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Article

Satellite-Derived Summer Albedo Variations on the Greenland Ice Sheet from 1979 to 2024 Linked with Climatic Indices

by
Yulun Zhang
,
Shang Geng
and
Yetang Wang
*
College of Geography and Environment, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(2), 295; https://doi.org/10.3390/rs18020295
Submission received: 27 November 2025 / Revised: 4 January 2026 / Accepted: 13 January 2026 / Published: 16 January 2026
(This article belongs to the Section Atmospheric Remote Sensing)
Browse Figures
Versions Notes

Highlights

What are the main findings?
  • Compared with earlier versions, the CLARA-A3-SAL product shows improved performance over Greenland, characterized by biases lower than 0.05 in the interior and correlations exceeding 0.6 at the majority of coastal stations.
  • Since 1979, Greenland summer albedo has declined significantly (−0.24% decade−1), driven primarily by meltwater-induced grain growth and bare ice exposure, while large-scale atmospheric circulation strongly influences the magnitude and spatial pattern of albedo change.
What are the implications of the main findings?
  • Reliable long-term satellite albedo records in polar regions, such as CLARA-A3-SAL, provide robust assessments of ice and snow changes and their energy-budget feedbacks, supporting analyses of climate trends and underlying processes.
  • The pronounced sensitivity of albedo to melt and atmospheric blocking, together with the decadal variability driven by the AMO and PDO, highlights the need for accurately representing albedo–melt–circulation interactions in climate and ice-sheet models.

Abstract

CLARA-A3 currently provides the longest temporal coverage among available albedo products, with improvements in both retrieval algorithms and product coverage compared to earlier versions. This study first evaluates the performance of the CLARA-A3-SAL product over Greenland Ice Sheet (GrIS) and subsequently applies it to investigate spatiotemporal trends in summer albedo from 1979 to 2024. Validation against 32 in situ observation sites indicates negligible bias in the interior regions, with RMSE values ranging from 0.01 to 0.07. Although larger errors exist in the coastal ablation zone due to unresolved sub-grid surface heterogeneity, the product successfully captures observed spatiotemporal variability and long-term trends, demonstrating that CLARA-A3-SAL provides a generally reliable representation of surface albedo. Since 1979, the summer surface albedo averaged over the entire ice sheet has decreased at a rate of −0.24% decade−1. Albedo in the dry snow area has remained relatively stable and showed no significant correlation with most climate variables, except for the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Conversely, the marginal zone has undergone substantial darkening (−0.66% decade−1), which is strongly correlated with temperature, snowfall and melt, with meltwater showing the highest correlation (r = −0.90, p < 0.01). This suggests that meltwater-driven grain growth and exposure of bare ice are the primary drivers of albedo reduction over the non-dry snow zone. Large-scale atmospheric circulation also plays a key role: the GBI exhibits the strongest association with albedo (r = −0.63, p < 0.05), underscoring the importance of persistent blocking in amplifying surface warming and darkening. Furthermore, decadal-scale variability associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) modulates both the magnitude and spatial pattern of albedo changes across GrIS, with AMO+ generally linked to reduced albedo and PDO+ tending to enhance it.

Graphical Abstract

1. Introduction

Greenland ice sheet (GrIS) mass-balance fluctuations exert a significant influence on global sea level, and changes within its cryosphere serve as key indicators of climate variability [1]. Over recent decades, its mass loss has accelerated, responding to dynamic thinning along the ice-sheet margins [2,3], enhanced discharge from outlet glaciers [4,5] and increased surface melt [6,7,8]. Surface shortwave albedo, defined as the fraction of incoming solar radiation reflected by the surface, is the dominant factor governing summer surface energy absorption and melt intensity [9]. It is highly sensitive to air temperature, with a maximum sensitivity exceeding −9% K−1 in northern terrestrial regions where the albedo contrast between snow-covered and bare surfaces can reach ~0.7. where snow–bare-ground contrasts approach ~0.7 [10]. Rising temperatures accelerate snow-grain growth and reduce surface albedo [11,12], thereby increasing the absorption of solar radiation at the glacier surface and further enhancing surface warming and snow cover retreat. This strong surface–albedo feedback also modifies sensible and latent heat fluxes, altering atmospheric moisture content and temperature structure [13,14]. The associated changes in water vapor and thermal stratification influence cloud formation and radiative properties. Furthermore, the resulting surface warming weakens the poleward thickness gradients, which can lead to slower zonal winds and increased wave amplitudes, thereby affecting large-scale atmospheric circulation patterns [14]. Consequently, long-term and reliable albedo observations are essential for constraining the Arctic surface energy budget and its climate response.
Given the logistical challenges of acquiring data in extremely harsh polar environments, in situ observations over the GrIS are sparse and rarely provide continuous records. Satellite remote sensing is the only approach capable of monitoring albedo spatially continuous and temporally consistent at continental scales. Several sensors have been used for albedo retrievals, including the Advanced Very High Resolution Radiometer (AVHRR), the Polarization and Directionality of the Earth Reflectances (POLDER) instrument, the Clouds and the Earth’s Radiant Energy System (CERES), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Different satellite sensors display distinct advantages and limitations [15,16,17]. Although MODIS offers high-quality albedo retrievals with improved angular sampling, its record beginning in 2000 restricts its relevance for multi-decadal change assessments. In contrast, the AVHRR dataset extends back to the early 1980s, nearly doubling the temporal coverage of MODIS and offering the possibility to investigate long-term albedo variability and trends in polar regions. Accordingly, CLARA-A3-SAL is an AVHRR-based albedo product with more complete temporal coverage and improved algorithmic stability compared to earlier CLARA versions.
A number of studies have employed AVHRR-based albedo products to investigate albedo variability over GrIS. For example, Knap and Oerlemans analyzed surface albedo variations east of Søndre Strømfjord during the 1991 melt season utilizing AVHRR narrowband reflectances and in situ measurements [18]. Riihelä et al. used the CLARA-A2 record to examine decadal albedo changes in the ice sheet from 1982 to 2015 and linked these changes to variations in surface mass balance [19]. Although many studies have relied on earlier AVHRR albedo products, the newest CLARA-A3-SAL dataset, which provides the longest and most advanced Arctic albedo record, has not yet been systematically evaluated over GrIS. As a result, its performance and the summer albedo characteristics it reveals, as well as the influences of various controlling factors, remain largely to be explored.
In this study, the performance of the CLARA-A3-SAL across GrIS is first evaluated using available in situ surface albedo observations. Then, based on this dataset, we explore spatial and temporal variations in summer albedo from 1979 to 2024 and examine their relationships with surface conditions and large-scale atmospheric circulation. Finally, we discuss the potential impacts of additional factors, including cloud conditions, impurity concentration, and a range of surface darkening processes, on variability in GrIS surface albedo.

2. Materials and Methods

2.1. CLARA-A3-SAL Product

Developed by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), CLARA-A3 is derived from the Advanced Very High Resolution Radiometer (AVHRR), operated by the National Oceanic and Atmospheric Administration (NOAA, Washington, DC, USA). AVHRR is one of the multispectral meteorological imagers providing monthly series of global coverage with a four-times-daily sampling frequency (more frequently at high latitudes) for over four decades [20]. Compared with CLARA-A2, which provides data from January 1982 to June 2019, the temporal coverage of CLARA-A3 is extended both backward (with additional years 1979–1982) and forward (by adding data up to 2020). Beyond 2020, the record is further extended up to 2024 through an Interim Climate Data Record (ICDR) generated using the same algorithms as the original CDR (1979–2020), ensuring consistency across the entire time period. For surface albedo retrieval, the CLARA-A3 algorithm processes data sequentially, starting with topography and atmospheric corrections, followed by the treatment of angular reflectance isotropy, the derivation of spectral albedos, and finally, the estimation of broadband surface albedo. In addition, the CLARA-A3-SAL dataset is built upon a Fundamental Climate Data Record (FCDR) during computation. Within this framework, individual sensors’ trend corrections and satellite inter-calibration are performed using an updated version of the method described by Heidinger et al. [21], with early sensors further calibrated by stable surface targets. This rigorous pre-processing minimizes shifts in the albedo time series. This latest release incorporates substantial algorithmic improvements across all retrievals, including an enhanced cloud mask and refined atmospheric correction procedures, which collectively enhance the accuracy and consistency of the climate data record. Furthermore, a notable advancement in this edition is the introduction of two additional surface albedo products derived through empirical relationships: blue-sky albedo (BAL) and white-sky albedo (WAL), which complement the existing black-sky albedo (SAL) product. All albedo estimates are provided on a standard global grid with 0.25° × 0.25° resolution, supplemented by higher-resolution (25 km × 25 km) polar subsets in the EASE-2 projection [22].

2.2. Automatic Weather Station Data

Surface albedo observations for validation were obtained from the Programme for Monitoring of the GrIS (PROMICE) and Greenland Climate Network (GC-Net) automatic weather station networks [23]. These networks comprise 51 automatic weather stations across the Greenland Ice Sheet, providing measurements at hourly, daily, and monthly resolutions. In this study, we use monthly albedo observations to ensure temporal consistency with the CLARA-A3-SAL satellite product and to reduce the influence of short-term weather variability and data gaps commonly present in daily station records. Moreover, a monthly temporal scale is more appropriate for comparison with large-scale and multi-decadal climate indices, such as the NAO and GBI.
The observational records vary across different stations, with the earliest measurements dating back to 1990. To maximize spatial representation while maintaining adequate data quality for robust statistical analysis, we implemented a selection criterion requiring a minimum of 20 months of valid observations. Finally, 32 sites formed the final validation dataset used in this study. The geographical distribution and spatial context of elected stations within the GrIS are presented in Table 1 and Figure 1, respectively.

2.3. ERA5

ERA5, the fifth-generation atmospheric reanalysis product from the European Centre for Medium-Range Weather Forecasts (ECMWF), provides comprehensive climate data spanning from 1940 to the present [24]. This dataset covers the globe with a horizontal resolution of 0.25° × 0.25° and resolves the atmosphere using 137 levels from the surface up to the top level of 0.01 hPa (~80 km). It incorporates advanced data assimilation techniques and extensive historical observations, generating hourly estimates of atmospheric, land, and oceanic climate variables [25]. Compared to its predecessor, ERA-Interim, ERA5 demonstrates significant improvements in accuracy through the integration of broader historical observational datasets and multi-level vertical satellite data. Previous validation studies have confirmed its reliable performance in representing temperature and snowfall patterns over GrIS [26,27]. For this study, we utilize monthly 2 m air temperature and snowfall data from ERA5 to analyze climate conditions and their relationship with surface albedo variations across the GrIS.

2.4. Snowmelt

The snowmelt data used in this study were derived from the Modèle Atmosphérique Régional (MAR). MAR is a limited-area atmosphere model developed in the mid-1990s that was specifically designed for studying the near-surface climate and surface mass balance in polar areas. The model resolves the hydrostatic primitive equations on a three-dimensional grid utilizing the full continuity equation [28]. Here, we utilized MAR version 3.12.0 (MARv3.12), which is forced by the ERA5 reanalysis and configured for a domain surrounding GrIS [29]. The model output has a spatial resolution of 15 km and a high temporal resolution of 1 h, covering the summer months (June–July–August, JJA) from 1980 to 2020 [30].

2.5. Large-Scale Atmospheric Circulation Indices

For analyzing the association between GrIS albedo and atmospheric circulation changes over the GrIS, we employed monthly series of the following four climate indices: The Greenland Blocking Index (GBI) is defined as the mean geopotential height over the Greenland region (60–80°N, 20–80°W), serving as a key metric for the strength of persistent high-pressure systems that influence ice sheet melting. The North Atlantic Oscillation (NAO), typically calculated based on the normalized pressure difference between stations near Iceland and the Azores, governs the intensity and direction of westerly winds and storm tracks across the North Atlantic. The Atlantic Multidecadal Oscillation (AMO) is calculated from detrended North Atlantic SST anomalies, while the Pacific Decadal Oscillation (PDO) is computed as the leading EOF (principal component) of North Pacific SST anomalies after removing the global SST trend. The above four large-scale climate indices can be acquired from NOAA (https://psl.noaa.gov/data/climateindices/, accessed on 10 December 2025).

2.6. Validation and Trend Calculation

To investigate the performance of CLARA-A3 in representing the surface albedo of the GrIS, mean bias (MB), correlation coefficient (R), and root-mean-square error (RMSE) are used to quantify the differences between satellite products and in situ observations for their overlapping time periods. The statistical significance of the correlation coefficient was assessed using a Student’s t-test, while the significance of linear trends was determined with an F-test. Area-weighted albedo time series are also provided for two subregions (dry snow and non-dry snow areas), based on the surface classification from Vandecrux et al. [31]. To identify albedo trends in the records of CLARA-A3, segmented regression was applied. This method has been widely used in climate and cryospheric studies to detect breakpoints in long-term time series [32]. It assumes that the time series can be represented by different linear segments separated by breakpoints, allowing different linear trends before and after the transition, as follows:
y t = k 1 t − t 0 + y 0 , t < t 0   k 2 t − t 0 + y 0 , t ≥ t 0
where t0 denotes the breakpoint year, y 0 is the fitted value at the breakpoint, and k 1 and k 2   represent the linear trends before and after the breakpoint, respectively. Breakpoints were identified through an iterative procedure with 100 repetitions. For each iteration, segmented linear regression was performed using the least-squares method, in which separate linear models were fitted to each segment to minimize the sum of squared differences between the observed values and the corresponding model estimates. The statistical significance of the identified breakpoint was evaluated using formal hypothesis testing, and only breakpoints satisfying a significance level of p < 0.05 were retained.

3. Results

3.1. Performance of CLARA-A3-SAL Product

The absence of direct and diffuse shortwave radiation measurements at in situ stations precludes reliable discrimination between clear and overcast skies, thus rendering validations of WAL and BAL impracticable. In addition, due to the generally high cloud fraction over the Arctic, truly clear-sky conditions (cloud cover = 0) are extremely rare in the observation records, further preventing the validation of BAL. Considering these limitations, only SAL was used in our validation. Figure 2 presents the statistical errors of monthly mean summer albedo from the CLARA-A3-SAL product during 1979–2024 (Figure 2a,b). In the interior dry-snow region, CLARA-A3-SAL exhibits RMSE values of 0.01–0.07 (≤0.04 at most sites), representing a small fraction of the mean dry-snow albedo (78.60%). However, correlations with in situ data are weak, possibly because the limited interannual variability of albedo in this region reduces the signal-to-noise ratio of the statistical comparison. On the other hand, slightly larger errors were observed in non-dry snow areas, particularly at coastal sites such as KPC_L, KPC_U, and NUK_L, where MB and RMSE values exceed 0.2. Despite these larger absolute errors, the correlation coefficients are generally above 0.6, indicating that CLARA-A3-SAL effectively captures the interannual variability of albedo. The comparison of long-term trends further supports this result (Figure 2d). The product successfully reproduces the observed temporal trend of albedo, suggesting that the satellite product accurately captures the observed albedo decline associated with intensified surface melting.
Spatially, the errors in CLARA-A3-SAL increase gradually from the inland ice sheet toward the coastal margins (Figure 3). The surface conditions along the margins of the GrIS are highly complex and heterogeneous. This heterogeneity cannot be adequately resolved by the coarse spatial resolution of satellite products, leading to larger errors relative to point-scale in situ measurements. From the comparison of correlation distribution, correlations are generally higher at southern and inland stations than at northern and coastal ones. This pattern suggests that, although absolute errors are smaller in the interior dry snow regions, the limited temporal variability of albedo there weakens correlation strength. In contrast, stronger surface variability in coastal areas enhances correlation sensitivity but simultaneously amplifies absolute errors.

3.2. Temporal and Spatial Variability of Summer Albedo over the Greenland Ice Sheet

The temporal and spatial trends of summer mean albedo over GrIS from 1979 to 2024 are presented in Figure 4 and Figure 5, respectively. To further reveal decadal trends and filter out high-frequency interannual fluctuations, a 9-year running mean was applied to the surface albedo time series (Figure 4b). For the entire GrIS, the summer albedo decreased by −0.24% decade−1, a trend that became more evident after a 9-year running mean (−0.27% decade−1). This ice-sheet-wide decline is primarily driven by changes in the marginal areas, as the interior dry snow zone remained remarkably stable with a negligible trend of −0.01% decade−1 (−0.03% decade−1 after smoothing). In contrast, the albedo in the non-dry snow zones exhibited a more pronounced decline of −0.66% decade−1 (−0.70% decade−1 after smoothing), reflecting the high sensitivity of these regions to surface melt and bare ice exposure.
As shown in Figure 4, the albedo over GrIS did not exhibit a uniform declining pattern throughout the study period. Therefore, we applied the segmented linear regression to identify temporal discontinuities and divided the period into two distinct phases: 1979–1985 and 1986–2024. Over the entire period (1979–2024), weak positive (0–1% decade−1) dominates the interior of the ice sheet, whereas significant decreases are concentrated along the coastal margins, reaching up to −3% decade−1 (Figure 5a). During the early phase (1979–1985), albedo increased markedly over most of GrIS, exceeding +2% decade−1 and even surpassing +6% decade−1 in the southeastern region. Although this period coincides with the early AVHRR era, CLARA-A3 applies a calibration approach to correct inter-sensor differences and mitigate potential biases arising from orbital drift and sensor degradation [21]. This marked increase likely reflects a response to atmospheric circulation patterns. This period was characterized by a predominantly positive NAO phase and a relatively low GBI, which typically favor cooler conditions and enhanced cyclonic activity. This is further supported by ERA5 (Figure 6 and Figure 7), which show increasing snowfall and decreasing near-surface air temperature across the whole GrIS during this period. The fresh snow likely enhanced albedo by reducing grain size and masking darker underlying surfaces. Subsequently, the albedo undergoes a phase of rapid and widespread decline, covering almost all regions except a few stable interior sites, with the most pronounced decreases (exceeding −4% decade−1) existing in the northern bare-ice zones. This acceleration marks a fundamental transition in the state of the whole ice sheet’s cryosphere during the 21st century.

3.3. Drivers of Summer Albedo Variability on the Greenland Ice Sheet

3.3.1. Regional Drivers of Albedo Variability

Given the significant spatial and temporal heterogeneity in surface albedo changes over GrIS (Figure 5), it is essential to further explore the potential factors driving these changes. Snowfall plays a crucial role in modulating surface albedo by depositing fresh, high-albedo snow layers. Therefore, we further analyzed the spatiotemporal relationship between snowfall and surface albedo over the GrIS (Figure 6). The results show that there is a significant positive correlation between snowfall and surface albedo across most parts of GrIS, with the strongest correlations (r > 0.6) occurring in the northern, western, and southern regions (Figure 6a). These regions exhibit a general decreasing trend in snowfall, which coincides with declining surface albedo trends. This pattern suggests that the combined effects of reduced snowfall, enhanced surface melting, and expanded bare-ice exposure contribute to the persistent decline in surface albedo in these areas. Although snowfall has increased to some extent after 2010, this recovery only partially alleviates the downward trend and is insufficient to reverse the long-term albedo decline. The relationship between the two factors is relatively weak over the interior zone. The temporal analysis further confirms this spatial relationship (Figure 6c,d). In the dry snow zone (Figure 6c), Snowfall and albedo exhibit coherent interannual variability, but the overall correlation is weak (r = 0.18), indicating that the albedo in this region remains relatively stable and is less sensitive to snowfall variations. In contrast, a significant positive correlation (r = 0.60, p < 0.05) is observed in the non-dry snow zone (Figure 6d), suggesting that snowfall variability exerts a more direct influence on albedo along coastlines experiencing frequent melting and refreezing.
Figure 7 illustrates the spatial and temporal relationships between 2 m air temperature and surface albedo over GrIS during the summer period of 1979–2024. The temperature trend indicates a significant warming across nearly the entire ice sheet, with most regions experiencing an increase exceeding 0.75 °C decade−1 (Figure 7b). There is a general negative correlation between temperature and albedo across GrIS, which means that higher surface temperatures are usually associated with lower albedo. This negative correlation is particularly pronounced in the northern regions and along the extensive coastal melt zones, where strong surface melting and frequent bare-ice exposure prevail. This is because increased snow temperatures in these regions accelerate the transformation of fine-grained fresh snow into coarse-grained old snow [12], thereby darkening the surface and enhancing albedo reduction through a positive feedback mechanism. Statistical analysis further revealed a significant negative correlation between temperature and albedo in non-dry snow areas (r = −0.77, p < 0.05). Conversely, the correlations over the interior dry snow region are insignificant (r = −0.25), and even show positive correlation in some areas, suggesting that albedo can increase during anomalously warm periods. Although a positive correlation may seem counterintuitive, this is physically consistent as a second-order effect and aligns with the positive relationship between anomalies in air temperature and snowfall [1].
The increase in surface meltwater substantially alters the physical properties of snow grains. The presence of meltwater promotes snow grain growth, and as light travels through larger grains, it must propagate over longer paths before being scattered, thereby enhancing light absorption within the snowpack and reducing surface albedo [12,33]. Based on this mechanism, we further investigated the relationship between meltwater and surface albedo. During the summer from 1979 to 2024, GrIS experienced an overall increasing trend in surface melting, with the most pronounced and statistically significant meltwater growth occurring along the coastal ablation regions (Figure 8a). Correspondingly, these regions also exhibit a marked decrease in surface albedo (Figure 8b), and the correlation between meltwater and albedo exceeds 0.5 in most of these areas, indicating a strong negative relationship. Further analysis of the interannual variations in albedo and meltwater over different regions (Figure 8c,d) reveals that their correlation reaches −0.37 in the dry snow region and as high as −0.90 in the non-dry snow region. This result suggests that in areas characterized by melt and refreeze, variations in meltwater exert a more direct and substantial influence on surface albedo, exerting an influence that is even stronger than that of temperature and snowfall changes. During abnormally warm melt seasons, the loss of the high-albedo snowpack exposes the relatively dark, impurity-rich bare ice surface, thereby amplifying melt volumes and further accelerating the decline in surface albedo [1]. This feedback mechanism is further explored in Section 4.

3.3.2. Influence of Atmospheric Circulation Patterns on Summer Albedo over the Greenland Ice Sheet

We further examined the relationships between key atmospheric circulation indices and surface albedo. The results reveal a significant positive correlation between the NAO and albedo (Figure 9a and Figure 10a). During the positive phase of the NAO (NAO+), the strengthened meridional pressure gradient across the mid- and high latitudes of the Northern Hemisphere enhances the westerly jet, which shifts northward under geostrophic balance. This northward displacement favors increased snowfall and cloud cover over GrIS, thereby elevating surface albedo. Conversely, during the negative phase of the NAO, anomalous North Atlantic Sea surface temperatures—linked to tropical Pacific Rossby wave–train activity [34]—induce large-scale atmospheric circulation anomalies. These anomalies promote moisture and heat transport from the North Atlantic to GrIS [35], leading to regional warming and reduced albedo. Overall, NAO and albedo exhibit a significant positive relationship (r = 0.43), with particularly strong coherence in the non–dry snow zones, where the correlation reaches 0.53.
Across the entire ice sheet, the temporal variations in GBI and albedo exhibit striking synchronicity, with a correlation up to 0.72 in non–dry snow regions—higher than for any other circulation index (Figure 9). Spatially, the relationship between GBI and albedo is strongest in the western interior and exhibits a clear weakening gradient toward the ice sheet margins. The physical mechanism underlying this correlation is linked to specific atmospheric regimes (Figure 10). Periods of NAO in a negative phase (NAO−) or transitions toward NAO− conditions are typically accompanied by the positive phase of the GBI (GBI+) [36,37,38]. The negative NAO and positive GBI regimes favor persistent anticyclonic blocking over GrIS [39]. Such blocking weakens the northerly flow and enhances the intrusion of warm, moist air from mid-latitudes. Such conditions promote clear skies and reduce diabatic heating, leading to less summer snowfall and sustained snow metamorphism that maintains low albedo. Conversely, a weakened or negative GBI allows for more cold northerly airflow and increasing cloudiness, thereby intensifying surface darkening [40].
Given that the AMO and PDO represent multidecadal climate variability, we applied a 9-year moving average to the albedo time series. This smoothing suppresses short-term interannual noise, thereby revealing decadal-scale variations more clearly and allowing for a more robust evaluation of the long-term relationships between albedo and climate variability. The results indicate that, except for a few inland regions, AMO and albedo are predominantly negatively correlated (Figure 9c and Figure 10c). Previous studies have reported a long-term positive correlation between AMO and GBI [41,42]. The AMO represents the leading low-frequency mode of the North Atlantic and, during its warm phase (AMO+), the subtropical ocean experiences pronounced warming [43]. Persistent wind patterns then transport warm water toward the subpolar North Atlantic [35], enhancing regional warming and reducing surface albedo. This is most pronounced along the GrIS coast, where the correlation with AMO and non–dry snow albedo reaches 0.6 (Figure 9c). Moreover, Auger et al. [44] showed that the shift from the AMO− to AMO+ phase is associated with increased precipitation and interannual variability, consistent with the signals observed in our albedo analysis (Figure 6). It notices that, although the 45-year study period (1979–2024) does not encompass a complete 60–80 year AMO cycle, it importantly spans a major phase transition from the cold phase to the warm phase in the 1990s. This coverage of two distinct phases (negative and positive) provides a more robust basis for correlation analysis than a period characterized by a single monotonic trend. Nevertheless, we acknowledge that the shared secular warming trend likely amplifies the statistical relationship between the AMO and albedo decline. The observed correlation should therefore be viewed as a combined signature of internal multidecadal variability and the background warming of the North Atlantic system.
In contrast, the PDO often evolves in the opposite phase to the AMO, and thus its relationship with albedo also reverses. Overall, PDO exhibits a positive correlation with albedo (r = 0.53; Figure 9d and Figure 10d). Although the PDO does not directly influence GrIS, its negative phase weakens the Icelandic Low, reducing the pressure gradient over southern GrIS [45]. This weakening leads to reduced zonal wind strength and slower upper-level flow, which in turn increases the frequency of atmospheric blocking events [44]. Such circulation patterns favor persistent extreme conditions, including droughts and heatwaves. Meanwhile, the weakened pressure gradient allows airflow to be easily disturbed, facilitating the advection of warm midlatitude air into the Arctic [46]. Both effects contribute to surface warming, enhanced meltwater runoff, and thus lower albedo. Conversely, during the positive phase of the PDO, the Icelandic Low strengthens and the associated ridge shifts southward. This change intensifies the pressure gradient, accelerating atmospheric flow and inhibiting northward warm-air advection from the midlatitudes [46], thereby creating favorable conditions for higher albedo. The opposite local patterns shown in Figure 9c,d likely reflect regions where, during the +AMO/–PDO phase, increased precipitation counteracts the warming effect. In these cold inland areas, snowfall-induced brightening may locally outweigh temperature-driven darkening, resulting in the observed contrasting spatial signatures.

4. Discussion

Although the validation in this study is limited to SAL due to the absence of in situ diffuse and direct radiation measurements, future efforts could explore proxy-based validation strategies. For instance, WAL could be evaluated during near-overcast conditions (cloud cover > 0.99) to approximate a fully diffuse radiation field. However, this approach remains challenging, as even complete cloud cover does not guarantee a lack of directionality in incoming irradiance, particularly under optically thin cloud conditions. Validating the BAL would further require either precise satellite overpass tracking to match instantaneous geometry. Such developments are planned for future CLARA efforts [20]. While the validation of BAL and WAL was beyond the scope of this study but represents an important direction for future climate data record assessments.
Our validation results show that, in the inland accumulation zone, the bias of CLARA-A3-SAL snow albedo is negligible, with no systematic overestimation or underestimation, and the RMSE ranges between 0.01 and 0.07. These results are consistent with previous validation of CLARA-A2 SAL reported by Riihelä et al. [19], although CLARA-A3-SAL exhibits improved performance with smaller uncertainties. In the coastal ablation zone, CLARA-A3-SAL exhibits substantially larger biases. This finding is supported by both Riihelä et al. [19] and Karlsson et al. [20], and is likely associated with the pronounced surface heterogeneity in this region [47]. Earlier in situ measurements have shown that the ablation zone is characterized by highly variable non-ice components and complex surface structures [48,49]. To assess spatial representativeness, we quantified sub-grid land-cover fractions using the Dynamic World dataset [50], highlighting sites with less than 90% snow/ice coverage in Table 1. Since this dataset does not distinguish specific ice facies (e.g., bare ice and melt ponds), we further examined the dependence of albedo errors on elevation (Figure S1), which serves as a useful proxy for melt intensity. For sites with high snow and ice coverage, all stations except KPC_L, KPC_U, and TAS_A exhibit biases smaller than 0.15 and RMSE values below 0.2. These three stations are located at relatively low elevations near the ice-sheet margin. This behavior is expected, as ablation-zone melt processes induce pronounced surface transformations, including the widespread development of melt ponds and channels, as well as the exposure of impurities following the removal of the seasonal snow layer [18]. Conversely, retrieval precision is significantly enhanced at higher elevations, where surface melt is minimal and melt effects are subdued.
A potential source of uncertainty in our regional analysis is the use of a static mask, which may not account for the inland migration of the snowline over the 45-year study period. To evaluate this, we conducted an analysis by comparing albedo trends derived from masks representing conditions at the beginning and the end of the satellite era. The results show a trend discrepancy of only ± 0.04%decade−1 (Table S1), indicating that the impact of snowline dynamics on the reported long-term trends is negligible. In addition, a static mask ensures a consistent geographical reference and avoids statistical artifacts from a shifting study area.
The CLARA-A3-SAL dataset exhibits a negative albedo trend of −0.24% decade−1 over the GrIS. Both the magnitude and spatio-temporal distributions of albedo trends are consistent with those reported by Tedesco et al. [51], who performed an analysis of summer GrIS surface albedo changes from the GLASS dataset derived from coherently and consecutively processed AVHRR and MODIS observations. Their analysis for 1981–2012 revealed that the observed albedo decline was associated with the surface enrichment of light-absorbing impurities (LAI) driven by enhanced sublimation and meltwater removal, alongside the melt-induced exposure of ‘dirty’ underlying ice layers. These processes provide a physical explanation for the role of meltwater in modulating surface albedo, as discussed above. Notably, even without a significant trend in mean aerosol optical depth, the increasing frequency of episodic wildfire events—such as the soot deposition in 2013—can enhance the deposition of light-absorbing impurities and thereby contribute to long-term albedo decline. A detailed discussion on the mechanisms of light-absorbing impurities and their impact on albedo is provided in Text S1 in Supplementary Materials [48,51,52,53,54,55]. Apart from that, clouds also play an important role in modulating surface albedo. Over highly reflective snow and ice-covered surfaces, cloud-induced spectral weighting shifts the transmitted downward irradiance toward shorter wavelengths, which tends to increase the shortwave surface albedo [56]. At the same time, clouds convert predominantly direct irradiance into more diffuse light, thereby reducing the shortwave albedo [11,56]. Studies indicate a tendency for surface albedo to be higher under cloudy compared with cloud-free conditions [57]. Cloud–albedo interactions also represent an important pathway through which circulation anomalies associated with the NAO and GBI modulate surface albedo variability. In addition to the factors discussed above, the dark zone is also influenced by several local processes—such as fluctuations in the snowline, ice-algal proliferation, surface roughening, and the expansion of cryoconite holes—that further contribute to albedo variability [58,59,60].

5. Conclusions

In this study, validation against in situ measurements confirms that the CLARA-A3-SAL product provides a reliable representation of summer albedo in the interior accumulation zone of the GrIS (RMSE: 0.01–0.07). In the coastal ablation zone, larger errors occur relative to inland areas, partly due to point-to-pixel mismatches associated with pronounced sub-grid surface heterogeneity. Despite these offsets, the consistently high correlation with in situ observations confirms that the product accurately captures the temporal evolution of albedo even in complex marginal environments. Moreover, the product successfully captures the observed albedo trends. These results indicate that CLARA-A3-SAL is suitable for assessing summer albedo variability across the GrIS. From 1979 to 2024, GrIS summer albedo exhibits marked spatial heterogeneity. Albedo over the dry snow zone remains largely stable, whereas the wet-snow and ablation areas show pronounced darkening (−0.66% decade−1), driving a significant decline for the ice sheet as a whole. A moving t-test reveals an abrupt shift around 1986, after which coastal darkening accelerates, exceeding −4% decade−1 in some regions.
The observed albedo decline is jointly governed by surface melt, temperature increases, snowfall variability, and large-scale circulation. However, in the dry snow zone, the correlations between albedo and these local factors are not statistically significant. In the non-dry snow zone, rising temperatures and enhanced meltwater strongly promote grain growth and bare-ice exposure, exerting the dominant influence on albedo reduction. Snowfall exerts a compensating effect; however, it shows an overall decreasing trend during 1979–2024, with only a partial recovery after 2010. The long-term reduction in snowfall weakens its ability to offset melt–darkening feedbacks. Furthermore, NAO, GBI, AMO, and PDO modulate temperature, moisture intrusions, and the frequency of blocking episodes that affect albedo. Among them, the GBI shows the strongest relationship with albedo, highlighting the role of persistent blocking in amplifying regional warming and surface darkening. In addition, the AMO and PDO show opposite spatial correlation patterns with albedo, and their associated decadal variability further modulates the magnitude and spatial pattern of albedo responses.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/rs18020295/s1, Text S1: Mechanisms of Light-Absorbing Impurities (LAI) and the Impact of Episodic Events; Figure S1: Relationship between surface elevation and albedo errors at observation sites; Table S1: Impact of snowline migration on estimated summer albedo trends in dry-snow and non-dry snow regions.

Author Contributions

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

Funding

This work was supported by the Shandong Province Science Foundation for Youths (Grant Number: ZR2024QD254), the National Key Research and Development Program of China (Grant Number:2020YFA0608202), the Taishan Scholars Program of Shandong Province (Grant Number: No. tsqn202312158), and the National Natural Science Foundation of China (Grant Number: 41971081).

Data Availability Statement

The data used in this study are available from the following publicly accessible sources: The SAL products are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (accessed on 10 December 2025); The albedo data from observation stations are available at https://doi.org/10.22008/FK2/IW73UU (accessed on 10 December 2025); The MAR surface melting dataset is available from https://doi.org/10.5281/zenodo.7591112 (accessed on 10 December 2025); The ERA5 monthly averaged data are available from https://doi.org/10.24381/cds.143582cf (accessed on 10 December 2025).

Acknowledgments

We thank the providers of all the datasets used in the article for making their data publicly accessible.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
GrISGreenland Ice Sheet
AVHRRAdvanced Very High Resolution Radiometer
POLDERThe Polarization and Directionality of the Earth Reflectances
CERESThe Clouds and the Earth’s Radiant Energy System
MODISThe Moderate Resolution Imaging Spectroradiometer
PROMICEProgramme for Monitoring of the GrIS
GC-NetGreenland Climate Network
ECMWFEuropean Centre for Medium-Range Weather Forecasts
MARModèle Atmosphérique Régional
JJAJune–July–August
GBIThe Greenland Blocking Index
NAOThe North Atlantic Oscillation
AMOThe Atlantic Multidecadal Oscillation
PDOPacific Decadal Oscillation
MBmean bias
Rcorrelation coefficient
RMSEroot-mean-square error
BALblue-sky albedo
WALwhite-sky albedo
SALblack-sky albedo

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Figure 1. Map of GrIS showing the two subregions. The non-dry snow area is defined as including both the percolation zone and the bare ice zone. The figure also shows the spatial distribution of observational data located on the GrIS, with valid albedo records exceeding 20 months. Panels (a–c) provide enlarged views of selected regions with dense station coverage. The numeric labels for each station in the map correspond directly to the Series listed in Table 1.
Figure 1. Map of GrIS showing the two subregions. The non-dry snow area is defined as including both the percolation zone and the bare ice zone. The figure also shows the spatial distribution of observational data located on the GrIS, with valid albedo records exceeding 20 months. Panels (a–c) provide enlarged views of selected regions with dense station coverage. The numeric labels for each station in the map correspond directly to the Series listed in Table 1.
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Figure 2. Performance evaluation of the monthly CLARA-A3 data at in situ stations over GrIS for summer. (a) Mean Bias; (b) RMSE; (c) Correlation coefficient; and (d) albedo trends (%/decade) for CLARA-A3-SAL (blue dots) and in situ observations (red dots). In (d), blue and red asterisks (*) indicate that the trends for CLARA-A3 and observations are statistically significant (p < 0.05), respectively. The vertical dashed line separates the stations into the dry snow zone (left) and non-dry snow zone (right).
Figure 2. Performance evaluation of the monthly CLARA-A3 data at in situ stations over GrIS for summer. (a) Mean Bias; (b) RMSE; (c) Correlation coefficient; and (d) albedo trends (%/decade) for CLARA-A3-SAL (blue dots) and in situ observations (red dots). In (d), blue and red asterisks (*) indicate that the trends for CLARA-A3 and observations are statistically significant (p < 0.05), respectively. The vertical dashed line separates the stations into the dry snow zone (left) and non-dry snow zone (right).
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Figure 3. Spatial distribution of the CLARA A3 SAL albedo bias relative to the observations. The colored dots represent the locations of the stations, with the fill color indicating the value at each site as defined by the color scale.
Figure 3. Spatial distribution of the CLARA A3 SAL albedo bias relative to the observations. The colored dots represent the locations of the stations, with the fill color indicating the value at each site as defined by the color scale.
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Figure 4. Time series of the summer albedo averaged in Greenland during the period 1979–2024: (a) original data with its linear trend (dashed line); (b) the 9-year moving average applied to the original time series with its linear trend (dashed line). The trends labeled with “*” are statistically significant at p < 0.05 level.
Figure 4. Time series of the summer albedo averaged in Greenland during the period 1979–2024: (a) original data with its linear trend (dashed line); (b) the 9-year moving average applied to the original time series with its linear trend (dashed line). The trends labeled with “*” are statistically significant at p < 0.05 level.
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Figure 5. The spatial trends of summer albedo over the GrIS from (a) the entire study period (1979–2024) and (b,c) two sub-periods. The trends in the areas covered by black dots are statistically significant at p < 0.05 level.
Figure 5. The spatial trends of summer albedo over the GrIS from (a) the entire study period (1979–2024) and (b,c) two sub-periods. The trends in the areas covered by black dots are statistically significant at p < 0.05 level.
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Figure 6. Influence of snowfall variability on surface albedo over GrIS: (a) spatial correlation between snowfall and surface albedo over the GrIS; (b) trend of summer snowfall; (c,d) time series of summer albedo and snowfall over the GrIS dry snow and non–dry snow areas. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
Figure 6. Influence of snowfall variability on surface albedo over GrIS: (a) spatial correlation between snowfall and surface albedo over the GrIS; (b) trend of summer snowfall; (c,d) time series of summer albedo and snowfall over the GrIS dry snow and non–dry snow areas. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
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Figure 7. Same as Figure 6, but for the relationship between 2 m air temperature and albedo over the GrIS. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
Figure 7. Same as Figure 6, but for the relationship between 2 m air temperature and albedo over the GrIS. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
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Figure 8. Same as Figure 6, but for the relationship between snow melt and albedo over the GrIS. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
Figure 8. Same as Figure 6, but for the relationship between snow melt and albedo over the GrIS. The areas covered by black dots in the spatial plots, and the correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
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Figure 9. Time series of summer albedo averaged over the total GrIS (black), dry snow area (yellow), and non-dry snow area (blue), along with the (a) NAO, (b) GBI, (c) AMO, and (d) PDO indices (red) from 1979 to 2024. For panels (c,d), the albedo time series are processed with a 9-year running mean. Dashed lines denote the linear trends for each time series. The correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
Figure 9. Time series of summer albedo averaged over the total GrIS (black), dry snow area (yellow), and non-dry snow area (blue), along with the (a) NAO, (b) GBI, (c) AMO, and (d) PDO indices (red) from 1979 to 2024. For panels (c,d), the albedo time series are processed with a 9-year running mean. Dashed lines denote the linear trends for each time series. The correlations labeled with “*” in the time series, are statistically significant at p < 0.05 level.
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Figure 10. Spatial correlations between summer albedo over the GrIS and the (a) NAO, (b) GBI, (c) AMO and (d) PDO Index from 1979 to 2024. The trends in the areas covered by black dots are statistically significant at p < 0.05 level.
Figure 10. Spatial correlations between summer albedo over the GrIS and the (a) NAO, (b) GBI, (c) AMO and (d) PDO Index from 1979 to 2024. The trends in the areas covered by black dots are statistically significant at p < 0.05 level.
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Table 1. Details of each station, including latitude, longitude, elevation, and valid month and the performance of CLARA-A3-SAL product (RMSE and MB). Stations with less than 90% snow/ice cover are highlighted in blue.
Table 1. Details of each station, including latitude, longitude, elevation, and valid month and the performance of CLARA-A3-SAL product (RMSE and MB). Stations with less than 90% snow/ice cover are highlighted in blue.
SeriesStation NameLat (°)Lon (°)Elevation (m)MBRMSERMonth
1CEN77.18−61.121891.8−0.00820.02550.1560
2EGP75.63−35.972667.730.00120.030.7824
3HUM78.53−56.851967.98−0.02390.0386−0.181
4NAE75−29.982627.40.00570.02180.178
5NAU73.84−49.542338.15−0.0020.0611−0.2286
6NEM77.44−51.082454.76−0.01880.031−0.2457
7NSE66.48−42.492388.30.00290.01770.6574
8SDL66−44.52473.120.00340.02160.5478
9SDM63.15−44.822896.26−0.01290.0340.2779
10TUN78.02−33.962078.22−0.04950.0703−0.1281
11CP169.87−47.051951.62−0.02420.05390.1476
12DY266.48−46.32124.57−0.0020.02370.6236
13JAR69.49−49.72907.580.08160.11880.8379
14KAN_L67.09−50.05629.98−0.08980.10140.8441
15KAN_M67.07−48.861264.50.07720.09380.9648
16KAN_U67−47.041845.670.00040.03430.6546
17KPC_L79.91−24.08362.14−0.20550.22770.8641
18KPC_U79.84−25.16867.71−0.18570.20110.7348
19NUK_L64.48−49.56469.950.23010.23620.6548
20NUK_U64.51−49.291113.870.10270.13640.5443
21QAS_L61.03−46.85229.960.00750.08820.5851
22QAS_M61.11−46.81671.88−0.12720.21490.8225
23QAS_U61.18−46.82908.48−0.11170.18040.7444
24SCO_L72.22−26.82437.860.1360.14410.6250
25SCO_U72.39−27.21973.01−0.03430.0680.7549
26SWC69.55−49.381119.920.01210.07320.6788
27TAS_A65.77−38.89876.61−0.20320.22240.7225
28TAS_L65.64−38.9225.97−0.09420.12850.7846
29THU_L76.4−68.27562.910.10650.15250.8838
30THU_U76.39−68.11746.51−0.16430.18040.6143
31UPE_L72.89−54.3199.87−0.0490.06720.9146
32UPE_U72.88−53.63907.970.08830.10670.9246
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Zhang, Y.; Geng, S.; Wang, Y. Satellite-Derived Summer Albedo Variations on the Greenland Ice Sheet from 1979 to 2024 Linked with Climatic Indices. Remote Sens. 2026, 18, 295. https://doi.org/10.3390/rs18020295

AMA Style

Zhang Y, Geng S, Wang Y. Satellite-Derived Summer Albedo Variations on the Greenland Ice Sheet from 1979 to 2024 Linked with Climatic Indices. Remote Sensing. 2026; 18(2):295. https://doi.org/10.3390/rs18020295

Chicago/Turabian Style

Zhang, Yulun, Shang Geng, and Yetang Wang. 2026. "Satellite-Derived Summer Albedo Variations on the Greenland Ice Sheet from 1979 to 2024 Linked with Climatic Indices" Remote Sensing 18, no. 2: 295. https://doi.org/10.3390/rs18020295

APA Style

Zhang, Y., Geng, S., & Wang, Y. (2026). Satellite-Derived Summer Albedo Variations on the Greenland Ice Sheet from 1979 to 2024 Linked with Climatic Indices. Remote Sensing, 18(2), 295. https://doi.org/10.3390/rs18020295

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