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Article

Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies

by
Yuqi Sun
,
Yetang Wang
*,
Zhaosheng Zhai
and
Min Zhou
College of Geography and Environment, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(20), 4940; https://doi.org/10.3390/rs15204940
Submission received: 26 July 2023 / Revised: 6 October 2023 / Accepted: 10 October 2023 / Published: 12 October 2023
(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)
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Abstract

:
In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then used to investigate spatial and temporal trends in the summer albedo over the Antarctic from 1982 to 2018, along with their association with Antarctic sea ice changes. The SAL product matches well surface albedo observations from eight stations, suggesting its robust performance in Antarctica. Summer surface albedo averaged over the entire ice sheet shows a downward trend since 1982, albeit not statistically significant. In contrast, a significant upward trend is observed in the sea ice region. Spatially, for ice sheet surface albedo, positive trends occur in the eastern Antarctica Peninsula and the margins of East Antarctica, whereas other regions exhibit negative trends, most prominently in the Ross and Ronne ice shelves. For sea ice albedo, positive trends are observed in the Ross Sea and the Weddell Sea, but negative trends are observed in the Bellingshausen and the Amundsen Seas. Between 2016 and 2018, an unusual decrease in the sea ice extent significantly affected both sea ice and Antarctic ice sheet (AIS) surface albedo changes. However, for the 1982–2015 period, while the effect of sea ice on its own albedo is significant, its impact on ice sheet albedo is less apparent. Air temperature and snow depth also contribute much to sea ice albedo changes. However, on ice sheet surface albedo, the influence of temperature and snow accumulation appears limited.

Graphical Abstract

1. Introduction

The polar regions are pivotal in global climate change due to the amplification of warming signals by the positive snow and ice albedo feedback (SIAF). Current studies on surface albedo in polar regions are primarily focused on the Arctic, with fewer dedicated to Antarctica. As the Antarctic Ice Sheet (AIS) is almost entirely covered by high-albedo snow and ice [1], its surface can reflect most of the solar radiation back to the atmosphere and space [2,3]. As a result, even minor changes in surface albedo can profoundly impact the ice sheet surface energy budget. Furthermore, a reduction in albedo enhances the absorption of solar radiation [4], supplying more energy to accelerate snow melting, contributing to the ice sheet mass loss, and subsequently increasing sea level rise. If melted completely, the Antarctic Ice Sheet (AIS) would raise global sea level by about 58 m [5]. Therefore, it is essential to monitor long-term changes in the Antarctic surface albedo to understand the ice sheet surface processes and to estimate regional and even global climate changes.
Surface albedo variations are influenced by multiple factors. Snow albedo tends to be high in a dry and cold environment [6]. However, snow property changes, snowfall, blowing snow, or the accumulation of light-absorbing impurities [7,8] can significantly alter surface albedo [9]. Moreover, sea ice extent changes may affect the surface snow properties by increasing or decreasing the poleward moisture and heat transport from the ocean surface, which can cause changes in snow accumulation or ablation on the ice sheet [10,11,12] and thereby result in alterations in surface albedo. Several studies have shown a slight increase in the total Antarctic sea ice extent from 1979 to 2015 [13,14,15], opposite to the substantial decline of Arctic sea ice during the same period [16,17,18]. In the Arctic, where sea ice is believed to provide positive SIAF, melting reduces sea ice surface albedo and thereby increases the absorption of solar radiation, which in turn accelerates sea ice melting [19]. In contrast, the Antarctic sea ice shows a negative SIAF, and the combined annual mean SIAF in the Arctic and Antarctica between 1992 and 2015 was 0.08 W/m2 [20]. However, satellite records show a sharp decline in Antarctic sea ice extent after 2015, reaching a record low in 2022, with a total sea ice extent of 10.6 × 106 km2 [21]; this caused a rapid increase in the SIAF, with a value of 0.26 W/m2 between 2016 and 2018, highlighting the importance of Antarctic sea ice loss to global SIAF [20]. Thus, it is essential to examine Antarctic surface albedo changes and their association with the anomalies of Antarctic sea ice.
Satellite-based observations, including Synthetic aperture radar (SAR) images and optical remote sensing data, are increasingly used to study various cryospheric elements, such as investigating permafrost changes [22], mapping snow cover and the ice crystal structure [23,24,25], and so on. To address the sparseness of in situ surface albedo observations over Antarctica, satellite sensors, such as the Advanced Very High-Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), offer the capability to continuously monitor surface albedo changes over the AIS. For example, the daily MODIS snow albedo product (MOD10A1, MYD10A1, and MCD43) was used by Corbea-Pérez et al. [26] to compare in situ albedo under both all-sky and clear-sky conditions on Livingston Island in the South Shetland Islands (SSI) between 2006 and 2015. Calleja et al. [27] examined the seasonality and trends of snow albedo over this region during the same period using the MOD10A1 product and in situ albedo. Despite the relatively high resolution of the MODIS albedo product, its availability (only from the year 2000 onwards) limits its potential to characterize long-term surface albedo variations. In contrast, AVHRR albedo products cover longer time spans, and recent calibration of its sensor family has enhanced the ability to detect surface albedo trends [28,29]. Laine [9] used calibrated surface albedo provided by the AVHRR Polar Pathfinder data to analyze surface albedo changes over the AIS and Antarctic sea ice region during spring–summer from 1981 to 2000. Using the CM SAF Cloud, Albedo, and Surface Radiation from AVHRR dataset first edition (CLARA-A1) Surface Albedo (SAL), Seo et al. [30] investigated spatial and temporal changes in the AIS surface albedo between 1983–2009 and its relationship with climate variables. Despite these efforts, long-term changes in surface albedo over Antarctica and its relationship with sea ice variability, especially the recent sharp sea ice decline, are still not being fully surveyed.
In this study, the performance of the CLARA-A2.1-SAL product is first examined using in situ surface albedo observations. Then, based on this albedo product, we investigate temporal and spatial variability in summer albedo over Antarctic continental and sea ice surface during the 1982–2018 period and their association with sea ice anomalies. Lastly, we explore the potential impacts of other factors, such as air temperature, snow cover accumulation, and surface ablation days, on variability in the AIS surface and sea ice albedo.

2. Data and Methods

2.1. CLARA-A2.1-SAL Product

The satellite-observed surface albedo used in this study is the CLARA-A2.1-SAL (CLARA dataset, AVHRR edition 2.1, Surface albedo) product, which is produced by the Climate Monitoring Satellite Application Facility project of the European Organization for the Exploitation of Meteorological Satellites. In addition to surface albedo, the CLARA-A2.1 dataset contains the data of cloud and surface radiation parameters [31], and they are all derived from the AVHRR sensor aboard polar-orbiting National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites [32]. The CLARA-A2.1-SAL dataset is an updated version of the original CLARA-A1-SAL dataset [33], with improvements in aerosol background treatment, land cover evolution tracking, ocean wind impacts on ocean albedo, and algorithm enhancements [32]. This dataset provides both pentad (five days) and monthly mean surface albedo from January 1982 to June 2019. Its global gridded data has a resolution of 0.25°, and there is a specialized subset for the polar regions in equal area projection at a 25 km resolution.
The albedo product is generated by means of the CLARA-SAL retrieval algorithm, which corrects topographic and atmospheric effects by converting top-of-atmosphere (TOA) reflectances from radiance and temperature data of AVHRR channels 1 and 2 [33]. Spectral surface reflectances are then transformed to shortwave broadband surface albedo using different algorithms based on the land cover at each pixel [34,35,36]. The resulting black sky surface albedo data, with a wavelength range of 0.25–2.5 μm for AVHRR, synthesizes radiation reflected from a single incidence direction to all observed directions. Unlike the blue sky albedo, it assumes extreme atmospheric conditions [30]. According to the Global Climate Observing System, the black sky surface albedo is an ideal choice for the estimation of climate changes [37]. The SAL product contains a number of valid observations in each grid cell [32]. According to reference [32], we only use the pentad mean and monthly mean albedo at grid cells with at least 5 and 20 observations, respectively.
The quality of the CLARA-A2.1-SAL product has been validated against limited in situ observations from the Baseline Surface Radiation Network (BSRN), Greenland Climate Network (GC-Net), the Arctic Research Center of the Finnish Meteorological Institute (FMI), and the MODIS albedo product. Validation results demonstrate that CLARA-A2 SAL has a typical accuracy of 3–15% over snow and ice, with less than 5% differences with MCD43C3 products [31]. These results underscore the high quality of SAL retrievals over snow and ice, making them suitable for cryospheric studies [32]. However, among these stations, only three are located in Antarctica, highlighting the need for further performance estimation of the CLARA-A2.1-SAL product in the Antarctic continent using more station observations.
The fractional cloud cover (CFC) also originates from the CLARA-A2.1 family. Summer CFC over the Antarctic sea ice zone is deemed accurate enough to examine its effect on sea ice albedo [32].

2.2. In Situ Surface Albedo Observations

We validated the CLARA-A2.1-SAL product in Antarctica using in situ surface albedo observations from fourteen automatic weather stations (AWSs) and four BSRN stations, which are shown in Figure 1. The AWS meteorological observations are derived from the Institute for Marine and Atmospheric Research, Utrecht University (IMAU) Antarctic AWS Project, which includes hourly observed variables (temperature, wind speed, humidity, pressure, and radiation) and derived variables (turbulent fluxes, ground heat flux, snow temperature, and density and melt rates) [38]. The BSRN, a radiometric network initiated by the World Climate Research Program (WCRP), aims to detect long-term changes in radiation at the Earth’s surface [39]. It collects and archives high-quality ground radiation measurements at a one-minute resolution [40].
The downward and upward shortwave radiation fluxes measured by AWSs over Antarctica have an uncertainty of less than 5% [41]. The hourly radiation data are retained when the downward shortwave radiation exceeds the upward shortwave. The daily surface albedo is then calculated by the ratio of the upward and downward shortwave radiation at the corresponding time. If more than 21% (5 h) of hourly values are missing within 1 day, the daily average is considered to be a missing value [42]. The daily averages are then processed into pentad mean albedo and monthly mean albedo. The uncertainty of downward and upward shortwave radiation data from the BSRN is less than 2% [43].

2.3. Sea Ice Indexes

Sea ice indexes used in this study include sea ice concentration (SIC) and sea ice extent (SIE), defined as the percentage of area covered by sea ice and the regions with SIC of at least 15%, respectively [44,45]. Monthly summer SIE data spanning 37 years (1982–2018) come from the Sea Ice Index (Version 3) of the National Snow and Ice Data Center (NSIDC). Monthly summer SIC fields for the period 1982–2018 are obtained from NOAA/NSIDC Climate Data Record (CDR) of Passive Microwave Sea Ice Concentration (Version 4) at a spatial resolution of 25 km in polar stereographic projection. These indexes are produced by the brightness temperature data obtained by the Scanning Multichannel Microwave Radiometer (SMMR) aboard the Nimbus-7 satellite and the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP), through correction, inversion and other processes [46]. Due to the difficulty in distinguishing between open water and ice water, the uncertainty of SIC in austral summer can be up to 10–20% in open water, but it can reduce as SIC increases. Thus, we only retain grid cells with SIC values of more than 15% to minimize the uncertainty [21,47]. The detailed description of SIC and SIE and their accuracy can be found in Fetterer et al. [21] and Meier et al. [47], respectively.

2.4. Surface Melting Days

Surface melt days are derived from a 42-year Antarctic daily surface melting dataset developed by Picard and Fily [48]. This dataset is generated using the passive microwave radiation data retrieved from SMMR and SSM/I radiometers by a melting detection algorithm [48,49]. SMMR recorded antenna temperature every two days before 1988 [50], while SSM/I recorded daily brightness temperature from 1988 [51]. This melting dataset provides the daily status of dry and wet snow at each grid pixel within the range, which is used to detect the days and regions where melting occurred. They are determined by the different dielectric constants of dry and wet snow, distinguished by the variation of upwelling microwave brightness temperature [52]. This dataset spans from April 1979 to March 2021 and has a spatial resolution of 25 km with a stereographic polar grid.

2.5. ERA5

ERA5 is the fifth-generation reanalysis of past and present global climate and weather produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which covers from 1940 to the present and will be extended forward in near real-time [53]. It is considered to be reliable for year-to-year variation of Antarctic air temperature and precipitation [54,55]. ERA5 also robustly captures the temporal variability in near-surface air temperature over the Antarctic sea ice zone [56]. Here, we use ERA5 monthly averaged data, including 2 m air temperature, precipitation, and evaporation in the austral summer at a spatial resolution of 0.25° between 1982 and 2018. Precipitation minus evaporation (P-E) is approximated as snow accumulation on the AIS.

2.6. Snow Depth over the Antarctic Sea Ice

Snow cover on the surface of sea ice regulates the energy budget, affects the growth and melting of sea ice, and plays a crucial role in climate dynamics. The snow depth dataset, including snow depth and its corresponding uncertainty, comes from the National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, spanning from 2002 to 2020 on a daily scale [57]. Accuracy estimation indicates that this dataset has a mean bias (MB) of 5.6 cm and a root mean square error (RMSE) of 13.8 cm [57]. This product can be further used in the validation of reanalysis products and climate models, calculation of sea ice thickness, and other applications [57]. In this study, we use the austral summer snow depth for the 2002–2018 period.

2.7. Validation and Trend Calculation

To validate the monthly mean and pentad mean satellite albedo products, we calculated in situ surface albedo as the monthly mean and pentad mean. We first obtained valid daily mean surface albedo. Then, when at least four days of valid values were available, they were averaged as a pentad mean. When at least 20 days of valid values are available per month, the monthly mean albedo was calculated. We employed the RMSE and MB to quantify the accuracy of the satellite albedo product.
RMSE = ∑ i = 1 n y i − t i 2 n
MB = ∑ i = 1 n y i − t i n
where n ,   y i , and t i represent the number of corresponding valid values between product and observation, the CLARA-A2.1-SAL values, and in situ surface albedo, respectively.
Our research only focuses on spatiotemporal variability of Antarctic surface albedo in austral summer (December to the following February) from 1982 to 2018 because of the unavailability of surface albedo observations at the polar night days. To match the albedo dataset in time, other datasets are selected only during summer. A least-squares linear regression was used to calculate the linear trends, and Pearson linear correlation coefficients were used to quantify the relationship between the ice sheet albedo and its potential affecting factors. The statistical significance of the trends and correlations was calculated by means of a two-tailed t-test. The flowchart of this study is shown in Figure 2.

3. Results

3.1. Validation of the CLARA-2.1-SAL Product

The monthly mean and pentad mean of summer mean albedo in CLARA-A2.1-SAL products from 1982 to 2018 (including all the time spans of stations) are verified by corresponding in situ measurements. The short time spans of in situ observations (from 1992 onwards) have resulted in a smaller number of effective matching values, which refers to the albedo values of the product and in situ measurements taken at the same time, at certain station locations (as shown in Table 1). is fewer at some station locations (Table 1). Thus, our analysis primarily focuses on eight stations (the orange points in Figure 1 and the bold fonts in Table 1) with at least 20 effective matching values. The RMSE and MB of these eight stations are shown in Figure 3, and the validation results are displayed in Figure 4 and Figure 5.
The CLARA-2.1-SAL product performs well in Antarctica. For the eight stations, the RMSE values between the two datasets are all below 0.1 (ranging from 0.025 to 0.099) for both monthly and pentad mean, and their MB values range from −0.077 to 0.055 (Figure 3). The albedo values of the product consistently exceed 0.5 at these stations and remain relatively stable over time (Figure 4 and Figure 5). Specifically, the CLARA-A2.1-SAL product slightly underestimates albedo at the three stations of AWS05, AWS06, and GVN (Neumayer Station), with MB values ranging from −0.08 to −0.05 and RMSE ranging from 0.05 to 0.1. By contrast, it overestimates albedo at AWS12, AWS13, SPO (South Pole), and SYO (Syowa), with the MB values ranging from 0.01 to 0.06 and RMSE ranging from 0.02 to 0.1. The bias between the two albedo datasets is minimum at AWS09 and maximum at GVN. Spatially, this product tends to underestimate surface albedo at the coastal stations, except for SYO stations, and overestimate at the inland stations. In addition, MB and RMSE values are larger at coastal stations than inland ones, which may be related to the fact that the product pixels covering coastal stations consist of multiple surface types (such as ice, water, and rock), while the stations represent only a single surface type. Despite these discrepancies, the SAL product robustly captures the magnitude and trend of long series albedo at each station, confirming its reliability in Antarctica.

3.2. Temporal and Spatial Trends of Surface Albedo over Antarctica

The temporal and spatial trends and standard deviation of summer mean albedo over the AIS and Antarctic sea ice zone for the period 1982–2018 are shown in Figure 6 and Figure 7, respectively. During these 37 years, the summer mean albedo of the AIS remained above 0.77 and showed a slight decreasing trend with a rate of 0.003 per decade (p < 0.1, Figure 6a). The albedo decreased at the same rate between 1982 and 2015 but was not statistically significant (p > 0.1). The average of AIS surface albedo from 2016 to 2018 decreased by nearly 0.01, compared to the 1982–2015 mean. Summer mean sea ice albedo remained between 0.2 and 0.3 between 1982 and 2018, which is lower than that of the AIS. It shows a significantly increasing trend at a rate of 0.006 per decade (p < 0.05, Figure 6b). By comparison, sea ice albedo increased at a larger rate of 0.01 per decade (p < 0.05) between 1982 and 2015. These suggest that the dramatic decline in sea ice from 2016 onwards has strongly affected the albedo trends in the sea ice region.
Spatially, large heterogeneity in surface albedo trends is observed over the AIS. The surface albedo shows negative trends over most of the AIS (Figure 7a), especially in the Ross ice shelf and Ronne ice shelf, where the trends are statistically significant (p < 0.05). On the contrary, significant positive trends (p < 0.05) are found in the eastern Antarctic Peninsula and the margins of East Antarctica, especially the area around the Amery ice shelf. There are positive trends in the Ross Sea and the Weddell Sea but negative trends in the Bellingshausen Sea and the Amundsen Sea. Compared with the AIS albedo, sea ice albedo trends are generally larger in magnitude, and their spatial heterogeneity is more prominent (Figure 7b). The standard deviation of the albedo time series is relatively low in the East Antarctic Plateau (Figure 7c), suggesting small year-to-year variations of surface albedo. However, it is significantly higher than 0.02 around the Ross ice shelf, Ronne ice shelf, Amery ice shelf, and the Antarctic Peninsula, indicating larger interannual variability in surface albedo in these regions. For sea ice regions, the standard deviation shows that the interannual variability of albedo is larger in the sea ice near the ice sheet but smaller away from the ice sheet (Figure 7d).

3.3. Impact of Sea Ice of the Different Southern Ocean Sectors on Albedo of Antarctic Sea Ice and Ice Sheet Surface

Following Brandt et al. [58], we divided the Southern Ocean around the AIS into five sectors (Figure 1) due to the differences in sea ice variations among them. We only examined the relationship between surface albedo over the AIS and sea ice area, and SIE of the Ross Sea, the Bellingshausen Sea, the Amundsen Sea, and the Weddell Sea between 1982 and 2015 and 1982 and 2018 (Figure 8), as these regions experienced considerable sea ice changes. The spatial distribution of the correlations between sea ice and surface albedo between 1982 and 2015 closely resembled that between 1982 and 2018 (Figure 8). Notable positive and significant correlations exist between the SIE in each oceanic sector and its corresponding sea ice albedo, with R values above 0.45; this shows that a decrease in sea ice albedo corresponds to a reduction in SIE and vice versa. However, the correlations between the SIE in each sea sector and AIS albedo are not as robust, with absolute R values of around 0.3. Notably, sea ice in both the Ross Sea and the Weddell Sea negatively correlates with surface albedo across most of the AIS but positively correlates in parts of East Antarctica. Sea ice in the Bellingshausen and Amundsen Sea sectors has a positive correlation with the albedo across the majority of Antarctica. For the surface albedo averaged over the entire ice sheet, an insignificant downward trend was found between 1982 and 2015, in contrast to the not significant upward trend of the SIE.
To assess the impact of a sharp decline in Antarctic sea ice starting in 2016 on surface albedo, we investigated the anomalies of both AIS and sea ice albedo and SIC for the consecutive three-year periods of 2013–2015 and 2016–2018, relative to the baseline period 1982–2011 (Figure 9). The patterns demonstrated a clear shift from positive to negative anomalies of sea ice albedo, in accordance with the switch of SIC anomalies. Between 2013 and 2015, positive SIC anomalies in most of the sea ice region corresponded with the dominant positive sea ice albedo. Between 2016 and 2018, both SIC and albedo showed notably strong positive anomalies in the Bellingshausen and Amundsen Sea sectors and the western Weddell Sea but negative ones in the Ross Sea and most of the sea ice zone over the South Atlantic. Negative anomalies of surface albedo dominated the AIS between 2013 and 2015, opposite to the dominant positive SIC anomalies. As the reversal of SIC anomalies occurred between 2016 and 2018, AIS albedo exhibited stronger negative anomalies than those between 2013 and 2015.

3.4. Factors Excluding Sea Ice Influencing Albedo of Antarctic Sea Ice and Ice Sheet Surface

As described above, the albedo of Antarctic sea ice is primarily influenced by the SIC/SIE. We further explored the relationship between sea ice albedo and other parameters, such as air temperature, snow depth, and CFC. To achieve this, we undertook a pixel-by-pixel correlation analysis between albedo and these factors within the sea ice zone during austral summer spanning from 1982 to 2018 (Figure 10). The sea ice albedo and air temperature were inversely correlated, evidenced by R values in most sea ice zone pixels falling below −0.6 (see Figure 10a); this indicates that the reduction of sea ice albedo corresponds to an increase in air temperature and vice versa. Conversely, significant and positive correlations between sea ice albedo and snow depth were dominant (Figure 10b); this is consistent with the understanding that deeper snow-covered sea ice has a higher albedo, whereas ice lacking snow cover tends to have a lower albedo [59]. We also investigated the effect of CFC on sea ice albedo (Figure 10c). Significantly negative correlations only occurred in the very small part of the sea ice zone at the Ross Sea, the Bellingshausen Sea, and the Weddell Sea.
Taking a cue from our analysis of sea ice albedo, we investigated the potential impacts of surface ablation days, air temperature, and snow accumulation on the AIS surface albedo. Correlation coefficients between summer surface melting days and the surface albedo at corresponding pixels within the AIS ablation zone (altitude below 1700 m, Figure 11a) were calculated. Almost all edges of the AIS exhibited strong negative correlations, marked by R values of less than −0.6 (p < 0.05). However, the correlations were positive in parts of the Ross and Ronne Ice Shelves. As noted by Scott et al. [60], the surface of these ice shelves undergoes less melting during the austral summer. It is notable that relatively few samplings of melting days make it challenging to determine their impact on surface albedo over the Ross Ice Shelf. Overall, variations of surface albedo are closely related to surface melting. Surface snow properties change as surface melting increases, which leads to a decline in surface albedo, further accelerating the melting process [61]. Air temperature correlates negatively with surface albedo on most ice sheet margins, especially on the ice shelves. The significant negative correlations are also present across most inland East Antarctica. However, these correlations are weak in general (Figure 11b), implying minimal temperature influence on surface albedo. Likewise, a weak positive correlation exists between snow accumulation and surface albedo (Figure 11c). Although increased snow accumulation can bring more fresh snow with relatively high albedo to the ice sheet surface, thus, to some extent, increasing the surface albedo, wind-driven snow redistribution may interfere with this process [62].

4. Discussion

In this study, the CLARA-A2.1-SAL albedo product was validated by eight in situ station observations between 1992 and 2018. Both the RMSE and MB of the product are within 0.1, which is similar to the previous estimations in the references [3,63]; this suggests that the CLARA-A2.1-SAL product is reliable for the investigation of long-term changes in the AIS and sea ice albedo. Our findings on the spatial pattern of sea ice albedo trends are similar to those presented by Shao et al. [64]. However, there are discrepancies in the spatial distribution of albedo trends on the AIS compared to the CLARA-A1 results of Seo et al. [30]. The CLARA-A1 surface albedo product shows positive trends across most of East Antarctica, whereas negative trends are still dominant for the CLARA-A2.1 product. This difference could stem from the distinct product versions used. Compared with the CLARA-A1, the CLARA-A2.1 surface albedo has been improved in many aspects (See the description in the CLARA-A2.1-SAL product section) [32]. In particular, homogenization of visible calibration and inter-calibration is performed in the CLARA-2.1 [31], which makes this product more stable and, thus, more suitable for the investigation of temporal changes and trends. The different timespans when the trends are calculated are a possible factor. Our results focus on the 1982–2018 period, but Seo et al. [30] examined the trends between 1983 and 2009.
There was a strong positive correlation between sea ice albedo and SIE in various sea sectors, especially in the Bellingshausen and Amundsen Sea and the Weddell Sea from 1982 to 2018 (Figure 8); this shows that as SIE diminishes, so does the sea ice albedo and vice versa. The albedo over AIS was less affected by the slight expansion of the Antarctic sea ice before 2015, but the effect of the anomalous decline in sea ice between 2016 and 2018 is not ignored. The substantial sea ice losses increase atmospheric water vapor content and enhance poleward warm moisture from the ocean toward the Antarctic interior [65]. This process leads to an increase in snowfall and, thus, causes a decrease in surface albedo; this can be further confirmed by the reversal of the Antarctic SIAF from a negative trend to a positive one since 2016 because of anomalous Antarctic sea-ice losses between 2016 and 2018 [20].
Surface melting and temperature can also affect the surface albedo of AIS. Positive snowmelt albedo feedback plays a decisive role in the marginal regions of Antarctica [38]; when the snow melts and subsequently freezes in the cold snow, the snow grains enlarge. The larger grains increase the possibility of absorption and subsequently reduce the surface albedo. Thus, it is easily understandable that significant correlations exist between surface albedo and melting days in the peripheral regions of Antarctica. Seo et al. [30] highlighted in their study that rising temperatures lead to a decrease in albedo over Antarctica, which can be explained by the snow and ice feedback mechanism. Our study agrees well with this in the ablation zone at the edge of the ice sheet, where warm summer temperatures cause metamorphism and snowmelt, thereby reducing the surface albedo [66]. Nevertheless, in our investigation, the effect of temperature on the interior of ice sheet albedo appears minimal; this is likely due to the slow snow metamorphism processes resulting from the extremely low surface temperature in inland Antarctica. In particular, on the Antarctic Plateau, frost deposition and ice crystal precipitation also have the potential to alter the structure of surface ice crystals [67]. Furthermore, our study also shows the relatively small effect of snow accumulation on AIS albedo. Wind-driven drifting snow causes snow redistribution and, subsequently, results in surface albedo changes. However, drifting snow processes are not parameterized by ERA5, which, to some extent, limits the relative role of snow accumulation in surface albedo.
Sea ice albedo is sensitive to variations in air temperature and snow depth. An increase in sea ice albedo typically corresponds to a decline in temperature. Thicker snow cover generally has a higher albedo, and vice versa. Based on our findings, cloud cover exerts minimal influence on sea ice albedo; this can be attributed to the relatively stable summer CFC in the CLARA-A2 version above the Antarctic sea ice region since 1982 [68].

5. Conclusions

In this study, validation against in situ observations confirms that CLARA-A2.1-SAL has high quality and can be used to examine the spatial and temporal variations of the summer AIS and sea ice surface albedo. From 1982 to 2018, the AIS summer mean albedo showed a negative trend of 0.003 per decade, while a significant positive trend of 0.006 per decade was observed for the sea ice albedo. Spatially, significant downward trends of the ice sheet albedo occur in most of West Antarctica and parts of East Antarctica, especially in the Ross ice shelf and Ronne ice shelf, while significant upward trends are observed in the East Antarctic Peninsula and the edge of East Antarctica. Sea ice albedo trends are significantly positive in the Ross Sea and the Weddell Sea but significantly negative in the Bellingshausen and Amundsen Seas.
Several factors affect the albedo over the AIS and sea ice region. While SIE/SIC primarily affects the albedo of sea ice, it also exerts some influence on the surface albedo of AIS. Between 1982 and 2018, the SIE of each sea sector was significantly positively correlated with its sea ice albedo, but their correlations with AIS albedo were relatively low. A sudden decline of SIC across the sea ice region, starting in 2016, coincides well with sea ice albedo anomalies. The substantial sea ice losses between 2016 and 2018 also affected the ice sheet surface albedo due to positive feedback mechanisms. The albedo of the sea ice region is more sensitive to air temperature changes than that of the AIS; this can be attributable to slower snow metamorphism processes due to the lower surface temperature over the inland AIS than the sea ice zone. Snow depth on the sea ice also contributes much to the sea ice albedo changes, but the role of snow accumulation in AIS surface albedo seems minimal.

Author Contributions

Conceptualization, Y.W. and Y.S.; methodology, Y.W. and Y.S.; software, Y.S.; validation, Y.S. and Y.W.; formal analysis, Y.S.; investigation, Y.S.; resources, Y.W.; data curation, Y.S.; writing—original draft preparation, Y.S.; writing—review and editing, Y.W., Z.Z., M.Z. and Y.S.; visualization, Y.S.; supervision, Y.W.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

Funding this work were the National Natural Science Foundation of China (41971081), the National Key Research and Development Program of China (2020YFA0608202), the Strategic Priority Research Program of the Chinese Academy of Sciences (XAD19070103), and the Project for Outstanding Youth Innovation Team in the Universities of Shandong Province (2019KJH011).

Data Availability Statement

The SAL product and CFC product are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01 (accessed on 19 July 2023). The radiation data from AWS and BSRN are, respectively, available from https://doi.pangaea.de/10.1594/PANGAEA.910473 (accessed on 19 July 2023) and https://dataportals.pangaea.de/bsrn/?q=LR0300 (accessed on 19 July 2023). The sea ice extent can be obtained from the sea ice index product landing page (https://nsidc.org/data/g02135 (accessed on 19 July 2023)). The sea ice concentration dataset is available from https://nsidc.org/data/g02202/versions/4 (accessed on 19 July 2023). The daily surface melting dataset produced by Picard and Fily (2006) is available from https://snow.univ-grenoble-alpes.fr/melting/ (accessed on 19 July 2023). The ERA5 monthly averaged data are available from https://cds.climate.copernicus.eu/ (accessed on 19 July 2023). The snow depth dataset can be downloaded from the National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at https://data.tpdc.ac.cn/en/data/61ea8177-7177-4507-aeeb-0c7b653d6fc3 (accessed on 15 September 2023).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Spatial distribution of meteorological and BSRN stations and the boundaries (bold black lines) of sea ice regions around Antarctica.
Figure 1. Spatial distribution of meteorological and BSRN stations and the boundaries (bold black lines) of sea ice regions around Antarctica.
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Figure 2. A flowchart of this study.
Figure 2. A flowchart of this study.
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Figure 3. The MB and RMSE values of the monthly (a,c) and pentad mean (b,d) of the CLARA-A2.1-SAL product at eight stations in austral summer.
Figure 3. The MB and RMSE values of the monthly (a,c) and pentad mean (b,d) of the CLARA-A2.1-SAL product at eight stations in austral summer.
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Figure 4. Monthly surface albedo from in situ observations and the CLARA-A2.1-SAL product at the meteorological stations on the Antarctica ice sheet in austral summer. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.
Figure 4. Monthly surface albedo from in situ observations and the CLARA-A2.1-SAL product at the meteorological stations on the Antarctica ice sheet in austral summer. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.
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Figure 5. As for Figure 4, but for pentad surface albedo. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.
Figure 5. As for Figure 4, but for pentad surface albedo. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.
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Figure 6. Interannual variability in (a) AIS and (b) Antarctic sea ice summer mean albedo between 1982 and 2018. Solid black and green lines represent the albedo changes between 1982 and 2018 and 1982 and 2015, respectively, and dashed black and green lines are the corresponding linear trends.
Figure 6. Interannual variability in (a) AIS and (b) Antarctic sea ice summer mean albedo between 1982 and 2018. Solid black and green lines represent the albedo changes between 1982 and 2018 and 1982 and 2015, respectively, and dashed black and green lines are the corresponding linear trends.
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Figure 7. Spatial distribution of trends in summer surface albedo over (a) the Antarctic continent and (b) the sea ice from 1982 to 2018. Standard deviations of summer mean albedo between 1982 and 2018 on the (c) AIS surface and (d) sea ice. Black shadows in panels a and b indicate that the trends are significant at the confidence level of 95%.
Figure 7. Spatial distribution of trends in summer surface albedo over (a) the Antarctic continent and (b) the sea ice from 1982 to 2018. Standard deviations of summer mean albedo between 1982 and 2018 on the (c) AIS surface and (d) sea ice. Black shadows in panels a and b indicate that the trends are significant at the confidence level of 95%.
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Figure 8. Spatial distribution of correlation coefficients (R) between SIE in each sector and Antarctic albedo in the austral summer at different time periods. The smaller angle between the two black lines corresponds to the SIE in each sector, which in turn is the (a,d) Ross Sea, (b,e) Bellingshausen and Amundsen Sea, and (c,f) Weddell Sea. (a–c) are the period 1982–2018, (d–f) are the period 1982–2015. The black shadow indicates that the correlations at each grid pixel are significant at the 95% confidence level.
Figure 8. Spatial distribution of correlation coefficients (R) between SIE in each sector and Antarctic albedo in the austral summer at different time periods. The smaller angle between the two black lines corresponds to the SIE in each sector, which in turn is the (a,d) Ross Sea, (b,e) Bellingshausen and Amundsen Sea, and (c,f) Weddell Sea. (a–c) are the period 1982–2018, (d–f) are the period 1982–2015. The black shadow indicates that the correlations at each grid pixel are significant at the 95% confidence level.
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Figure 9. Anomalies of averaged SIC (a,b) and surface albedo (c,d) over the Antarctic continent and sea ice areas in the austral summer between 2013 and 2015 and 2016 and 2018, respectively, relative to the 1982–2011 mean. (a,c) are the period 2013–2015, and (b,d) are the period 2016–2018.
Figure 9. Anomalies of averaged SIC (a,b) and surface albedo (c,d) over the Antarctic continent and sea ice areas in the austral summer between 2013 and 2015 and 2016 and 2018, respectively, relative to the 1982–2011 mean. (a,c) are the period 2013–2015, and (b,d) are the period 2016–2018.
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Figure 10. Spatial correlations between summer mean albedo and (a) air temperature, (b) snow depth, and (c) CFC over Antarctic sea ice from 1982 to 2018. The correlation at the pixels covered by black shadow is significant at the 95% confidence level.
Figure 10. Spatial correlations between summer mean albedo and (a) air temperature, (b) snow depth, and (c) CFC over Antarctic sea ice from 1982 to 2018. The correlation at the pixels covered by black shadow is significant at the 95% confidence level.
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Figure 11. Spatial correlations between summer mean albedo and (a) surface melting days, (b) air temperature, and (c) snow accumulation (P-E) over the AIS from 1982 to 2018. The four rectangles in (a–c) are magnifications of the AIS margin. The correlations at the pixels covered by black shadow are significant at the 95% confidence level.
Figure 11. Spatial correlations between summer mean albedo and (a) surface melting days, (b) air temperature, and (c) snow accumulation (P-E) over the AIS from 1982 to 2018. The four rectangles in (a–c) are magnifications of the AIS margin. The correlations at the pixels covered by black shadow are significant at the 95% confidence level.
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Table 1. Validation metrics of the monthly and pentad mean surface albedo of the CLARA-A2.1-SAL product at stations during austral summer. Italic fonts indicate stations in which the number of effective monthly mean albedo is less than 20. The symbols “-“ at AWS18 indicate that there are no effective matching values.
Table 1. Validation metrics of the monthly and pentad mean surface albedo of the CLARA-A2.1-SAL product at stations during austral summer. Italic fonts indicate stations in which the number of effective monthly mean albedo is less than 20. The symbols “-“ at AWS18 indicate that there are no effective matching values.
MonthlyPentad
RMSEMBValid Value (N)RMSEMBValid Value (N)
AWS040.091−0.086140.111−0.09668
AWS050.062−0.057360.083−0.062187
AWS060.066−0.062240.079−0.068161
AWS090.039−0.001430.045−0.003281
AWS100.095−0.09540.158−0.12530
AWS110.105−0.102150.125−0.10778
AWS120.0250.021260.0310.021144
AWS130.0500.049250.0520.049129
AWS140.100−0.095120.117−0.11064
AWS150.125−0.123110.144−0.13661
AWS160.031−0.022150.044−0.02889
AWS170.119−0.11940.128−0.12526
AWS18------
AWS190.5490.54850.5570.55429
SYO0.0750.054580.0990.055343
SPO0.0470.019480.0490.017254
DOM0.4370.432170.4500.43197
GVN0.075−0.072810.095−0.077467
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MDPI and ACS Style

Sun, Y.; Wang, Y.; Zhai, Z.; Zhou, M. Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies. Remote Sens. 2023, 15, 4940. https://doi.org/10.3390/rs15204940

AMA Style

Sun Y, Wang Y, Zhai Z, Zhou M. Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies. Remote Sensing. 2023; 15(20):4940. https://doi.org/10.3390/rs15204940

Chicago/Turabian Style

Sun, Yuqi, Yetang Wang, Zhaosheng Zhai, and Min Zhou. 2023. "Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies" Remote Sensing 15, no. 20: 4940. https://doi.org/10.3390/rs15204940

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