Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance-a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier anomalies. However, the dynamics of the Karakoram anomaly, particularly its evolution and persistence, remain insufficiently explored. In this study, we employed Landsat reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 albedo products to developed high-resolution albedo retrieval models using two machine learning (ML) regressions--random forest regression (RFR) and back-propagation neural network regression (BPNNR). The optimal BPNNR model (Pearson correlation coefficient [r] = 0.77-0.97, unbiased root mean squared error [ubRMSE] = 0.056-0.077, RMSE = 0.055-0.168, Bias = -0.149 similar to -0.001) was implemented on the Google Earth Engine cloud-based platform to estimate summer albedo at a 30-m resolution for the Karakoram region from 1990 to 2021. Validation against in-situ albedo measurements on three glaciers (Batura, Mulungutti and Yala Glacier) demonstrated that the model achieved an average ubRMSE of 0.069 (p < 0.001), with RMSE and ubRMSE improvements of 0.027 compared to MODIS albedo products. The high-resolution data was then used to identify firn/snow extents using a 0.37 threshold, facilitating the extraction of long-term firn-line altitudes (FLA) to indicate the glacier dynamics. Our findings revealed that a consistent decline in summer albedo across the Karakoram over the past three decades, signifying a darkening of glacier surfaces that increased solar radiation absorption and intensified melting. The reduction in albedo showed spatial heterogeneity, with slower reductions in the western and central Karakoram (-0.0005-0.0005 yr(-1)) compared to the eastern Karakoram (-0.006 similar to -0.01 yr(-1)). Notably, surge- or advance-type glaciers, avalanche-fed glaciers and debris-covered glaciers exhibited slower albedo reduction rates, which decreased further with increasing glacier size. Additionally, albedo reduction accelerated with altitude, peaking near the equilibrium-line altitude. Fluctuations in the albedo-derived FLAs suggest a transition in the dynamics of Karakoram glaciers from anomalous behavior to retreat. Most glaciers exhibited anomalous behavior from 1995 to 2010, peaking in 2003, but they have shown signs of retreat since the 2010s, marking the end of the Karakoram anomaly. These insights deepen our understanding of the Karakoram anomaly and provide a theoretical basis for assessing the effect of glacier anomaly to retreat dynamics on the water resources and adaptation strategies for the Indus and Tarim Rivers.

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 degrees C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost-vegetation-climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near-surface air temperature in summer (June-August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude- and biome-dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (similar to 30% of climatic signal) on SSO with varying elevation- and canopy height-dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. Climate change has caused substantial vegetation growth that was detected by satellite observations (greening) over northern permafrost regions. However, the consequences or feedbacks of vegetation greening remain largely unknown, hindering the understanding of near-surface thermal dynamics and bringing considerable uncertainty in model projections. Here we aimed to decipher the biophysical impact of permafrost greening on the summer surface offset (SSO), which is an indicator of permafrost degradation. We found latitude- and biome-dependent patterns of SSO changes and divergent responses of SSO to greening. Increases in satellite-observed leaf area index lead to higher SSO in relatively cold and wet areas but lower SSO in warm-dry regions. Biophysical mechanisms associated with surface albedo, evapotranspiration, and SM can help explain various effects of greening on SSO. Our results highlight greening feedbacks on the thermal dynamics of permafrost with climate warming, calling for the improvement of current projections. Vegetation greening impacts the thermal dynamics of permafrost surface Biophysical effects of greening on surface offset could be related to surface albedo, evapotranspiration, and soil moisture

Scientific innovation is overturning conventional paradigms of forest, water, and energy cycle interactions. This has implications for our understanding of the principal causal pathways by which tree, forest, and vegetation cover (TFVC) influence local and global warming/cooling. Many identify surface albedo and carbon sequestration as the principal causal pathways by which TFVC affects global warming/cooling. Moving toward the outer latitudes, in particular, where snow cover is more important, surface albedo effects are perceived to overpower carbon sequestration. By raising surface albedo, deforestation is thus predicted to lead to surface cooling, while increasing forest cover is assumed to result in warming. Observational data, however, generally support the opposite conclusion, suggesting surface albedo is poorly understood. Most accept that surface temperatures are influenced by the interplay of surface albedo, incoming shortwave (SW) radiation, and the partitioning of the remaining, post-albedo, SW radiation into latent and sensible heat. However, the extent to which the avoidance of sensible heat formation is first and foremost mediated by the presence (absence) of water and TFVC is not well understood. TFVC both mediates the availability of water on the land surface and drives the potential for latent heat production (evapotranspiration, ET). While latent heat is more directly linked to local than global cooling/warming, it is driven by photosynthesis and carbon sequestration and powers additional cloud formation and top-of-cloud reflectivity, both of which drive global cooling. TFVC loss reduces water storage, precipitation recycling, and downwind rainfall potential, thus driving the reduction of both ET (latent heat) and cloud formation. By reducing latent heat, cloud formation, and precipitation, deforestation thus powers warming (sensible heat formation), which further diminishes TFVC growth (carbon sequestration). Large-scale tree and forest restoration could, therefore, contribute significantly to both global and surface temperature cooling through the principal causal pathways of carbon sequestration and cloud formation. We assess the cooling power of forest cover at both the local and global scales. Our differentiated approach based on the use of multiple diagnostic metrics suggests that surface albedo effects are typically overemphasized at the expense of top-of-cloud reflectivity. Our analysis suggests that carbon sequestration and top-of-cloud reflectivity are the principal drivers of the global cooling power of forests, while evapotranspiration moves energy from the surface into the atmosphere, thereby keeping sensible heat from forming on the land surface. While deforestation brings surface warming, wetland restoration and reforestation bring significant cooling, both at the local and the global scale.image

Surface albedo is a quantitative indicator for land surface processes and climate modeling, and plays an important role in surface radiation balance and climate change. In this study, by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS), we analyzed the spatiotemporal variation, persistence status, land cover type differences, and annual and seasonal differences of surface albedo, as well as the relationship between surface albedo and various influencing factors (including Normalized Difference Snow Index (NDSI), precipitation, Normalized Difference Vegetation Index (NDVI), land surface temperature, soil moisture, air temperature, and digital elevation model (DEM)) in the north of Xinjiang Uygur Autonomous Region (northern Xinjiang) of Northwest China from 2010 to 2020 based on the unary linear regression, Hurst index, and Pearson's correlation coefficient analyses. Combined with the random forest (RF) model and geographical detector (Geodetector), the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated. The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer. The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south, showing a weak decreasing trend and a small and stable overall variation. Land cover types had a significant impact on the variation of surface albedo. The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation, and negatively correlated with NDVI, land surface temperature, soil moisture, and air temperature. In addition, the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons. To be specific, NDSI had the largest influence on surface albedo, followed by precipitation, land surface temperature, and soil moisture; whereas NDVI, air temperature, and DEM showed relatively weak influences. However, the interactions of any two influencing factors on surface albedo were enhanced, especially the interaction of air temperature and DEM. NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation, with an explanatory power greater than 92.00%. This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Snow-covered regions are the main source of reflection of incident shortwave radiation on the Earth's surface. The deposition of light-absorbing particles on these regions increases the capacity of snow to absorb radiation and decreases surface snow albedo, which intensifies the radiative forcing, leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work, the changes in surface snow albedo and radiative forcing were investigated, induced by light-absorbing particles in the Upper Aconcagua River Basin (Chilean Central Andes) using remote sensing satellite data (MODIS), in situ spectral snow albedo measurements, and the incident shortwave radiation during the austral winter months (May to August) for the 2004-2016 period. To estimate the changes in snow albedo and radiative forcing, two spectral ranges were defined: (i) an enclosed range between 841 and 876 nm, which isolates the effects of black carbon, an important light-absorbing particle derived from anthropogenic activities, and (ii) a broadband range between 300 and 2500 nm. The results indicate that percent variations in snow albedo in the enclosed range are higher than in the broadband range, regardless of the total amount of radiation received, which may be attributed to the presence of light-absorbing particles, as these particles have a greater impact on surface snow albedo at wavelengths in the enclosed band than in the broadband band.

According to the particle size and absorptivity as determined by the fine mode fraction and the single-scattering albedo (SSA) retrievals from AErosol RObotic NETwork (AERONET) sites around the world, aerosols are classified into four key categories: coarse and absorptive aerosol (Type I), mixed aerosol (Type II), fine and absorptive aerosol (Type III), fine and non-absorptive aerosol (Type IV). Seasonal variations of aerosol types with their corresponding direct radiative forcing efficiency (RFE) are observed on different continents. The RFE at the surface (RFEsfc) and top of the atmosphere (RFEtoa) reach their maximum (minimum) values over Asia and North America (Europe, Oceania, and South America) from June to August. The effects of solar zenith angle (SZA), surface albedo (SA), and SSA on RFEsfc and RFEtoa are investigated. The absolute values of RFE at TOA of all types of aerosols are largest at cos(SZA) =0.3 to 0.4. The increased SA reduces the absolute value of RFE both at SFC and TOA for all types of aerosols, and when SA reaches a specific threshold, depending on the type of aerosol, the RFEtoa turns positive. RFEtoa increases while RFEsfc decreases with decreasing SSA. The RFEsfc of the four categories of aerosol varies slightly in the same SZA, SSA and SA conditions, while RFEtoa is aerosol type dependent. It is found that larger particles reflect more solar energy into space per optical depth, resulting in an enhanced cooling effect under similar SZA, SSA, and SA conditions.

Surface albedo exerts substantial control over the energy available for glacier melting. For Urumqi Glacier No.1 in the Tien Shan Mountains, China, represented as a summer accumulation glacier, the variations in albedo driven by surface processes are complex and still poorly understood. In this study, we examined the interannual trends in ablation-period albedo from 2000 to 2021 using MOD10A1 products, evaluated the variation in bare-ice albedo retrieved from 13 end-of-summer Landsat images obtained between 2002 and 2019, and investigated the seasonal variation and diurnal cycle of surface albedo collected near the equilibrium line of the glacier by an AWS from September 2018 to August 2021. During the period of 2000-2021, the average ablation-period albedo presented a slight but not statistically significant downward trend, with a total decrease of 1.87%. Specifically, the decrease in glacier albedo was quicker in July than that in August, and there was a slight increase in May and June. The blackening phenomenon was shown on the east branch glacier, but not on the west branch glacier. For seasonal variability, a bimodal pattern was demonstrated, different from the unimodal seasonal variation in other midlatitude glaciers. The albedo peaks occurred in December and April or May. Under clear sky conditions, the diurnal cycle presented three patterns: a symmetric pattern, an asymmetric pattern, and a progressive decreasing pattern. Air temperature and solid precipitation are the main drivers of variations in glacier albedo, but in different periods of the ablation season, two climate variables affect albedo to varying degrees. The effect of surface albedo reduction enhanced glacier melting by about 20% over the past 20 years. The short-term increase in albedo caused by summer snowfall can considerably reduce glacier melting by as much as 80% in June.

Land surface albedo plays a crucial role in the land surface energy budget and climate. This paper identified the spatiotemporal variations of surface albedo on the Tibetan Plateau (TP) from 1982 to 2015, and quantified the relationships between the spatial and temporal patterns of the albedo and associated influencing factors (snow cover, vegetation, and soil moisture) on the seasonal and interannual basis using satellite products and reanalysis data. It was determined that the albedo presented a distinct spatial variability, with high values in mountainous areas and low values on the southeastern TP. Spatially, average albedo exhibited a positive correlation with snow cover and negative correlations with vegetation and soil moisture. Average albedo over the whole TP had a clear seasonal cycle with a peak in winter and a minimum value in summer, which is dictated by seasonal changes in snow and vegetation covers. Annual average albedo exhibited a weakly downward trend, which was mainly contributed by a significant decrease in summer, pointing to the important role in vegetation dynamics for temporal change of the albedo. On the regional basis, interannual variation of albedo was more responsive to snow cover over the snow-and vegetation-coexisting area than the snow-covered area, and to changes in Normalized Difference Vegetation Index (NDVI) over the vegetation-covered area than the snow-and vegetation-coexisting area; albedo had a weakly negative correlation with soil moisture over bare soil. Furthermore, our results indicated that snow cover was the dominant factor for albedo change on mountainous areas, and vegetation change predominated the variation of albedo on the eastern, southern, and northwestern TP. Specifically, variations in snow cover contributed more than those of vegetation to the interannual albedo variation over the Three Rivers Headwater Region. These results would be beneficial for better understanding the climate and eco-environment changes over the TP. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

We study the statistical relations between the black carbon (BC) content in the atmospheric column and the surface albedo (A), the values of which are available from MERRA-2 reanalysis data for four test areas near the Arctic coast of Russia in April 2010-2016. We also analyze the atmospheric meteorological parameters: air temperature and rainfall and snowfall amounts. The statistical analysis has been carried out using diurnally averaged parameters. An increase in the air temperature is accompanied everywhere by a decrease in the surface albedo, both on a monthly scale and in daily variations. Precipitation in the form of fresh snow increases the surface albedo. On the whole over 7 years, a significant negative correlation between BC andAin April was found in Nenets Autonomous okrug and on the Gydan Peninsula. Separate years (generally diverse for different areas) are revealed when day-to-day variations inAand BC correlate within a month, again with negative coefficients. We estimated possible albedo variations due to changes in different parameters, as well as variations in albedo radiative forcing.