Light-absorbing impurities (LAIs), such as mineral dust (MD), organic carbon (OC), and black carbon (BC), deposited in snow, can reduce snow albedo and accelerate snowmelt. The Ili Basin, influenced by its unique geography and westerly atmospheric circulation, is a critical region for LAI deposition. However, quantitative assessments on the impact of LAIs on snow in this region remain limited. This study investigated the spatial distribution of LAIs in snow and provided a quantitative evaluation of the effects of MD and BC on snow albedo, radiative forcing, and snowmelt duration through sampling analysis and model simulations. The results revealed that the Kunes River Basin in the eastern Ili Basin exhibited relatively high concentrations of MD. In contrast, the southwestern Tekes River Basin showed relatively high concentrations of OC and BC. Among the impurities, MD plays a dominant role in the reduction of snow albedo and has a greater effect on the absorption of solar radiation by snow than BC, while MD is the most important light-absorbing impurity responsible for the reduction in the number of snow-melting days in the Ili Basin. Under the combined influence of MD and BC, the snowmelt period in the Ili Basin was reduced by 2.19 +/- 1.43 to 7.31 +/- 4.76 days. This study provides an initial understanding of the characteristics of LAIs in snow and their effects on snowmelt within the Ili Basin, offering essential basic data for future research on the influence of LAIs on snowmelt runoff and hydrological processes in this region.

The Tibetan Plateau (TP) has experienced accelerated warming in recent decades, especially in winter. However, a comprehensive quantitative study of its long-term warming processes during daytime and nighttime is lacking. This study quantifies the different processes driving the acceleration of winter daytime and nighttime warming over the TP during 1961-2022 using surface energy budget analysis. The results show that the surface warming over the TP is mainly controlled by two processes: (a) a decrease in snow cover leading to a decrease in albedo and an increase in net downward shortwave radiation (snow-albedo feedback), and (b) a warming in tropospheric temperature (850 - 200 hPa) leading to an increase in downward longwave radiation (air warming-longwave radiation effect). The latter has a greater impact on the spatial distribution of warming than the former, and both factors jointly influence the elevation dependent warming pattern. Snow-albedo feedback is the primary factor in daytime warming over the monsoon region, contributing to about 59% of the simulated warming trend. In contrast, nighttime warming over the monsoon region and daytime/nighttime warming in the westerly region are primarily caused by the air warming-longwave radiation effect, contributing up to 67% of the simulated warming trend. The trend in the near-surface temperature mirrors that of the surface temperature, and the same process can explain changes in both. However, there are some differences: an increase in sensible heat flux is driven by a rise in the ground-atmosphere temperature difference. The increase in latent heat flux is associated with enhanced evaporation due to increased soil temperature and is also controlled by soil moisture. Both of these processes regulate the temperature difference between ground and near-surface atmosphere.

This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (similar to 33,000 km(2)) during the presence of BC AARs as compared to similar to 13,000 km(2) during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions. Impact Statement Sea ice protects ice sheets, which are melting at a very high rate to raise the sea level. In addition to global warming, this study is indicative that black carbon aerosols transported from anthropogenic wildfire events, such as from the Amazon, darken the snow, reduce their reflectance, increase the absorption of solar energy incident on the surface, and exacerbate the sea ice retreat. Thus, this study points out that anthropogenic wildfire impacts far away from a region can have a severe impact on sea ice and ice sheets over the Antarctic which has a sea level rise potential of 60 m. Our study shows that only over the Weddell region, sea ice retreat was 20,000 km(2) higher during the presence of BC transport events than other days in 2019.

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.

Tibetan Plateau (TP) is known as the Third Pole of the Earth. Any changes in land surface processes on the TP can have an unneglectable impact on regional and global climate. With the warming and wetting climate, the land surface of the TP saw a darkening trend featured by decreasing surface albedo over the past decades, primarily due to the melting of glaciers, snow, and greening vegetation. Recent studies have investigated the effects of the TP land surface darkening on the field of climate, but these assessments only address one aspect of the feedback loop. How do these darkening-induced climate changes affect the frozen ground and ecosystems on the TP? In this study, we investigated the impact of TP land surface darkening on regional frozen ground and ecosystems using the state-of-the-art land surface model ORCHIDEE-MICT. Our model results show that darkening-induced climate changes on the TP will lead to a reduction in the area of regional frozen ground by 1.1x104 +/- 0.019x104 km2, a deepening of the regional permafrost active layer by 0.06 +/- 0.0004 m, and a decrease in the maximum freezing depth of regional seasonal frozen ground by 0.06 +/- 0.0016 m compared to the scenario without TP land surface darkening. Furthermore, the darkening-induced climate change on the TP will result in an increase in the regional leaf area index and an enhancement in the regional gross primary productivity, ultimately leading to an increase in regional terrestrial carbon stock by 0.81 +/- 0.001 PgC. This study addresses the remaining piece of the puzzle in the feedback loop of TP land surface darkening, and improves our understanding of interactions across multiple spheres on the TP. The exacerbated regional permafrost degradation and increasing regional terrestrial carbon stock induced by TP land surface darkening should be considered in the development of national ecological security barrier.

Glacial changes are crucial to regional water resources and ecosystems in the Sawir Mountains. However, glacial changes, including the mass balance and glacial meltwater of the Sawir Mountains, have sparsely been reported. Three model calibration strategies were constructed including a regression model based on albedo and in-situ mass balance of Muz Taw Glacier (A-Ms), regression model based on albedo and geodetic mass balance of valley, cirque, and hanging glaciers (A-Mr), and degree-day model (DDM) to obtain a reliable glacier mass balance in the Sawir Mountains and provide the latest understanding in the contribution of glacial meltwater runoff to regional water resources. The results indicated that the glacial albedo reduction was significant from 2000 to 2020 for the entire Sawir Mountains, with a rate of 0.015 (10a)- 1, and the spatial pattern was higher in the east compared to the west. Second, the three strategies all indicated that the glacier mass balance has been continuously negative during the past 20 periods, and the average annual glacier mass balance was -1.01 m w.e. Third, the average annual glacial meltwater runoff in the Sawir Mountains from 2000 to 2020 was 22 x 106 m3, and its

Light absorbing particles (LAPs) present high absorbance and contribute to reducing the snow albedo when deposited on snow surfaces. This deposition can be caused by aerosols transported from natural or anthropogenic, either distant or nearby sources. In this study, snow was artificially contaminated with soil samples collected in the Central Andes (near El Yeso dam) to simulate the most common nearby source of Mineral Dust (MD) deposition onto snow surface. Andean soil samples previously conditioned were characterized through Single Particle Optical Sizing (SPOS), X-ray diffraction (XRD) analysis, and Scanning Electron Microscope (SEM) for the determination of optical properties. Spectral snow albedo was measured in situ with a spectroradiometric system. To evaluate the heterogeneity of the particle distribution over the snow surface, aerial photographs were taken with a drone to apply a visual color segmentation of the surface and to determine the equivalent MD concentration. Experimental snow albedo was compared with theoretical values obtained with the OptiPar radiative transfer model. Inputs for the model were: the MD refractive index (calculated from the mineralogical composition and morphology of MD) and particle size, cloudiness, snow density, surface roughness, snow grain size, and LAPs concentration (obtained from the snow samples collected during the experiments and analyzed in the laboratory). Small black carbon concentrations were found in natural snow and considered in the simulations. Spectral albedo measurements showed high albedo reductions in the UV and VIS range (300-800 nm), being less significant in the NIR range (800-1700 nm). A nonlinear behavior was observed in broadband albedo when increasing MD concentration. For lower values of MD concentration (lower than 1500 mg center dot kg (-1) ), a significant albedo reduction rate of 0.1 units per 1000 mg center dot kg (-1) was found, while at higher concentrations (> 3500 mg center dot kg (-1) ), such reduction tends to the minimum. Simulated values with OptiPar are in agreement with measured albedo, but some differences are observed, probably due to the refractive index considered, the snow surface roughness, and the non-uniform MD concentration in snow.

A comprehensive global investigation on the impact of reduction (changes) in aerosol emissions due to Coronavirus disease-2019 (COVID-19) lockdowns on aerosol single scattering albedo (SSA) utilizing satellite observations and model simulations is conducted for the first time. The absolute change in Ozone Monitoring Instrument (OMI) retrieved, and two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated SSA is <4% (<0.04-0.05) globally during COVID (2020) compared to normal (2015-2019) period. Change in SSA during COVID is not significantly different from long-term and year-to-year variability in SSA. A small change in SSA indicates that significant reduction in anthropogenic aerosol emissions during COVID-19 induced lockdowns has a negligible effect in changing the net contribution of aerosol scattering and/or absorption to total aerosol extinction. The changes in species-wise aerosol optical depth (AOD) are examined in detail to explain the observed changes in SSA. Model simulations show that total AOD decreased during COVID-19 lockdowns, consistent with satellite observations. The respective contributions of sulfate and black carbon (BC) to total AOD increased, which resulted in a negligible change in SSA during the spring and summer seasons of COVID over South Asia. Europe and North America experience a small increase in SSA (<2%) during the summer season of COVID due to a decrease in BC contribution. The change in SSA (2%) is the same for a small change in BC AOD contribution (3%), and for a significant change in sulfate AOD contribution (20%) to total AOD. Since, BC SSA is 5-times lower (higher absorption) than that of sulfate SSA, the change in SSA remains the same. For a significant change in SSA to occur, the BC AOD contribution needs to be changed significantly (4-5 times) compared to other aerosol species. A sensitivity analysis reveals that change in aerosol radiative forcing during COVID is primarily dependent on change in AOD rather than SSA. These quantitative findings can be useful to devise more suitable future global and regional mitigation strategies aimed at regulating aerosol emissions to reduce environmental impacts, air pollution, and public health risks.

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016-2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as -4.6 mu g m- 2 day- 1 providing a total deposition of 531 mu g m- 2 during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 - except December 2016) was -39% lower than that of WRFChem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352-854 ng g- 1, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.