North Atlantic Oscillation–Associated Variation in Cloud Phase and Cloud Radiative Forcing over the Greenland Ice Sheet

Haotian Zhang aCollege of Global Change and Earth System Science, Beijing Normal University, Beijing, China

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Chuanfeng Zhao bDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

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Yan Xia aCollege of Global Change and Earth System Science, Beijing Normal University, Beijing, China

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Yikun Yang bDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

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Abstract

The Greenland ice sheet (GrIS) has been losing mass at an accelerating rate in recent decades due to warming, and understanding the underlying mechanisms, such as the impacts of clouds, is essential. Using spaceborne data, this study investigates the spatial distribution of ice clouds and liquid-bearing clouds (LBCs) over the GrIS and their surface radiative forcing effects during summer daytime from 2006 to 2017, along with their characteristics during the North Atlantic Oscillation (NAO) events. Due to the perennial high-albedo surface, both ice and LBCs have a less important shortwave radiative cooling effect than in other environments. Based on the spatial variation pattern of clouds with the NAO index, the GrIS can be divided into three regions: the western, central, and eastern GrIS. During the positive NAO, the westerly wind strengthens in the western region, which causes the fraction of both ice clouds and LBCs to increase, and the cloud radiative effect at the surface increases by 2.07 W m−2; the temperature decreases in the central region, the fraction of ice clouds increases, the fraction of LBCs decreases, and the net radiative forcing is −2.05 W m−2; and sinking airflow is generated in the eastern region, both ice cloud and LBCs decrease, and the net cloud radiative effect at the surface is −1.34 W m−2. The spatial and temporal variations in clouds in different phases over the GrIS are closely related to the NAO, and the response of clouds to changes in the atmospheric circulation field during the NAO varies in different regions of the GrIS.

Significance Statement

This study investigates the associations between the North Atlantic Oscillation and the spatiotemporal variations in clouds, including both cloud fraction and cloud phases, and further examines the impacts of these changes on the surface radiation balance. The findings can help us improve our understanding of cloud variability and the corresponding influence on surface radiation over the GrIS, which are essential for better prediction of ice coverage over this region and for more efficient protection of ecosystems located there.

© 2023 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: Chuanfeng Zhao, cfzhao@pku.edu.cn

Abstract

The Greenland ice sheet (GrIS) has been losing mass at an accelerating rate in recent decades due to warming, and understanding the underlying mechanisms, such as the impacts of clouds, is essential. Using spaceborne data, this study investigates the spatial distribution of ice clouds and liquid-bearing clouds (LBCs) over the GrIS and their surface radiative forcing effects during summer daytime from 2006 to 2017, along with their characteristics during the North Atlantic Oscillation (NAO) events. Due to the perennial high-albedo surface, both ice and LBCs have a less important shortwave radiative cooling effect than in other environments. Based on the spatial variation pattern of clouds with the NAO index, the GrIS can be divided into three regions: the western, central, and eastern GrIS. During the positive NAO, the westerly wind strengthens in the western region, which causes the fraction of both ice clouds and LBCs to increase, and the cloud radiative effect at the surface increases by 2.07 W m−2; the temperature decreases in the central region, the fraction of ice clouds increases, the fraction of LBCs decreases, and the net radiative forcing is −2.05 W m−2; and sinking airflow is generated in the eastern region, both ice cloud and LBCs decrease, and the net cloud radiative effect at the surface is −1.34 W m−2. The spatial and temporal variations in clouds in different phases over the GrIS are closely related to the NAO, and the response of clouds to changes in the atmospheric circulation field during the NAO varies in different regions of the GrIS.

Significance Statement

This study investigates the associations between the North Atlantic Oscillation and the spatiotemporal variations in clouds, including both cloud fraction and cloud phases, and further examines the impacts of these changes on the surface radiation balance. The findings can help us improve our understanding of cloud variability and the corresponding influence on surface radiation over the GrIS, which are essential for better prediction of ice coverage over this region and for more efficient protection of ecosystems located there.

© 2023 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: Chuanfeng Zhao, cfzhao@pku.edu.cn

1. Introduction

The Greenland ice sheet (GrIS) is a critical component of the global climate system and one of the most significant regions of high-latitude climate change in the Northern Hemisphere (Alexander et al. 2019; Sherman et al. 2020; Sjolte et al. 2020; Shao et al. 2021; Plach et al. 2021). Clouds play a crucial role in Greenland by regulating the surface radiation balance (Solomon et al. 2017; Miller et al. 2018; Wang et al. 2019). The cloud radiative effect (CRE) depends strongly on the surface albedo and cloud thermodynamic phase (Loeb et al. 2007; Dong et al. 2016; McCoy et al. 2016; Schill et al. 2020). As global warming continues, there is an urgent need to better understand climate change on the GrIS and the climatic factors associated with the melting of the GrIS (Hansen et al. 2016; Hofer et al. 2020; Muntjewerf et al. 2020; Hanna et al. 2021). However, due to the lack of observational data, previous studies on the spatial distribution and radiative effects of clouds with different phases over the GrIS are scarce, especially in response to the atmospheric circulation background field over a long time period and at a large spatial scale (Kuipers Munneke et al. 2011; Lenaerts et al. 2019).

Ice loss from the GrIS has accelerated rapidly over the past 20 years, much faster than what current models have simulated (Mernild et al. 2011; Rinke et al. 2019). This makes the GrIS an important contributor to global sea level rise, accounting for 0.5 out of a total of 3.2 mm yr−1 (Khan et al. 2014). If this acceleration continues, the loss of ice from the GrIS alone could cause as much as 9 cm of sea level rise before 2050, compared to the 15–20 cm of total sea level rise observed in the last century (Church and White 2006). The future of the GrIS has become one of the largest unknowns in climate change. However, changes of the GrIS are dependent on various surface processes, which are closely related to atmospheric and oceanic conditions (Holland et al. 2008; van den Broeke et al. 2009). With the increase in global warming and the frequent occurrence of extreme weather events, Greenland’s climate change has become a focus of current studies (Hay 2014; Le clec’h et al. 2019; Shannon et al. 2019; Aoki et al. 2021).

As one of the regions with the highest surface albedo in the world, the radiative effect of clouds over the GrIS differs from that over other regions of the Arctic in that the net radiative effect of clouds on the surface is, in most cases, a warming effect (Miller et al. 2015; Van Tricht et al. 2016), particularly in winter and spring. In the Arctic, the longwave radiative effect of clouds is usually more dominant than the shortwave radiative effect, especially for thin clouds. The albedo effect of thin clouds is low in the Arctic, but the longwave-warming effect is still strong, maximizing the net warming effect at the surface (Shupe and Intrieri 2004; Sedlar et al. 2011; Bennartz et al. 2013; Zhao and Garrett 2015; Miller et al. 2017). In July 2012, the Summit ground-based observatory in Greenland showed that liquid-bearing clouds (LBCs) at low altitudes can cause surface temperatures to be above the melting point of snow, which leads the surface to melt (Bennartz et al. 2013). The melting surface, in turn, reduces the surface albedo, thus absorbing more solar radiation, and the radiative forcing of clouds changes accordingly (Box et al. 2012). The nature of polar clouds plays a vital role in the energy balance of the polar surface, especially low-level LBCs (Bennartz et al. 2013). In addition to LBCs, ice clouds are widespread over Greenland and warm the ice sheet surface (Van Tricht et al. 2016). Ice clouds are generally more transparent to solar shortwave radiation than LBCs. Their higher altitude also makes less outgoing longwave radiation, traps more longwave radiation within the atmosphere, and then has a stronger longwave-warming effect on the Earth–atmosphere than LBCs at lower altitudes from a view of climate (Slingo and Slingo 1988; Norris et al. 2016). Clouds are known to play a pivotal role in regulating the local surface energy balance. The dominating effect depends strongly on cloud properties such as vertically integrated ice and liquid water contents that determine cloud optical depth and emissivity (Shupe and Intrieri 2004). In addition to directly increasing the surface melting, Van Tricht et al. (2016) found that the CRE can affect the GrIS through a new pathway by which ice clouds and LBCs reduce meltwater refreezing, thereby accelerating bare-ice exposure and enhancing meltwater runoff. This also confirms the high sensitivity of the GrIS to both ice-only and liquid-bearing clouds. Despite their importance, the spatial distribution of ice clouds, LBCs and their impacts on the surface energy balance are poorly understood. There are only a few studies based on short-term observations of clouds in a particular phase state (Bennartz et al. 2013; Neff et al. 2014). Investigating radiative effects and variations in different cloud phases at a long time scale is essential for exploring the surface energy balance on the GrIS.

Due to the remoteness of Greenland, conventional airborne instruments and stations can only observe clouds at short time and limited space scales and cannot study cloud properties at a large spatial scale and for an extended period (Zwally et al. 2002; Lacour et al. 2017; Gruber et al. 2018). In 2006, the National Aeronautics and Space Administration (NASA) and French National Center for Space Studies (Centre National d’Études Spatiales) launched the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Winker et al. 2009) and CloudSat (Stephens et al. 2002) to study clouds and aerosols and perform simultaneous observations with four other satellites in the A-Train. Both satellites measure long-duration, high-resolution vertical profiles of clouds, providing unprecedented data for understanding the vertical distribution and internal structure of polar cloud systems. The use of 2B-Cloud Scenario Classification lidar (2B-CLDCLASS-lidar) and Level-2B Radar–Lidar Fluxes and Heating Rates (2B-FLXHR-lidar) joint satellite products effectively combines the advantages of CloudSat microwave radar and CALIPSO lidar, providing the possibility to investigate cloud phase and cloud radiative effects at large spatial and long temporal scales (Xu et al. 2019; Marchant et al. 2020; Feng and Huang 2021; Wang et al. 2021; McErlich et al. 2021). Previous studies have analyzed the seasonal variation and spatial distribution of clouds and cloud phases (Lacour et al. 2017; Wang et al. 2019). However, few studies have investigated the horizontal and vertical distributions of different cloud phases in Greenland at an interannual scale, along with the spatiotemporal variation of radiative forcing of clouds with different phases.

In the Arctic region, both cloud properties and cloud radiative effects have a more pronounced response to changes in atmospheric circulation patterns (Hofer et al. 2017), such as the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). Wang and Key (2005) found that cloud properties such as cloud optical depth, cloud droplet effective radius, and cloud phase have obvious associations with AO, which further affect the surface radiation balance. Edwards-Opperman et al. (2018), on the other hand, found that the LBCs over Greenland are more sensitive to NAO than PNA and AO. At interannual and even interdecadal scales, the dominant mode of atmospheric circulation variability in the Greenland region is NAO (Dorn et al. 2003; Hanna et al. 2013; Schubert et al. 2020). It represents the seesaw pattern between the Icelandic low pressure and the Azores high pressure areas, which also reflects the strength and location of the North Atlantic westerly jet, and the interannual variation in the NAO index reflects the most important atmospheric circulation changes in the Greenland region (Ulbrich and Christoph 1999; Moore et al. 2015; Robson et al. 2018; Muilwijk et al. 2019; Ballinger et al. 2021). The negative phase of the NAO is positively correlated with the Greenland blocking index (GBI) over the GrIS (Fettweis et al. 2013; Tedesco et al. 2016). The prolonged atmospheric blocking episodes may promote surface melting through the northward advection of warm air over the western GrIS (Fettweis et al. 2011). During the NAO positive events, several obvious features of NAO-related climate variability over the GrIS exist, which include colder near-surface air temperatures, lower sea level pressure, stronger westerly winds over southern Greenland, and reduced precipitation over the western GrIS (Previdi and Veron 2005). Changes in NAO are also closely related to clouds over the GrIS, and negative NAO events are associated with warm and moist air mass advection (Bromwich et al. 1999; Mosley-Thompson et al. 2005). Furthermore, Ruan et al. (2019) showed that NAO positive events increase cloudiness over the GrIS in summer, contributing to a decrease in downward solar shortwave radiation, which leads to a deceleration of GrIS melting. Interestingly, Nygård et al. (2019) argued that increased cloudiness due to the positive phase of NAO substantially increases the downward longwave radiation and revealed a strong link between water vapor, cloud cover, longwave radiation and the atmospheric pressure field. Previous studies have demonstrated a strong link between cloud radiative effects and NAO at a large scale (Trigo et al. 2002; Li et al. 2014). Previdi and Veron (2007) also confirmed that cloud radiative effects over the high-latitude North Atlantic in winter have a sensitive response to NAO and influence the surface air temperature (SAT) by using the polar version of MM5 (PMM5) model simulations. However, further studies are highly demanded to examine the summer cloud radiative effect over the GrIS, especially to explore the physical mechanisms between the spatial distribution changes of clouds and NAO.

In this study, we quantify the three-dimensional occurrence frequency, thermodynamic phase partitioning, and surface radiative impact of clouds over the GrIS. We use composite analysis to explore their response to the NAO index and reveal the physically driven mechanism. Section 2 presents the data and methodology used in this study. Section 3 shows the spatial distribution of clouds, the surface radiative effect of clouds, and the background field of the atmospheric circulation in summer over the GrIS. It also provides the spatial distribution of different cloud phases and CRE variations with the NAO events and explores the potential mechanisms driving the variation. Section 4 summarizes the main findings and provides further discussion.

2. Data and methods

a. Spaceborne observations from NASA A-Train satellites

1) 2B-CLDCLASS-lidar

We use the combined CALIPSO and CloudSat data product 2B-CLDCLASS-lidar from the A-Train satellite to classify the thermodynamic phase of clouds (Sassen and Wang 2008), which is further used to analyze Greenland clouds in both horizontal and vertical directions. The specific parameters used include “cloud phase,” “cloud layer top,” and “cloud layer bottom.” According to Bennartz et al. (2013), the radiative properties of low-altitude liquid clouds and mixed-phase clouds are more consistent with each other and are collectively referred to as LBCs. For this study, LBCs refer to any continuous cloud layers in which CloudSat–CALIPSO identifies liquid or mixed phases in a vertical profile. If more than one cloud phase is identified in several different cloud layers, the type of cloud is also classified as an LBC. The rest of the cases are classified as ice clouds. Note that multilayer clouds could play different cloud radiative forcing effects from single layer clouds (Li et al. 2015; Wang et al. 2021), which are not classified in this study. However, the multilayer clouds in general enhance the surface-warming effect with the same (fixed) optical depth, making our findings even more robust. Considering this, the treatment in this study without classifying single and multilayer clouds should be applicable.

The calculation of the horizontal and vertical cloud frequencies follows the method of Scott et al. (2017) with slight modifications. We divide the clouds into ice clouds and LBCs and divide the study region into several grid points by 1° × 1° in the two-dimensional horizontal direction based on latitude and longitude. Then there are 1975 grids in the study region (55°–80°N, 1°–80°W). The monthly mean cloud frequency in each grid point is calculated as follows:
CFreq(i,j)=1N(i,j)p=1N(i,j)np,cloud(i,j),
where N represents the number of satellite profiles in a 1° × 1° latitude/longitude grid (i, j), and np,cloud = 1 (otherwise = 0) if any kind of cloud phase is present in an atmospheric column. We divide the atmosphere near the Antarctic Peninsula into two dimensions with a vertical resolution of 240 m (the same as the vertical resolution of the data) and a horizontal interval of 1° (1980 grids). Note that we only study the longitudinal cloud distribution from 15° to 60°W in this study, and the latitudinal data from 72° to 76°N are averaged. When CloudSat–CALIPSO observes a cloud layer, we make an increment by unifying all atmospheric volumes containing the cloud (Scott et al. 2017). Then, all grids are normalized by the total number of “n” in each grid cell (all sky).

In addition, the CloudSat had a battery failure in 2011 and was only able to operate in daytime. It means that 2B-CLDCLASS-lidar only has daytime data available after 2011. To ensure data consistency between the time periods 2006–10 and 2011–17, we use daytime (solar zenith angle less than 90°) data only from 2006 to 2017.

2) 2B-FLXHR-lidar

The 2B-FLXHR-lidar product uses cloud liquid and ice phase water content from CALIPSO, CloudSat, and MODIS products, atmospheric state variable values from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and ground reflectance data to derive the atmospheric longwave radiation fluxes and heating rates for each radar profile (Henderson et al. 2013). Upward and downward longwave and shortwave radiative fluxes are calculated over the atmospheric discontinuities, and these fluxes are further used to derive the corresponding heating rates. The specific parameter we used is “BOACRE” in 2B-FLXHR-lidar product, which represents the surface radiative effect of clouds. The impacts of clouds on surface radiation are quantified by calculating the CRE, which is defined as the difference between all-sky with clouds and equivalent clear-sky surface radiative fluxes. We calculate the longwave (LW) and shortwave (SW) CRE using Eqs. (2) and (3):
CRELW=FLW(cloudysky)FLW(clearsky), and
CRESW=FSW(cloudysky)FSW(clearsky),
where FLW and FSW represent the net longwave and shortwave surface radiative fluxes, respectively. When the CLDCLASS data detect a satellite profile that determines any kind of cloud phase and FLXHR provides the surface CRE for the radar profile, the CRE is considered the surface CRE with a cloud-phase frequency of 100%. The longwave, shortwave, and net CREs for different cloud phases are calculated as follows:
CRELW=CREFull-cloud,LW×CFreq,
CRESW=CREFull-cloud,SW×CFreq, and
CRENET=CRELW+CRESW.
In particular, CREFull-cloud is obtained by averaging FLXHR data from 2006 to 2010 (radiative forcing at a 100% cloud fraction) in the grid. In contrast, CFreq is the cloud frequency data from 2006 to 2017. Therefore, the cloud radiative forcing variation we obtained is calculated based on the radiative data from 2006 to 2010 and the cloud frequency data from 2006 to 2017. Note that the NASA CloudSat Data Processing Center only provides 2B-FLXHR-lidar data for the 2006–10 period. To have enough observations in each grid to ensure the accuracy of the cloud frequency and CRE and to maximize the resolution, we gridded the observation data on 1° × 1° before making a multiyear average.

Spaceborne observations from the NASA A-Train satellites were evaluated based on various surface observations. Cloud macrophysical properties agree well with the available ground-based and airborne observations (Protat et al. 2009; Kim et al. 2011; Lacour et al. 2017). Furthermore, the radiative fluxes in the 2B-FLXHR-lidar product were evaluated to have good quality via comparisons with Clouds and the Earth’s Radiant Energy System (CERES) at a global scale (Henderson et al. 2013). The refined 2B-FLXHR-lidar product was also evaluated to have high quality based on ground-based measurements (Blanchard et al. 2021), which were obtained from the Baseline Surface Radiation Network (BSRN).

b. CERES data products

This study utilizes the CERES Energy Balanced and Filled (EBAF) with a spatial resolution of 1° × 1° during summers in the 2006–17 period to examine whether the cloud radiative forcing variation from 2006 to 2010 can be used to represent that from 2011 to 2017. The CERES (Wielicki et al. 1996) EBAF product (Loeb et al. 2009; Kato et al. 2018) was specifically created for use in climate model evaluation and energy budget estimation. It is based on the global 1° × 1° CERES SYN1deg flux product, with modifications that improve its similarity to numerically modeled datasets. We make a further analysis by checking the differences in CRE, cloud fraction and cloud optical depth between the two periods of 2006–10 and 2011–17 using the CERES EBAF product. The scatterplots of summer mean average values of the three variables at 1° × 1° spatial resolution (over 55°–80°N, 1°–80°W) between two periods are shown in Fig. 1. The correlation coefficient of surface net CRE is very high at 0.99 between the two periods, while the correlation coefficients of cloud optical depth and cloud frequency reach 0.97 and 0.96. Overall, the results show that cloud radiative forcing, cloud optical thickness, and cloud fraction have a good agreement in two time periods (2006–10 and 2011–17). Although the CERES EBAF product does not provide the fractions of different phase clouds, making it unable to be used for quantifying CRF from different phases of clouds, we consider the observed cloud radiative effects from 2006 to 2010 to be representative of the cloud radiative effects for the whole period 2006–17, particularly considering that the cloud optical depth and cloud fraction remain consistent between the two periods.

Fig. 1.
Fig. 1.

Scatterplots of summer averaged (a) surface net CRE (W m−2), (b) cloud optical depth, and (c) cloud fraction (%), between the periods of 2006–10 and 2011–17. Each point represents a 1° × 1° latitude–longitude gridbox mean value during the study periods in the study region (55°–80°N, 1°–80°W).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

c. ERA5 reanalysis data

This study utilizes the ERA5 monthly average data with a spatial resolution of 1° × 1° from the ECMWF during summers in the 2006–17 period (Hersbach et al. 2020). ERA5 is the latest global atmospheric reanalysis that replaces ERA-Interim data, it has been well evaluated in the Greenland region (Zhang et al. 2021) and has been widely used in recent studies over Arctic regions (Xia et al. 2021a,b). It provides 18 levels of horizontal wind speed, vertical wind speed, and atmospheric temperature data greater than 300 hPa and consists of the atmospheric circulation background field near Greenland and can be used to investigate the factors driving the variation in cloud-phase patterns with the NAO index. In addition, to match the resolution of ERA5 data with the satellite-based observations, we used 1° × 1° data provided by ERA5 instead of 0.25° × 0.25° data, even though the latter has a higher resolution.

d. North Atlantic Oscillation index

We use the summer monthly average NAO index provided by NOAA’s Climate Prediction Center (http://www.cpc.ncep.noaa.gov/) and perform further processing as follows. First, we detrend and normalize the monthly average NAO index series at a time scale of 2006–17. Then, we select positive NAO months and negative NAO months according to Edwards-Opperman et al. (2018) and perform composite analyses of various variables to investigate the association between NAO and cloud radiative effects over the GrIS.

3. Results

a. Spatial distribution of cloud climatology with different phases in summer

Figure 2 shows the climatological distribution of three different cloud-phase frequencies during the summer (June, July, and August) daytime in Greenland averaged from 2006 to 2017, which indicates that ice clouds and LBCs have different spatial distribution characteristics over Greenland. As shown in Fig. 2a, ice clouds are generally distributed over Greenland with frequencies up to 30%–35%. In contrast, the ice-cloud frequencies over oceanic regions, such as Baffin Bay on the western side and the Greenland Sea on the eastern side, are lower, with values between 5% and 20%. LBCs are distributed over the ocean with frequencies up to 50%–85%, while they are distributed over Greenland with low-frequency values of only 20%–40% (Fig. 2b). Interestingly, the frequency of LBCs reaches the highest in the region west of Summit station (72°36′N, 38°25′W) and the lowest in the region east of Summit station. In terms of total cloud frequency, the eastern side of central Greenland is also slightly different from the western side (Fig. 2c). The frequency of total clouds is lower on the eastern side of Greenland than on the western side. This difference is mainly due to the uneven spatial distribution of LBCs. Overall, the total cloud frequency is lower over Greenland and higher over the ocean. To better investigate the association between NAO positive events and cloud radiative effects in Greenland, we divide the midpart region of Greenland into three regions along 72°–76°N, namely, the western region (region A, 72°–76°N, 45°–55°W), the central region (region B, 72°–76°N, 35°–45°W), and the eastern region (region C, 72°–76°N, 25°–35°W).

Fig. 2.
Fig. 2.

Geographical distribution of the (a) ice-cloud frequency based upon CloudSat–CALIPSO, (b) liquid-bearing cloud frequency, and (c) total cloud frequency for JJA based on 2006–17 averages. The black contours show topography at 500-m intervals (the same hereinafter). An “x” in (b) indicates the location of the Summit station. The midpart of Greenland is divided into region A (72°–76°N, 45°–55°W), region B (72°–76°N, 35°–45°W), and region C (72°–76°N, 25°–35°W) by the dotted line in (c) (the same hereinafter).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Figure 3 illustrates the vertical distribution of clouds with different phases averaged along latitudes 72°–76°N on the GrIS during summer from 2006 to 2017. Ice clouds are mainly distributed at altitudes of 2–10 km, but their frequency is low. As shown in Fig. 3a, the vertical distribution of ice-cloud frequency peaks at 20%, but the distribution is more uniform than that of LBCs over Greenland. In contrast, Fig. 3b shows that LBCs are mainly distributed at low altitudes of 1–5 km, with a maximum occurrence frequency higher than that of ice clouds. The frequency of LBCs is higher in the western and central regions of Greenland than in the eastern region, with a frequency of up to approximately 25%, mainly due to the prevailing westerly winds in Greenland. The topography of Greenland influences the westerly winds prevailing at high latitudes in the Northern Hemisphere, forming an upward movement in the western region. A clear increase in air currents can be seen over the region between 55° and 60°W. As a result, water vapor from the western Greenland oceans is more likely to rise and condense with upward movement. On the other hand, the polar atmosphere is cold enough, even in summer, to be below 0°C, which helps the formation of ice crystals. As seen from Fig. 3b, the uplift motion in western Greenland can reach up to 5 km in altitude; therefore, the high-altitude ice clouds are also affected by the uplift motion, causing the ice-cloud frequency to be also slightly higher on the western side of Greenland than on the eastern side. In terms of total cloud frequency, the distribution of LBCs dominates that of total clouds since the variability of clouds over Greenland is higher for LBCs than for ice clouds. Due to orographic effects, the total cloud frequency is higher in Greenland’s western and central regions than in the eastern region, with partial regions passing the 90% confidence significance test, as shown in Fig. 3c.

Fig. 3.
Fig. 3.

Zonal transects of cloud frequency (based upon CloudSat–CALIPSO) over the GrIS averaged along latitudes 72°–76°N in the summer daytime during the 2006–17 period: (a) ice-cloud frequency (color shading; %); (b) LBC frequency (color shading; %), temperature (contours; °C), vertical (×102), and zonal wind (vectors; m s−1) from ERA5; and (c) total cloud frequency (color shading; %). Gray shading shows the topography (the same hereinafter).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

b. Surface radiative effects of different cloud phases in summer

Figures 46 show the spatial distribution of the surface radiative effect of different cloud phases throughout the Greenland summer daytime averaged from 2006 to 2010. The spatial distribution of the longwave CRE is closely related to the cloud frequency distribution, as shown in Fig. 2. The most substantial longwave radiative effect of ice clouds at the surface is located in the central Greenland region. In contrast, the longwave CRE on the Atlantic Ocean surface is much weaker. In contrast, the surface longwave radiative effect of LBCs is strongest over the oceans because the LBCs are mainly distributed over them. It is worth noting that even though the LBC frequency is low over Greenland, there is still a longwave-warming effect of 25–40 W m−2, which is higher than that of ice clouds. Overall, the longwave CRE in summer shows higher values over the center and lower values over the edges of Greenland, which is mainly related to the cloud frequency distribution.

Fig. 4.
Fig. 4.

The surface LW CRE (W m−2) of (a) ice clouds, (b) LBCs, and (c) total clouds over Greenland calculated from 2B-CLDCLASS-lidar and 2B-FLXHR-lidar for the 2006–10 period.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for the surface SW CRE (W m−2).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Fig. 6.
Fig. 6.

As in Fig. 4, but for the surface net CRE (W m−2).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

As shown in Fig. 5, the high surface albedo of Greenland weakens the shortwave radiative effect of clouds. Compared with the longwave cloud radiative effect, the shortwave cloud radiative effect has a weaker correlation with clouds in spatial distribution. Figure 5a shows that the shortwave radiative effect of ice clouds over the GrIS and the sea area on the eastern side of Greenland are both 5–10 W m−2. From Fig. 2a, it is clear that the frequency of ice clouds over the land of Greenland is clearly higher than that over the sea area, which indicates that the surface albedo and frequency of ice clouds jointly determine the magnitude of the surface shortwave radiative effect of ice clouds. Since both the optical thickness and occurrence frequency of LBCs are higher than those of ice clouds, the shortwave radiative effect of LBCs is stronger than that of ice clouds.

Based on the net surface radiative effect of clouds, as shown in Fig. 6, regardless of the thermodynamic phase of clouds, there is a strong warming effect at the surface in the Greenland region and a cooling effect over the ocean. The cloud radiative effect has good spatial consistency with the cloud frequency distribution over Greenland due to the dominant longwave-warming effect of clouds. Ice clouds warm the ground by approximately 5–10 W m−2 on average over Greenland. The longwave-warming effect of LBCs is more distinct and can reach approximately 10–20 W m−2, with peak values located in the western central region. Note that the strong longwave-warming effect over the western central region is also caused by the elevated cloud frequency due to topographic uplift. In terms of the total net surface radiative effect, clouds have a strong warming effect on Greenland during the daytime in summer and play an important role in the energy balance of Greenland’s surface. Actually, in the summer evening or during the other three seasons, the shortwave cooling effect of clouds on the surface is further weakened or even disappears and the clouds may then have an even stronger net warming effect on the surface.

c. NAO event synthesis analysis

We use the monthly average NAO index in summer provided by the Climate Prediction Center (CPC) and detrend the index for the study period from 2006 to 2017 (Fig. 7). The index used is normalized over an 11-yr time series according to the method used by Edwards-Opperman et al. (2018). To better investigate the variability of cloud radiative effects during NAO and make the results more distinct, the summer NAO months whose index is greater (less) than 1 (−1) are referred as positive(negative) NAO events. Based on this, we compare the differences of various variables between the NAO positive and negative events by averaging them to derive the potential relationship between the NAO and cloud frequency and cloud radiative effects. In addition, to constrain the effect of seasonal variation of each variable between June, July, and August on the results, the variables subtracted in the composite analysis are all anomalies of the variables.

Fig. 7.
Fig. 7.

Temporal variation of monthly NAO index during summers (June–August) from 2006 to 2017.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Figure 8 shows the spatial distribution of differences in the frequencies of different cloud phases between positive and negative NAO events. As shown in Fig. 8a, the ice-cloud frequency increases in regions A and B but changes little in region C from negative to positive NAO events. The LBC frequency increases in region A and decreases in regions B and C from negative to positive NAO events (Fig. 8b), with partial regions passing the 90% confidence significance test. As a whole, the variation in cloud frequency within regions A, B, and C is more distinct compared to other regions in Greenland. For the differences in cloud frequencies between positive and negative NAO events, both ice clouds and LBCs in region A show an increasing trend, those in region C show a decreasing trend, while those in region B show little change in total cloud frequency due to the increasing ice clouds and decreasing LBCs.

Fig. 8.
Fig. 8.

The difference in (a) ice-cloud frequency, (b) liquid-bearing cloud frequency, and (c) total cloud frequency between positive NAO events and negative NAO events. The regions with dots are those passing the 90% confidence level of significance.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

To better understand the difference in clouds and their physically driven mechanisms over the GrIS, we investigate the difference between positive and negative NAO events for clouds averaged along latitudes 72°–76°N by composite analysis. Figure 9 shows that the spatial distributions of ice clouds and LBCs are slightly different in the three regions. In region A, the frequencies of both ice clouds and LBCs increase obviously because there is a westerly wind enhancement. The airflow in region A rises with the topography with a height up to 6 km, which results in a clear increase in the frequencies of both ice clouds and LBCs. Although there is no obvious updraft or downdraft in region B, the frequency of ice clouds increases, while the frequency of LBCs decreases because the temperature decreases and supercooled liquid water droplets are more likely to form ice crystals. Due to the strengthening of westerly winds, evident downdrafts occur in region C, thus causing a decrease in the frequency of LBCs, consistent with that shown in Fig. 8b.

Fig. 9.
Fig. 9.

The difference in cloud frequency, temperature, and vertical and zonal wind along latitudes 72°–76°N in summer daytime between the positive and negative NAO events: (a) ice-cloud frequency (color shading; %); (b) LBC frequency (color shading; %), temperature (contours; °C), vertical (×102), and zonal wind (vectors; m s−1); and (c) total cloud frequency (color shading; %). The regions with dots are those passing the 90% confidence level of significance (only for cloud frequency).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Figure 10 shows the difference in the surface cloud radiative effect in summer between positive and negative NAO events. The frequency and radiative effect of ice clouds and LBCs in regions A, B, and C are quantified, with the results shown in Table 1. Figures 46 have already shown that both ice clouds and LBCs have a warming effect on the surface during the summer daytime over the GrIS, while their magnitudes differ. Compared to negative NAO events, positive NAO events are associated with stronger westerly winds and lower temperatures in Greenland’s A and B regions, the increase in ice clouds in Greenland’s A and B regions increases the surface-warming effect of clouds and increases the radiation received by the surface. In contrast, the changes in net surface CRE of ice clouds over region C are not significant. LBCs tend to increase in region A, resulting in an increase in net radiation at the surface, while the decreases in LBCs in regions B and C reduce warming. Interestingly, the total cloud frequency in region B does not change much (−1.79% in Table 1) between the positive and negative NAO events, while the net cloud radiative effect in region B has a clear decrease. This may be due to the different surface CREs between ice clouds and LBCs. Even though the ice-cloud frequency in region B increases and warms the surface, this warming trend is not enough to compensate for the reduction in radiation caused by the decrease in LBC frequency. As a result, the net cloud radiative forcing in region B is smaller during positive NAO events than during negative NAO events. Quantitatively, the total cloud frequency in region B decreases by 1.79%, while the net surface cloud radiative effect changes by −2.05 W m−2; in region C, the total cloud frequency decreases by 5.94%, while the net surface cloud radiative effect only changes by −1.34 W m−2. The difference in cloud radiative forcing during NAO could lead to a clear difference in Greenland surface temperature. Following the method used by Garrett and Zhao (2006), we roughly estimate the surface temperature differences caused by the CRE differences. For the three regions, the surface temperature could increase by about 0.6 K in region A due to the variation of cloud radiative effect between the positive and negative NAO events, while the surface temperature decreases by about 0.6 and 0.4 K in region B and C, respectively. These quantitative results also illustrate the importance of examining cloud radiative effects by phase. Even though the total cloud frequency does not change much, the radiative effects of clouds on the surface could change considerably due to changes in the vertical structure and phase of clouds, thus further leading to changes in surface temperature.

Fig. 10.
Fig. 10.

The difference in the average surface net CRE (W m−2) of (a) ice clouds, (b) LBCs, and (c) total clouds over Greenland calculated from 2B-CLDCLASS-lidar and 2B-FLXHR-lidar between positive and negative NAO events in summer.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0718.1

Table 1

Average cloud frequency (%), CRE (W m−2), and their changes between positive NAO events and negative NAO events in three regions in summer from 2006 to 2017.

Table 1

4. Summary and discussion

Due to the remote location and harsh environment, the understanding of cloud-phase patterns, radiative effects, and response to large-scale circulation background fields over Greenland are still limited. Combined active spaceborne data allow for a long-term and large-scale analysis of cloud properties and variability over Greenland, improving the understanding of cloud properties and processes in the study region (72°–76°N, 25°–55°W) and their effects on the surface energy balance. Moreover, we analyzed the effect of different cloud phases on the surface energy balance during NAO events and explored the physical mechanisms. Our main findings are as follows:

Ice clouds are mainly distributed over Greenland, with daytime frequencies up to 30% in summer. LBCs are mainly distributed over the oceans, but the occurrence frequency of LBCs over the GrIS is still higher than that of ice clouds. The distribution of LBC frequency over the GrIS shows a spatial trend of high in the middle and low in the edges, with the highest frequency, 50%, to the west of the Summit station (72°36′N, 38°25′W). Since LBCs dominate in the study region, the total cloud frequency spatial distribution is similar to that of the LBCs.

By analyzing the vertical distribution of different cloud phases at 72°–76°N over the GrIS, we see that ice clouds are mainly distributed at high altitudes between 2 and 10 km with a peak frequency of 20%, and LBCs are mainly distributed at low altitudes between 1 and 5 km with a peak frequency of 25%. The prevailing westerly winds at middle and high latitudes rise to the west of the Summit station in Greenland, and water vapor condenses with the uplift movement; therefore, the frequency of ice clouds and LBCs is higher in the western and central regions of the GrIS, while in the eastern regions, the cloud frequency is lower due to sinking movement. Topographic forcing is an essential factor affecting cloud distribution in Greenland.

Because of Greenland’s extremely high surface albedo, the surface shortwave cloud radiative forcing during the summer daytime is limited. The cloud longwave radiative effect at the surface is closely related to the distribution of cloud frequencies. The region with the strongest longwave radiation is located west of the Summit station in central Greenland, where the cloud frequency is the largest due to the westerly orographic effect. Since the frequency of ice clouds is lower than that of LBCs, both the longwave and shortwave radiative effects of ice clouds at the surface are lower than those of LBCs. However, both ice clouds and LBCs have a strong warming effect on the Greenland surface.

Compared to the negative NAO events, Greenland experiences a general decrease in temperature and an increase in westerly winds during the positive NAO events. As a result, the uplift flow in western Greenland is further enhanced, and the frequency of ice clouds and LBCs increases. Both uplift and sinking airflow in the central region are not obvious, and the total cloud frequency does not change much. However, due to the lower temperature, the liquid droplets in the clouds are more easily converted into ice crystals. Hence, the frequency of ice clouds increases, while the frequency of LBCs decreases. The sinking airflow on the eastern side is obvious, and the frequencies of ice clouds and LBCs are obviously reduced.

The differences in cloud frequency and CRE between the positive and negative NAO events vary considerably in different regions of Greenland, resulting in a change in the regional surface radiation balance. In the western region of Greenland, both ice clouds and LBCs increase in frequency, thus leading to an increase in net surface radiation and a surface warming of 2.07 W m−2. There is little change in ice clouds in the eastern region and an obvious decrease in LBCs, leading to a decrease (cooling) in net surface radiation of 1.34 W m−2. The central region is a special area because the total cloud frequency does not change much due to the increase in ice clouds and the decrease in LBCs. However, the surface-warming effect due to the increase in ice clouds is smaller than the cooling effect due to the decrease in LBCs; therefore, the overall cloud radiative forcing in the central region during the NAO positive phase is still cooling compared to that during the NAO negative phase, reaching −2.05 W m−2.

The findings above suggest that long-term, large-scale active remote sensing satellite observations can contribute to our understanding of regional cloud properties and their radiative effects at the surface in Greenland. However, there are still uncertainties in data processing and statistical methods, especially in the diurnal sampling of the data and in the computational errors of the vertical radiative fluxes, suggesting the need for further research.

As global warming continues to increase, exploring the link between NAO and global warming has become one of the current research hotspots. Hurrell (1995) proposed that the major warming of the Northern Hemisphere (NH) continents is largely due to the intensity and duration of NAO during its positive phase. Hurrell (1996) further showed that almost all observed Eurasian warming since the mid-1970s is directly attributed to interannual trends in NAO. Differently, there are also studies (e.g., Cohen and Barlow 2005) indicating that the pattern and magnitude of global warming trends over the last 30 years are independent of NAO. As Greenland is one of the most obvious high-latitude regions with clear climate change in the Northern Hemisphere (Alexander et al. 2019; Sherman et al. 2020), it is important to study the relationship between NAO and CRE as well as surface temperature changes in Greenland. In the future, we will investigate the contribution of CRE to polar melting in different seasons and on different time scales.

Acknowledgments.

This work is supported by the National Natural Science Foundation of China (Grant 41925022) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19070202).

Data availability statement.

The data used in this study were obtained from the NASA CloudSat Data Processing Center (2B-CLDCLASS-LIDAR and 2B-FLXHR-LIDAR), the ECMWF data server (ERA5 reanalysis data), and the NOAA Climate Prediction Center (NAO index). The authors declare no conflicts of interest.

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