at the ARM SGP site (Dong et al. 2006). The ERA5 reanalyzed surface albedo (for all sky) represents an oceanic surface with an annual average of 0.0688, ranging from 0.063 in July to 0.078 in December. Since the differences in surface albedos under different sky conditions are very small as shown in Fig. 5a, we can use the ERA5 derived oceanic surface albedo under all-sky condition to represent different sky conditions in recalculating SW CREs. Using the monthly means of downwelling SW flux for each scene type in Fig. 1b and the ERA-5 derived ocean surface albedo, we recalculate the SW CREs for each scene type. The annual averages of the modified SW CREs for all sky, total, low, and high clouds are −66.1, −89.3, −85.9, and −31.2 W m−2, respectively (Table 2). Compared to the original SW CREs in Table 1, the modified SW CREs are more negative, with differences of −9.4, −12.7, −12.2, and −4.4 W m−2, respectively, for all sky and for total, low, and high clouds because lower oceanic surface albedo (0.0688) is used to replace the PSP measured land surface albedo (∼0.2).

Fig. 5.
Fig. 5.

Monthly means of (a) surface albedos measured by ARM ENA PSPs under clear-sky, all-sky, and total cloud conditions, as well as derived from ERA5 reanalysis from all sky, and (b) upwelling LW fluxes measured by ARM ENA PIR measurements under different skies and derived from ERA-5 reanalysis from all sky.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

Figure 5b shows the monthly means of upwelling LW fluxes measured by ARM downward-looking PIR under clear-sky, all-sky and total cloud conditions, as well as those derived from ERA5 reanalysis for all sky. The annual averages of upwelling LW fluxes measured by PIR are 410.9, 410.4, and 409.5 W m−2, respectively, for clear sky, all sky, and total clouds. The nearly identical upwelling LW fluxes for different scene types are canceled out in calculating LW CREs in Eq. (1b). Although the annual average difference between PIR measurement and ERA5 reanalysis for all sky is only −1.9 W m−2, their monthly mean differences can be up to ±10 W m−2 during winter months and summer months as illustrated in Fig. 5b. These differences reflect the stronger seasonal variation of surface temperature at the ENA site (island) than over ocean. However, the calculated LW CREs are determined almost entirely by the downwelling LW flux difference, and the ERA5 derived upwelling LW flux is canceled out in calculating LW CRE. Thus, we keep the original LW CREs in Table 2. That is, the modified NET CREs are determined completely by the modified SW CREs, and they are more negative than original NET CREs.

There is a possibility that the PSP measured downwelling SW fluxes at ENA site during cloudy conditions, especially for low clouds, may be higher than the true values (over ocean) due to the multiple reflections of solar radiation between the cloud layer and the reflective surface. Dong and Mace (2003) used a two-stream discrete-ordinate method (Liou 1974) to quantitatively estimate the surface albedo (Rsfc) effect on cloud transmission (measured downwelling SW fluxes under cloudy conditions). When Rsfc = 0, the cloud albedo and transmission solely depend on cloud optical depth, but they both monotonically increase with increased Rsfc. Given cloud optical depth = 10, Rsfc = 0.202 at ENA site, and Rocean = 0.0688 over ocean, the PSP measured downwelling SW fluxes for low clouds may be 3% more than the true values, which is ∼4.5 W m−2 (148.9 × 0.03). That is, the calculated SW CREs in Table 1 could be 4.5 W m−2 more negative if using the ocean surface to replace the land surface. However, this calculation is based on the assumption of the uniform surface scene type, while the ARM ENA site is located at northeastern Graciosa Island, Azores, surrounded by ocean (∼500 m away from the surface site). Therefore, it is difficult to quantitatively estimate the impact of surface albedo on downwelling SW fluxes in this study.

5. Comparisons with ARM SGP results

Both ARM ENA and SGP sites are located at middle latitudes (39.09° and 36.61°N) but represent different surface scene types (ocean vs land). Therefore, it is necessary to compare their similarities and differences in clear-sky parameters and cloud properties, as well as their impacts on SRB and CRE, between the two sites. The ARM ENA site is dominated by clean maritime air masses with periodic episodes of polluted air masses advected from the continents (Logan et al. 2014; Zheng et al. 2022), while the ARM SGP site represents continental air masses with periodic episodes of maritime air masses advected from the Gulf of Mexico (Logan et al. 2018; Zheng et al. 2020). The different air masses, precipitable water vapor, cloud fractions, and cloud macrophysical and microphysical properties, especially for low cloud properties over two sites (Dong et al. 2005, 2014; Dong and Minnis 2022), will definitely impact their SRB. In this section, we will investigate the similarities and differences of their aerosol optical depth (AOD) and PWV during clear-sky conditions, cloud macrophysical (cloud-base height and temperature) and microphysical properties (COD and LWP), their impacts on the SRB, and their corresponding CREs under all scene types over two sites.

The annual averages of downwelling SW and LW fluxes, cloud properties, and their corresponding CREs for all scene types are listed in Tables 3, 4, and 5, respectively. Since the clear-sky downwelling fluxes are independent of clouds, it is necessary first to understand these clear-sky results, and then use these results as a reference to discuss the downwelling fluxes during all-sky and cloudy conditions. As listed in Table 3, the annual averages of clear-sky downwelling SW fluxes are 242 and 247.6 W m−2 (ΔSW = −5.6 W m−2), respectively, at the ARM ENA and SGP sites, while their LW fluxes are 322.5 and 314.4 W m−2 (ΔLW = +8.1 W m−2). Dong et al. (2006) investigated the clear-sky downwelling LW and normalized SW (SW/μ0; the ratio of SW flux to μ0, the cosine of SZA) fluxes as a function of PWV based on the 6-yr ARM SGP dataset, and concluded that SW/μ0 generally decreases and LW flux increases fairly smoothly with increasing PWV. In addition to PWV, they also pointed out that SW/μ0 is also sensitive to atmospheric aerosols. Nearly the same annual averages of clear-sky downwelling SW fluxes indicate similar atmospheric backgrounds over the two sites, such as clear-sky PWV (2.13 cm at ENA vs 1.90 cm at SGP), aerosol optical depth at 550 nm (0.06 at ENA vs 0.10 at SGP), and solar zenith angle (SZA = 63.64° at ENA vs SZA = 62.55° at SGP). The slightly higher downwelling SW flux (Δ = 5.6 W m−2) at the SGP site could result from lower PWV and smaller SZA than those of ENA, while slightly higher downwelling LW flux at ENA could attribute to higher PWV. This small difference (ΔLW = +8.1 W m−2) is consistent with their PWV difference (ΔPWV = +0.23 cm), which can be explained by the relationships in Fig. 10 of Dong et al. (2006). That is, higher PWV can result in lower downwelling SW flux and higher downwelling LW flux. The small differences in clear-sky downwelling SW and LW fluxes suggest that these two sites have similar atmospheric backgrounds although they represent different surface scene types (ocean vs land).

Table 3

Annual averages of downwelling SW and LW fluxes (W m−2) at ARM ENA and SGP sites.

Table 3
Table 4

Seasonal and annual means of single-layered low cloud properties and fluxes at ARM ENA and SGP sites. Annual SWdn and LWdn of low clouds remain the same as in Table 3.

Table 4
Table 5

Annual averages of SW, LW, and NET CREs (W m−2) at ARM ENA and SGP sites. ENA CREs remain the same as in Table 2.

Table 5

Table 3 lists the annual averages of downwelling SW and LW fluxes for all sky and total, low, and high clouds at the ARM ENA and SGP sites. Using the clear-sky fluxes as a baseline, we can quantitatively discuss the impacts of CF and PWV on all-sky downwelling SW and LW fluxes. The annual averages of all-sky downwelling SW fluxes at ENA and SGP are 170.1 and 195.3 W m−2, respectively. With nearly the same SZA, the different SW flux could be attributed to their different total CFs (0.785 at ENA vs 0.488 at SGP) and all-sky PWVs (2.62 cm at ENA vs 2.28 cm at SGP). That is, all-sky downwelling SW flux decreases with increased total CF and all-sky PWV. Conversely, all-sky downwelling LW flux increases with both total CF and all-sky PWV. In addition to CF and PWV, cloud optical depth can be another important parameter to affect all-sky downwelling SW flux. Given the challenge of retrieving the CODs for all different types of clouds, however, it is difficult to quantitatively estimate the impact of COD on all-sky downwelling SW flux at the surface. Nevertheless, it is possible to retrieve the CODs for single-layered clouds, such as low clouds, which will be discussed as follows.

As discussed in Figs. 1b and 3b, the monthly and hourly means of downwelling SW fluxes for all scene types are nearly symmetric and independent of their corresponding CFs. This is mainly attributed to two reasons: 1) no strong seasonal and hourly variations in CFs and 2) enough cloud samples, especially for low clouds. For high clouds, their diurnal variations of CFH monotonically increase from ∼0.08 at 0800 LT to 0.16 at 2000 LT with an annual average of 0.123 (Fig. 3a), which results in more downwelling SW flux at 0800 LT than that at 1700 LT. The different SW fluxes during these two periods may result from insufficient samples for high clouds because we had similar results at the ARM SGP site. As shown in Fig. 4 of Dong et al. (2006), the downwelling SW fluxes for low clouds during the afternoon were noticeably larger than during the morning, which coincides with decreasing CFL during the afternoon. The annual average of CFL at SGP site was 0.11, which might not have enough cloudy samples.

In addition to cloud fraction, cloudy downwelling SW fluxes also depend on COD, SZA, and PWV, while cloudy downwelling LW fluxes are primarily governed by their cloud-base temperature and emissivity and PWV. For example, the nearly same downwelling SW fluxes (145.1 W m−2 at ENA vs 150.7 W m−2 at SGP) for total clouds at two sites result from a combination of COD, SZA, and PWV (2.77 cm at ENA vs 2.65 cm at SGP). Note that the downwelling SW and LW fluxes for high clouds at two sites only differ by a few watts per square meter, indicating that their high cloud optical and physical properties do not have significant differences.

To further explore the similarities and differences of single-layered low cloud properties and their impacts on downwelling SW and LW fluxes over the two sites, we provide the winter, summer, and annual means of COD, PWV, LWP, cloud-base height (Hb) and temperature (Tb), and downwelling SW and LW fluxes in Table 4. As discussed in Dong et al. (2006), cloudy downwelling SW flux at the surface is primarily determined by COD, while PWV plays a minor role. As listed in Table 4 and presented in Dong and Minnis (2022), the low cloud LWPs at ENA are comparable to those at SGP with some seasonable variations. As Dong and Minnis (2022) presented, the low cloud microphysical properties at ENA represent typical MBL cloud properties (cloud-droplet effective radius re ∼ 12.0 μm and number concentration Nc ∼ 80 cm−3), while they are 8.9 μm and 244 cm−3 at SGP, representing typical continental stratus cloud microphysical properties. With similar cloud PWV and LWP, the significant differences in re and Nc at the two sites arise primarily from different cloud condensation nuclei (CCN) concentrations (608 cm−3 cm at SGP vs 212 cm−3 at ENA). Thus, the CODs at SGP are as twice as those at ENA, even more in some seasons, which could inevitably impact their downwelling SW fluxes at the surface.

The downwelling SW flux at ENA is 15 W m−2 more than at SGP, which is attributed to its small COD (12.3 vs 26. 7 at SGP). The SW flux difference (ΔSWdn = 17.1 W m−2) is even larger during winter season due to the large difference in their COD (ΔCOD = 14.9), while summer SW difference at the two sites is small due to a combination of low COD and PWV at ENA. Since most of the low clouds are optically thick (COD > 5; LWP > 50 g m−2) in this study and at the SGP site (Dong et al. 2005), they can be treated as near blackbody (emissivity ∼1). Cloud-base temperature (Tb) and cloud PWV will play an important role in determining downwelling LW flux at the surface. Dong et al. (2006) concluded that downwelling LW flux is four times more sensitive to cloud PWV than downwelling SW flux. Table 4 shows that the annual mean LW flux at ENA is 17.4 W m−2 more than that at SGP, which primarily results from its higher Tb (283.2 K at ENA vs 281.9 K at SGP) and secondarily from its PWV (2.39 cm at ENA and 2.37 cm at SGP). This conclusion is further confirmed by the seasonal means listed Table 4. The winter LW flux at ENA is 61.8 W m−2 more than that at SGP due to its combined effect of higher Tb (280.5 K vs 273.0 K at SGP) and PWV (1.86 cm vs 1.05 cm at SGP). In contrast, the summer LW flux at SGP is slightly higher than that at ENA due to its slightly higher Tb and PWV.

Table 5 lists the annual averages of SW, LW, and NET CREs for all scene types at two sites. As discussed in section 3a, SW and LW CRFs are determined primarily by the differences in downwelling SW and LW fluxes between cloudy and clear-sky conditions. This is particularly true over ENA when comparing the results in Table 3. However, the reflected SW fluxes and emitted LW fluxes from the land surface at SGP will partially compensate for their downwelling counterparts, which results in less negative SW and more positive LW CREs. Through the comparisons of CF, SRB, and CRE between ENA and SGP, we draw the following conclusions. There are more clouds for all types at ENA than at SGP. These clouds combined with their physical and optical properties result in different downwelling SW and LW fluxes, although their clear-sky backgrounds are similar to each other. There are stronger SW cooling effects and LW warming effects for all sky and total clouds at ENA than at SGP, while these effects are reversed for low and high clouds. As summarized in Table 5, ENA has a stronger cooling effect for all sky due to its high CFT, while the NET CREs for all cloud types agree within 1–4 W m−2 with the SGP results.

6. Sensitivities of CREs to CFT

To quantify the impact of CFT on all-sky solar transmission, SW CRE, and LW CRE, we adapt the same method as that at ARM SGP site (Dong et al. 2006) except that we use daily means for annual, summer, and winter seasons in this study. Figure 6 shows the scatterplots between all-sky daily means of all-sky SW transmission and SW and LW CREs against their CFT at ARM ENA with the empirical relationships and correlations overlaid. All-sky SW transmission is defined as the ratio of the difference in mean downwelling SW fluxes under all-sky and clear-sky conditions to the mean downwelling clear-sky fluxes. As expected, all-sky SW transmissions decrease with increasing CFT. Both annual and summer have nearly the same relationships; that is, all-sky SW transmissions decrease from 0 to −0.34 when CFT increases from 0.3 to 1.0 for annual and summer, and from 0.5 to 1.0 for winter. The SW transmission in winter appears to be more sensitive to the CFT than the summer and annual cases, similar to the relationship at SGP (Fig. 3a of Dong et al. 2006).

Fig. 6.
Fig. 6.

Dependence of all-sky (a)–(c) SW transmission, (d)–(f) SW CRE, and (g)–(i) LW CRE on daily mean total cloud fraction at ARM ENA site, 17 Jul 2015–30 Sep 2019. Dots denote daily mean samples, and lines denote polynomial regression fits. (left) Annual and (center) summer and (right) winter seasons.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0290.1

To investigate the sensitivities of all-sky SW CREs to CFT, we compare the empirical relationship in Figs. 6d with that in Fig. 3b of Dong et al. (2006). With a range of CFT = 0.5–0.7, the all-sky SW CREs at SGP increase from −38 to −59.4 W m−2, while they increase from −7.0 to −20.84 W m−2 at ENA. That is, the sensitivity of all-sky SW CRE to change in CFT (the cloud radiative kernel) at SGP is more sensitive to CFT than at ENA. The ratios of ΔCRESW/ΔCFT are −1.07 and −0.689 W m−2 %−1, respectively, at SGP and ENA. Figures 6e and 6f have shown that there is large seasonal variation in cloud radiative kernel where all-sky SW CREs increase from −18.2 to −39.9 W m−2 (−1.08 W m−2 %−1) for summer, and −2.2 to −12.2 W m−2 (−0.5 W m−2 %−1) for winter with a range of CFT = 0.5–0.7. Therefore, it should be used with caution for different seasonal cloud radiative kernels.

Under the same range of CFT, the all-sky LW CREs at SGP increase from 18.8 to 33.5 W m−2 (0.735 W m−2 %−1), while they increase from 31.9 to 38.2 W m−2 (0.318 W m−2 %−1) at ENA. The all-sky LW CREs at ENA increase 43.2 to 49.0 W m−2 (0.291 W m−2 %−1) for summer and 19.6 to 29.6 W m−2 (0.5 W m−2 %−1) for winter. These results have demonstrated that with a 0.01 or 1% increase in CFT, the all-sky SW CRE at SGP has −0.381 W m−2 (or 55.3%) more cooling effect and LW CRE has +0.417 W m−2 (or 131%) more warming effect than those at ENA.

7. Summary and conclusions

More than four years of ground-based measurements taken at the ARM ENA site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. Furthermore, we analyze the impacts of surface scene types on calculated CREs, compare them with the results at ARM SGP site, and finally explore the sensitivities of SW and LW CREs to CFT. From the results and comparisons with other studies, we have the following conclusions:

  1. Monthly means of CFT decrease from January (∼0.853) to August (0.672), and then climb up to 0.826 in October and level off in November–December. For CFL, they remain nearly constant (∼0.310) from January to May, reach a maximum in July (∼0.494), and then gradually decrease to December (∼0.188), whereas CFH values almost mirror those of CL and vary slightly over the course of the year with a minimum of 0.07 during June–July and a maximum of 0.187 in December. Downwelling SW fluxes for all scene types monotonically increase from January to June and then decrease smoothly toward December. As expected, the downwelling SW fluxes during clear skies reach the maximum, followed by high clouds, all-sky, low clouds, and total clouds. Opposite to their corresponding SW counterparts, clear sky and low clouds have the lowest and greatest downwelling LW fluxes, respectively.

  2. The monthly means of LW, SW, and NET CRFs for all scene types are governed primarily by the differences in downwelling LW and SW fluxes between cloudy and clear-sky conditions. This is especially true for LW CREs, but only partially true for SW CRE as listed in Tables 1 and 2. There are no strong seasonal variations in LW CREs, but more negative for SW CREs during the summer months. As expected, low and total clouds have the largest positive LW CREs and negative SW CREs among all scene types. The annual averages of SW CREs calculated from PSPs measurements are −56.7, −76.6, −73.7, and 26.8 W m−2, respectively, for all sky, total, low, and high clouds. Using ERA5 derived ocean surface albedo to replace PSP measured one, we recalculate the SW CREs for all scene types. The annual means of the modified SW CREs for all sky, total, low, and high clouds are −66.1, −89.3, −85.9, and −31.2 W m−2, respectively (Table 2). LW CREs are not significantly impacted by land and ocean scene types.

  3. Hourly means of CFT, CFL, and CFH are relatively invariant with a few percent varying around their annual averages of 0.785, 0.342, and 0.123. Hourly means of downwelling SW fluxes basically follow the solar zenith angle with nearly symmetric distributions for all scene types. Hourly means of downwelling LW fluxes for all sky and low clouds are nearly invariant through the course of the day, whereas clear sky, high, and total clouds have ∼10 W m−2 more during daytime. Hourly means of SW, LW, and NET CREs are similar to their monthly mean downwelling flux counterparts, with the largest positive LW and negative SW and NET CREs around local noon. Overall, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux resulting in a NET cooling effect for all scene types.

  4. Comparing the cloud properties and CREs at both the ARM ENA and SGP sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric backgrounds, such as AOD, PWV, etc. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky PWV. Low cloud PWVs and LWPs at the two sites are close to each other, whereas their re, Nc, and CCN have large differences, which result in much higher COD at SGP than at ENA. COD plays an important role in determining downwelling SW flux, while Tb and PWV are important for downwelling LW flux at the surface. There are more total and low clouds at ENA than at SGP, and stronger SW cooling effects and LW warming effects for all sky and total clouds at ENA than at SGP, while these effects are reversed for low and high clouds.

  5. Sensitivity studies have shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than those at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). These results have demonstrated that with 0.01 or 1% increase in CFT, the all-sky SW CRE at SGP has −0.381 W m−2 (or 55.3%) more cooling effect, and LW CRE has +0.417 W m−2 (or 131%) more warming effect than those at ENA. Different sensitivities are found at ENA during summer and winter seasons: ΔCRESW/ΔCFT is −1.08 W m−2 %−1 in summer vs −0.5 W m−2 %−1 in winter, and ΔCRELW/ΔCFT is 0.291 W m−2 %−1 in summer vs 0.5 W m−2 %−1 in winter.

This 4-yr dataset over the ARM ENA should also provide statistically reliable estimates of the monthly and diurnal variations of different types of cloud amounts, and their impact on the SRB and CREs, for climate modelers to test cloud–radiation–climate interactions. The comparisons with the SGP results suggest that their clear-sky downwelling SW and LW fluxes are similar to each other, while their all-sky downwelling SW and LW fluxes and CREs are dependent on their total cloud fraction and PWV. The results over two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land. More comparisons with other climatological regions, such as the Arctic and tropical regions, are warranted in future studies.

Acknowledgments.

This research was primarily supported by the NSF under Grant AGS-2031750 at the University of Arizona, and also supported as part of the “Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES)” project (74358), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Earth System Modeling program with the subcontract to the University of Arizona. This research is also partially supported by the DOE ARM program through the subcontract to the University of Arizona under prime contract DE-AC52-07NA27344 between LLNS and DOE. Work at LLNL was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.

Data availability statement.

The ground-based measurements were obtained from the Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S. Department of Energy (DOE) Office of Energy Research, Office of Health and Environmental Research, and Environmental Sciences Division. The data can be downloaded from http://www.archive.arm.gov/.

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  • Jiang, J. H., H. Su, L. Wu, C. Zhai, and K. A. Schiro, 2021 : Improvements in cloud and water vapor simulations over the tropical oceans in CMIP6 compared to CMIP5. Earth Space Sci., 8, e2020EA001520, https://doi.org/10.1029/2020EA001520.

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  • Liou, K.-N., 1974: Analytic two-stream and four-stream solutions for radiative transfer. J. Atmos. Sci., 31, 14731475, https://doi.org/10.1175/1520-0469(1974)031<1473:ATSAFS>2.0.CO;2.

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  • Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s Radiant Energy System (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895918, https://doi.org/10.1175/JCLI-D-17-0208.1.

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  • Logan, T., B. Xi, and X. Dong, 2014: Aerosol properties and their influences on marine boundary layer cloud condensation nuclei at the ARM mobile facility over the Azores. J. Geophys. Res. Atmos., 119, 48594872, https://doi.org/10.1002/2013JD021288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Logan, T., X. Dong, and B. Xi, 2018: Aerosol properties and their impacts on surface CCN at the ARM Southern Great Plains site during the 2011 Midlatitude Continental Convective Clouds Experiment. Adv. Atmos. Sci., 35, 224233, https://doi.org/10.1007/s00376-017-7033-2.

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    • Search Google Scholar
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  • Long, C. N., and T. P. Ackerman, 2000: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J. Geophys. Res., 105, 15 60915 626, https://doi.org/10.1029/2000JD900077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Long, C. N., and Y. Shi, 2008: An automated quality assessment and control algorithm for surface radiation measurements. J. Open Atmos. Sci., 2, 2337, https://doi.org/10.2174/1874282300802010023.

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  • Morris, V. R., 2016: Ceilometer instrument handbook. Tech Rep. DOE/SC-ARM-TR-020, 26 pp., https://www.arm.gov/publications/tech_reports/handbooks/ceil_handbook.pdf.

    • Crossref
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  • Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 5763, https://doi.org/10.1126/science.243.4887.57.

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  • Riihimaki, L. D., K. L. Gaustad, and C. N. Long, 2019: Radiative Flux Analysis (RADFLUXANAL) Value-Added Product: Retrieval of clear-sky broadband radiative fluxes and other derived values. Tech. Rep. DOE/SC-ARM-TR-228, 23 pp., https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-228.pdf.

    • Crossref
    • Export Citation
  • Riihimaki, L. D., and Coauthors, 2021: The shortwave spectral radiometer for atmospheric science: Capabilities and applications from the ARM user facility. Bull. Amer. Meteor. Soc., 102, E539E554, https://doi.org/10.1175/BAMS-D-19-0227.1.

    • Crossref
    • Search Google Scholar
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  • Sassen, K., and J. M. Comstock, 2001: A midlatitude cirrus cloud climatology from the facility for atmospheric remote sensing. Part III: Radiative properties. J. Atmos. Sci., 58, 21132127, https://doi.org/10.1175/1520-0469(2001)058<2113:AMCCCF>2.0.CO;2.

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  • Shupe, M. D., and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17, 616628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2.

    • Crossref
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    • Export Citation
  • Stanfield, R. E., X. Dong, B. Xi, A. Kennedy, A. D. Del Genio, P. Minnis, and J. H. Jiang, 2014: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part I: Cloud fraction and properties. J. Climate, 27, 41894208, https://doi.org/10.1175/JCLI-D-13-00558.1.

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  • Stanfield, R. E., X. Dong, B. Xi, A. D. Del Genio, P. Minnis, D. Doelling, and N. Loeb, 2015: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA radiation budgets and CREs. J. Climate, 28, 18421864, https://doi.org/10.1175/JCLI-D-14-00249.1.

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  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

  • Stephens, G. L., and Coauthors, 2010: The dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, https://doi.org/10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Su, H., and Coauthors, 2013: Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using “A-Train” satellite observations and reanalysis data. J. Geophys. Res. Atmos., 118, 27622780, https://doi.org/10.1029/2012JD018575.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., C. Lo, Q. Min, D. Zhang, and K. Gaustad, 2021: Cloud Optical Properties from the Multifilter Shadowband Radiometer (MFRSRCLDOD): An ARM value-added product. Tech. Rep. DOE/SC-ARM-TR-047, 23 pp., https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-047.pdf.

  • Widener, K., N. Bharadwaj, and K. Johnson, 2012: Ka-band ARM zenith radar (KAZR) instrument handbook. Tech. Rep. DOE/SC-ARM/TR-106, 25 pp., https://www.arm.gov/publications/tech_reports/handbooks/kazr_handbook.pdf.

    • Crossref
    • Export Citation
  • Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76, 21252153, https://doi.org/10.1175/1520-0477(1995)076<2125:MTPERO>2.0.CO;2.

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  • Wielicki, B. A., and Coauthors, 1998: Clouds and the Earth’s Radiant Energy System (CERES): Algorithm overview. IEEE Trans. Geosci. Remote Sens., 36, 11271141, https://doi.org/10.1109/36.701020.

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  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

  • Wood, R., and Coauthors, 2015: Clouds, aerosols, and precipitation in the marine boundary layer: An ARM mobile facility deployment. Bull. Amer. Meteor. Soc., 96, 419440, https://doi.org/10.1175/BAMS-D-13-00180.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, P., X. Dong, and B. Xi, 2020: A climatology of marine boundary layer cloud and drizzle properties derived from ground-based observations over the Azores. J. Climate, 33, 10 13310 148, https://doi.org/10.1175/JCLI-D-20-0272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xi, B., X. Dong, P. Minnis, and M. M. Khaiyer, 2010: A 10-year climatology of cloud cover and vertical distribution derived from both surface and GOES observations over the DOE ARM SGP site. J. Geophys. Res., 115, D12124, https://doi.org/10.1029/2009JD012800.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, X., B. Xi, X. Dong, T. Logan, Y. Wang, and P. Wu, 2020: Investigation of aerosol–cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) ground-based measurements. Atmos. Chem. Phys., 20, 34833501, https://doi.org/10.5194/acp-20-3483-2020.

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    • Search Google Scholar
    • Export Citation
  • Zheng, X., C. Tao, C. Zhang, S. Xie, Y. Zhang, X. Dong, and B. Xi, 2021: Evaluation of aerosols, clouds, and radiation in CMIP6 models over different climate regimes using ARM Data-oriented Metrics and Diagnostics Package version 3. 2021 Fall Meeting, New Orleans, LA, Amer. Geophys. Union, Abstract A45F-1917.

  • Zheng, X., B. Xi, X. Dong, P. Wu, T. Logan, and Y. Wang, 2022: Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic. Atmos. Chem. Phys., 22, 335354, https://doi.org/10.5194/acp-22-335-2022.

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    • Search Google Scholar
    • Export Citation
  • Jiang, J. H., H. Su, L. Wu, C. Zhai, and K. A. Schiro, 2021 : Improvements in cloud and water vapor simulations over the tropical oceans in CMIP6 compared to CMIP5. Earth Space Sci., 8, e2020EA001520, https://doi.org/10.1029/2020EA001520.

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  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006,1587:TSCOLS.2.0.CO;2.

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    • Search Google Scholar
    • Export Citation
  • Liljegren, J. C., E. E. Clothiaux, G. G. Mace, S. Kato, and X. Dong, 2001: A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature. J. Geophys. Res., 106, 14 48514 500, https://doi.org/10.1029/2000JD900817.

    • Crossref
    • Search Google Scholar
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  • Liou, K.-N., 1974: Analytic two-stream and four-stream solutions for radiative transfer. J. Atmos. Sci., 31, 14731475, https://doi.org/10.1175/1520-0469(1974)031<1473:ATSAFS>2.0.CO;2.

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    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s Radiant Energy System (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895918, https://doi.org/10.1175/JCLI-D-17-0208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Logan, T., B. Xi, and X. Dong, 2014: Aerosol properties and their influences on marine boundary layer cloud condensation nuclei at the ARM mobile facility over the Azores. J. Geophys. Res. Atmos., 119, 48594872, https://doi.org/10.1002/2013JD021288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Logan, T., X. Dong, and B. Xi, 2018: Aerosol properties and their impacts on surface CCN at the ARM Southern Great Plains site during the 2011 Midlatitude Continental Convective Clouds Experiment. Adv. Atmos. Sci., 35, 224233, https://doi.org/10.1007/s00376-017-7033-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Long, C. N., and T. P. Ackerman, 2000: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J. Geophys. Res., 105, 15 60915 626, https://doi.org/10.1029/2000JD900077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Long, C. N., and Y. Shi, 2008: An automated quality assessment and control algorithm for surface radiation measurements. J. Open Atmos. Sci., 2, 2337, https://doi.org/10.2174/1874282300802010023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morris, V. R., 2016: Ceilometer instrument handbook. Tech Rep. DOE/SC-ARM-TR-020, 26 pp., https://www.arm.gov/publications/tech_reports/handbooks/ceil_handbook.pdf.

    • Crossref
    • Export Citation
  • Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 5763, https://doi.org/10.1126/science.243.4887.57.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riihimaki, L. D., K. L. Gaustad, and C. N. Long, 2019: Radiative Flux Analysis (RADFLUXANAL) Value-Added Product: Retrieval of clear-sky broadband radiative fluxes and other derived values. Tech. Rep. DOE/SC-ARM-TR-228, 23 pp., https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-228.pdf.

    • Crossref
    • Export Citation
  • Riihimaki, L. D., and Coauthors, 2021: The shortwave spectral radiometer for atmospheric science: Capabilities and applications from the ARM user facility. Bull. Amer. Meteor. Soc., 102, E539E554, https://doi.org/10.1175/BAMS-D-19-0227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sassen, K., and J. M. Comstock, 2001: A midlatitude cirrus cloud climatology from the facility for atmospheric remote sensing. Part III: Radiative properties. J. Atmos. Sci., 58, 21132127, https://doi.org/10.1175/1520-0469(2001)058<2113:AMCCCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17, 616628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanfield, R. E., X. Dong, B. Xi, A. Kennedy, A. D. Del Genio, P. Minnis, and J. H. Jiang, 2014: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part I: Cloud fraction and properties. J. Climate, 27, 41894208, https://doi.org/10.1175/JCLI-D-13-00558.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanfield, R. E., X. Dong, B. Xi, A. D. Del Genio, P. Minnis, D. Doelling, and N. Loeb, 2015: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA radiation budgets and CREs. J. Climate, 28, 18421864, https://doi.org/10.1175/JCLI-D-14-00249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

  • Stephens, G. L., and Coauthors, 2010: The dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, https://doi.org/10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Su, H., and Coauthors, 2013: Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using “A-Train” satellite observations and reanalysis data. J. Geophys. Res. Atmos., 118, 27622780, https://doi.org/10.1029/2012JD018575.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., C. Lo, Q. Min, D. Zhang, and K. Gaustad, 2021: Cloud Optical Properties from the Multifilter Shadowband Radiometer (MFRSRCLDOD): An ARM value-added product. Tech. Rep. DOE/SC-ARM-TR-047, 23 pp., https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-047.pdf.

  • Widener, K., N. Bharadwaj, and K. Johnson, 2012: Ka-band ARM zenith radar (KAZR) instrument handbook. Tech. Rep. DOE/SC-ARM/TR-106, 25 pp., https://www.arm.gov/publications/tech_reports/handbooks/kazr_handbook.pdf.

    • Crossref
    • Export Citation
  • Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76, 21252153, https://doi.org/10.1175/1520-0477(1995)076<2125:MTPERO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., and Coauthors, 1998: Clouds and the Earth’s Radiant Energy System (CERES): Algorithm overview. IEEE Trans. Geosci. Remote Sens., 36, 11271141, https://doi.org/10.1109/36.701020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

  • Wood, R., and Coauthors, 2015: Clouds, aerosols, and precipitation in the marine boundary layer: An ARM mobile facility deployment. Bull. Amer. Meteor. Soc., 96, 419440, https://doi.org/10.1175/BAMS-D-13-00180.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, P., X. Dong, and B. Xi, 2020: A climatology of marine boundary layer cloud and drizzle properties derived from ground-based observations over the Azores. J. Climate, 33, 10 13310 148, https://doi.org/10.1175/JCLI-D-20-0272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xi, B., X. Dong, P. Minnis, and M. M. Khaiyer, 2010: A 10-year climatology of cloud cover and vertical distribution derived from both surface and GOES observations over the DOE ARM SGP site. J. Geophys. Res., 115, D12124, https://doi.org/10.1029/2009JD012800.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, X., B. Xi, X. Dong, T. Logan, Y. Wang, and P. Wu, 2020: Investigation of aerosol–cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) ground-based measurements. Atmos. Chem. Phys., 20, 34833501, https://doi.org/10.5194/acp-20-3483-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, X., C. Tao, C. Zhang, S. Xie, Y. Zhang, X. Dong, and B. Xi, 2021: Evaluation of aerosols, clouds, and radiation in CMIP6 models over different climate regimes using ARM Data-oriented Metrics and Diagnostics Package version 3. 2021 Fall Meeting, New Orleans, LA, Amer. Geophys. Union, Abstract A45F-1917.

  • Zheng, X., B. Xi, X. Dong, P. Wu, T. Logan, and Y. Wang, 2022: Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic. Atmos. Chem. Phys., 22, 335354, https://doi.org/10.5194/acp-22-335-2022.

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  • Fig. 1.

    Seasonal variations of cloud fractions derived from ARM radar–lidar–ceilometer measurements and observed downwelling fluxes at ARM ENA site during the period 17 Jul 2015–30 Sep 2019. (a) Monthly mean total (CFT) and single-layered low (CFL, Ztop ≤ 3 km) and high (CFH, Zbase ≥ 6 km) cloud fractions. (b) Downwelling shortwave (SW) and (c) longwave (LW) fluxes measured by upward PSP and PIR.

  • Fig. 2.

    Seasonal variations of cloud radiative effects (CREs) at ARM ENA site during the period 17 Jul 2015–30 Sep 2019: (a) LW, (b) SW, and (c) NET.

  • Fig. 3.

    As in Fig. 1, but for the diurnal variations.

  • Fig. 4.

    Diurnal variations of (a) net SW and LW fluxes (down–up) at the surface, as well as their sum (NET). (b) LW CRE, (c) SW CRE, and (d) NET CRE.

  • Fig. 5.

    Monthly means of (a) surface albedos measured by ARM ENA PSPs under clear-sky, all-sky, and total cloud conditions, as well as derived from ERA5 reanalysis from all sky, and (b) upwelling LW fluxes measured by ARM ENA PIR measurements under different skies and derived from ERA-5 reanalysis from all sky.

  • Fig. 6.

    Dependence of all-sky (a)–(c) SW transmission, (d)–(f) SW CRE, and (g)–(i) LW CRE on daily mean total cloud fraction at ARM ENA site, 17 Jul 2015–30 Sep 2019. Dots denote daily mean samples, and lines denote polynomial regression fits. (left) Annual and (center) summer and (right) winter seasons.

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