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.

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March-30th June) in all 22 years. We tested for long-term trend significance with the Mann-Kendall test and trend magnitude with Theil-Sen's slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%-8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Permafrost in High Mountain Asia (HMA) is becoming increasingly vulnerable to thaw due to climate change. However, the lack of either in situ ground surface or borehole temperature data beyond the Tibetan Plateau prevents comprehensive assessments of its impact on the regional hydrologic cycle and local cascading hazards. Although past studies have generated estimates of permafrost extent in Central Asia, many are limited to the Tibetan Plateau, excluding the more remote reaches of the Tien Shan, Pamirs, and Himalayas. By leveraging surface temperatures from both the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infra-Red Sounder (AIRS), this study advances further understanding of remotely sensed permafrost occurrence at high altitudes, which are prone to error due to frequent cloud cover. We demonstrate that the fusion of MODIS and AIRS products can accurately estimate long-term thermal regimes of the subsurface, with reported correlation coefficients of 0.773 and 0.560, RMSEs of 0.890 degree celsius and 0.680 degree celsius, and biases of 0.003 degree celsius and 0.462 degree celsius, respectively, for the ground surface and the depth of zero annual amplitude, during a reference period of 2003-2016. Furthermore, we provide a range of possible permafrost extents based on established equations for calculating the temperature at the top of the permafrost to demonstrate temperature sensitivity to soil moisture and snow cover. The MODIS-AIRS product is recommended to be a robust source of ground temperature estimates, which may be sufficient for inferring mountain permafrost presence in HMA. Incorporating the influence of soil moisture and snow depth, although limited by biased estimates, also produces estimates of permafrost regional areas comparable to previously reported permafrost indices. A total permafrost area of 1.69 (+/- 0.32) million km(2) is estimated for the entire HMA, across 15 mountain subregions.

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth's three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth's three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001-2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of -0.72. The polar amplification for the Earth's three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of -1.68 and -2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth's three poles. The Earth's three poles are experiencing drastic changes, and the potential influence of climate change should receive attention.

This study assesses the physical and optical properties and estimated the radiative forcing of aerosol at Agra over the Indo-Gangetic Basin (IGB) during July 2016-December 2019 using black carbon (BC) mass concentration (AE-33 aethalometer), data sets from satellite and model simulations. The optical properties of aerosol and radiative forcing have been measured by the Optical and Physical Properties of Aerosols and Clouds (OPAC) and Santa Barbara Discrete Ordinate Radiative Transfer Atmospheric Radiative Transfer (SBDART) model. The high BC mass concentration has been observed in November and lowest in August. An adverse meteorological condition due to a combination of temperature and low wind speed results in poor dispersion in the wintertime is a common factor for high concentration level pollutants over Agra. The diurnal and temporal cycle of BC mass concentration exhibits a high concentration at nighttime due to the lower atmospheric boundary layer. The seasonal variation of absorption coefficient (& beta;abs) and Absorption Angstrom Exponent (AAE) is found to be higher during post-monsoon and lowest in monsoon season. This suggests that black carbon concentration over Agra is mainly generated from crop burning, waste burning, automobile exhaust and long-range transport from Punjab and Haryana as the present site is downwind. OPAC-derived aerosol optical depth (AOD), single-scattering albedo (SSA), Angstrom Exponent (AE) and asymmetry parameter (AsyP) were estimated to be 0.57 & PLUSMN; 0.07, 0.78 & PLUSMN; 0.16, 0.99 & PLUSMN; 0.21 and 0.81 & PLUSMN; 0.15, respectively. AOD and AE from the OPAC and the moderate resolution imaging spectroradiometer (MODIS) have shown the consistent relationship. The mean radiative forcing is 18.3 & PLUSMN; 2.1 W m-2 at the top of the atmosphere while, at the surface, net radiative forcing is -42.4 & PLUSMN; 7.2 and 59.1 & PLUSMN; 6.5 W m-2 at the atmosphere during the study period. Vertical profiles were estimated using the observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the change in heating rate from the SBDART model over Agra. First-time short-lived climate forcer black carbon mass concentration along with optical properties of aerosols has been reported, and quantification of radiative forcing has been done at the Agra region.The radiative forcing due to black carbon has been found to be high highlighting the heat risk over this region.image

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.

This article investigates the snow albedo changes in Colombian tropical glaciers, namely, Sierra Nevada de Santa Marta (SNSM), Sierra Nevada del Cocuy (NSC), Nevado del Ruiz (NDR), Nevado Santa Isabel (NDS), Nevado del Tolima (NDT), and Nevado del Huila (NDH). They are associated with the possible mineral dust deposition from the Sahara Desert during the June and July months using snow albedo (SA), snow cover (SC), and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. And mineral dust (MD) from The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), both of them during 2000-2020. Results show the largest snow albedo reductions were observed at 39.39%, 32.1%, and 30.58% in SNC, SNSM, and NDR, respectively. Meanwhile, a multiple correlation showed that the glaciers where MD contributed the most to SA behavior were 35.4%, 24%, and 21.4% in NDS, NDC, and NDR. Results also display an increasing trend of dust deposition on Colombian tropical glaciers between 2.81 x 10-3 & mu;g & BULL;m-2 & BULL;year-1 and 6.58 x 10-3 & mu;g & BULL;m-2 & BULL;year-1. The results may help recognize the influence of Saharan dust on reducing snow albedo in tropical glaciers in Colombia. The findings from this study also have the potential to be utilized as input for both regional and global climate models. This could enhance our comprehension of how tropical glaciers are impacted by climate change.

With global warming accelerating, polar amplification is one of the hot issues in climate research. However, most studies focus on Arctic amplification, and little attention has been paid to Antarctic amplification (AnA), and there is no relevant research based on MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature observations. Compared with 128 stations' observations, MODIS can capture the variations in temperature over Antarctica. In addition, the temperature changes in Antarctica, East Antarctica, West Antarctica and the Antarctic Peninsula during the period 2001-2018 reflected by the MODIS and ERA5 are basically consistent, and the temperature changes in Antarctica are negatively correlated with the Southern Annular Mode. AnA occurs under all annual and seasonal scales, with an AnA index greater than 1.27 (1.31) from the MODIS (ERA5), and is strongest in the austral winter and weakest in summer. AnA displays regional differences, and the signal from the MODIS is similar to that from ERA5. The strongest amplification occurs in East Antarctica, with an AnA index greater than 1.45 (1.48) from the MODIS (ERA5), followed by West Antarctica, whereas the amplified signal is absent at the Antarctic Peninsula. In addition, seasonal differences can be observed in the sub regions of Antarctica. For West Antarctica, the greatest amplification appears in austral winter, and in austral spring for East Antarctica. The AnA signal also can be captured in daytime and nighttime observations, and the AnA in nighttime observations is stronger than that in daytime. Generally, the MODIS illustrates the appearance of AnA for the period 2001-2018, and the Antarctic climate undergoes drastic changes, and the potential impact should arouse attention.

For the period 2001-2020, the interannual variability of the normalized difference vegetation index (NDVI) is investigated in connection to Indian summer monsoon rainfall (ISMR). According to Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, the ISMR and the vegetative activity of the Indo-Gangetic plain (IGP) in the month of January show a significant negative association. We hypothesized that the January vegetation state affects the ISMR via a delayed hydrological response, in which the wet soil moisture anomaly formed throughout the winter to accommodate the water needs of intensive farming influences the ISMR. The soil moisture anomalies developed in the winter, particularly in the root zone, persisted throughout the summer. Evaporative cooling triggered by increasing soil moisture lowers the summer surface temperature across the IGP. The weakening of monsoon circulation as a result of the reduced intensity of land-sea temperature contrast led in rainfall suppression. Further investigation shows that moisture transport has increased significantly over the past two decades as a result of increasing westerly over the Arabian Sea, promoting rainfall over India. Agriculture activities, on the other hand, have resulted in greater vegetation in India's northwest and IGP during the last two decades, which has a detrimental impact on rainfall processes. Rainfall appears to have been trendless during the last two decades as a result of these competing influences. With a lead time of 5 months, this association between January's vegetation and ISMR could be one of the potential predictors of seasonal rainfall variability.