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.

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in -situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in -situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid -high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon -climate feedbacks in the warming world.

In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 degrees C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from -0.17 degrees C/10a to 0.41 degrees C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900-2014 and 1980-2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014.

Permafrost degradation on the Tibetan Plateau (TP) is anticipated to result in the thaw of permafrost carbon. Existing studies have been conducted to assess the future thaw of frozen carbon on the TP, primarily focusing on the deepening of the active layer while neglecting the impact of permafrost area shrinkage. This oversight may lead to a significant underestimation of the potential thaw of frozen carbon. Our research underscores the pivotal role of permafrost area shrinkage in estimating the future thaw of frozen carbon. Our findings reveal that when the combined effects of permafrost area shrinkage and active layer deepening are considered, the thaw rates of frozen carbon in various radiative forcing scenarios are nearly four times those based on active layer deepening alone. Notably, our results demonstrate substantial thaw of frozen organic carbon in the TP permafrost area under all four future scenarios: In the low radiative forcing scenario SSP1-2.6, it is predicted that 55.4 % of the organic carbon in the permafrost area 0-10 m soils will be in a state of thaw by 2100, and more than 90 % in the high radiative forcing scenario SSP5-8.5. This substantial thaw is poised to diminish the TP's current carbon sink function significantly. Our study emphasizes that as global warming persists, frozen carbon in permafrost areas will play a more active role in global carbon cycle processes in the future. Furthermore, we stress the importance of considering permafrost area shrinkage in understanding the thaw of frozen carbon, providing valuable insights for carbon balance studies on the TP.

Many studies have focused on elevation-dependent warming (EDW) across high mountains, but few studies have examined both EDW and LDW (latitude-dependent warming) on Antarctic warming. This study analyzed the Antarctic amplification (AnA) with respect to EDW and LDW under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) during the period 2015-2100. The results show that the AnA appears under all socioeconomic scenarios, and the greatest signal appears in austral autumn. In the future, Antarctic warming is not only elevation-dependent, but also latitude-dependent. Generally, positive EDW of mean temperature (T-mean), maximum temperature (T-max) and minimum temperature (T-min) appear in the range of 1.0-4.5 km, and the corresponding altitudinal amplification trends are 0.012/0.012/0.011 (SSP1-2.6)- 0.064/0.065/0.053 (SSP5-8.5) degrees C decade(-1)center dot km(-1). Antarctic EDW demonstrates seasonal differences, and is strong in summer and autumn and weak in winter under SSP3-7.0 and SSP5-8.5. For T(mea)n, T-max and T-min, the feature of LDW is varies in different latitude ranges, and also shows seasonal differences. The strongest LDW signal appears in autumn, and the warming rate increases with increasing latitude at 64-79 degrees S under SSP1-2.6. The similar phenomenon can be observed at 68-87 degrees S in the other cases. Moreover, the latitude component contributes more to the warming of T-mean and T-max relative to the corresponding altitude component, which may relates to the much larger range of latitude (similar to 2600 km) than altitude (similar to 4.5 km) over Antarctica, while the EDW contributes more warming than LDW in the changes in T-min in austral summer. Moreover, surface downwelling longwave radiation, water vapor and latent heat flux are the potential factors influencing Antarctic EDW, and the variation in surface downwelling longwave radiation can also be considered as an important influencing factor for Antarctic LDW. Our results provide preliminary insights into EDW and LDW in Antarctica.

The Tibetan Plateau (TP) is the largest permafrost distribution zone at high-altitude in the mid-latitude region. Climate change has caused significant permafrost degradation on the TP, which has important impacts for the eco-hydrological processes. In this study, the frost number is utilized to calculate the frost number (F) based on the air freezing/thawing index obtained from the downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets. A novel method is proposed to determine the frost number threshold (Ft) for diagnosing permafrost distribution. Then the simulated permafrost distribution maps are compared with the existing permafrost distribution map, employing the Kappa coefficient as the measure of classification accuracy to identify the optimal Ft. Finally, the permafrost distribution on the TP under different Shared Socio-economic Pathways (SSP) scenarios are diagnosed with the optimal Ft. Simulation results demonstrate that across all scenarios, the rates of permafrost degradation during the mid-future period (2040-2060) remain comparable to those observed in the baseline period (2000), ranging from 33% +/- 3% to 53% +/- 4%. Conversely, during the far-future (2080-2099), the permafrost degradation rates display significant variation across different scenarios, ranging from 37% +/- 4% to 96% +/- 3%. The profound impacts of permafrost degradation on the TP are reflected in decreasing trends in soil moisture and runoff, as well as a slower increasing trend in Normalized Difference Vegetation Index (NDVI) compared to other regions, indicating negative impacts on vegetation growth. The Tibetan Plateau, the highest plateau in the world and the largest high-altitude permafrost region, is experiencing permafrost degradation due to climate change, significantly impacting eco-hydrological processes in this region. In this study, we used the frost number model with air temperature to simulate the distribution of permafrost on the Tibetan Plateau under different scenarios. The results show that permafrost on the Tibetan Plateau is projected to degrade in the 21st century, especially under high-emission scenarios. The degradation of permafrost will likely reduce soil moisture and runoff. Additionally, vegetation growth in areas with permafrost degradation is expected to be slow. These findings are of great significance for understanding permafrost changes on the Tibetan Plateau and their impacts on eco-hydrological processes. A new method using the frost number model with Kappa coefficient is proposed to diagnose permafrost distributionPermafrost on the Tibetan Plateau will experience the least degradation (33% +/- 3%) under SSP126, and the most (96% +/- 3%) under SSP585 in 2080-2099Permafrost degradation on the Tibetan Plateau is anticipated to reduce soil moisture and runoff, adversely affecting vegetation growth

Permafrost degradation on the Tibetan Plateau (TP) will significantly affect local water cycle processes, downstream water ecology, and water security. In this study, we evaluate the long-term interannual dynamics of permafrost distribution and active layer thickness (ALT) on the TP based on historical data from Climatic Research Unit gridded Time Series (CRU TS) downscaling and projected data under four shared socio-economic pathways (SSPs) in Scenario Model Intercomparison Project (ScenarioMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP 6). To achieve this, we employ a data-driven scheme at 1 km resolution for both historical and future periods (1901-2100) that compares the performance of four machine learning algorithms to select the optimal algorithm for permafrost distribution and ALT simulations. Our results indicate that the permafrost on the TP has been undergoing degradation in both historical and future periods, with a decrease in permafrost area and an increase in ALT. The changing rates of permafrost area and regionally averaged ALT during the historical period (1901-2020) are -1.05 x 104 km2 decade-1 and 0.012 m decade-1, while an accelerated degradation is observed after the 1970 s (with changing rates of permafrost area and regionally average ALT of -3.62 x 104 km2 decade-1 and 0.055 m decade-1). Our results also suggested that permafrost degradation on the TP will continue in the future under the four SSP scenarios. The individual global climate models (GCMs) exhibit a consistent degradation trend but great uncertainty in degradation speed. The ensemble mean of simulations across 15 selected GCMs showed that the degradation percentage of permafrost area on the TP under scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 was 26.0 +/- 6.8 %, 50.4 +/- 5.6 %, 79.2 +/- 4.5 %, and 89.0 +/- 4.0 % by 2100, and the regionally average ALT increased by 0.301 +/- 0.112 m, 0.628 +/- 0.113 m, 1.204 +/- 0.119 m, and 1.486 +/- 0.125 m, respectively. We also analyze permafrost stability and elevationdependent changes of ALT on the TP. The permafrost stability increases with elevation and latitude, and ALT changes more intensely with increasing elevation. This study will provide valuable data for hydrological and ecological studies related to permafrost on the TP.

In the context of global warming, the soil freeze depth (SFD) over the Tibetan Plateau (TP) has undergone significant changes, with a series of profound impacts on the hydrological cycle and ecosystem. The complex terrains and high elevations of the TP pose great challenges in data acquisition, presenting difficulties for studying SFD in this region. This study employs Stefan's solution and downscaled datasets from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the future SFDs over the TP. The changing trends of the projected SFDs under different Shared Socio-economic Pathways (SSP) scenarios are investigated, and; the responses of SFDs to potential climatic factors, such as temperature and precipitation, are analyzed. The potential impacts of SFD changes on eco-hydrological processes are analyzed based on the relationships between SFDs, the distribution of frozen ground, soil moisture, and the Normalized Difference Vegetation Index (NDVI). Results show that the projected SFDs of the TP are estimated to decrease at rates of 0.100 cm/yr under the SSP126, 0.330 cm/yr under the SSP245, 0.565 cm/yr under the SSP370, and 0.750 cm/yr under the SSP585. Additionally, the SFD decreased at a rate of 0.160 cm/yr during the historical period from 1950 to 2014, which was between the decreasing rates of the SSP126 and SSP245 scenarios. The projected SFDs are negatively correlated with air temperature and precipitation, more significant under the higher emissions scenario. The projected decrease in SFDs will significantly impact eco-hydrological processes. A rapid decrease in SFD may lead to a decline in soil moisture content and have adverse impacts on vegetation growth. This research provides valuable insights into the future changes in SFD on the TP and their impacts on eco-hydrological processes.

In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 degrees C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from -0.17 degrees C/10a to 0.41 degrees C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900-2014 and 1980-2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014.

Aerosol-cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX-GDDP, a multimodel ensemble of high-resolution (0.25 degrees) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX-GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models. We investigate the impacts of aerosol on India's wet and dry climate. High-resolution downscaled CMIP5 models were used to calculate extreme indices like CDD (consecutive dry days), CWD (consecutive wet days), SDII (precipitation intensity). From the group of 22 models, 12 reliable models were chosen based on their fidelity to the observations. Amongst the reliable models, certain models incorporate only aerosol-radiation interaction (REMADE), while others have both aerosol-radiation and aerosol-cloud interaction (REMALL). We found that the simulated trends in the REMAll were similar to the observed trends. In the current period (1975-2005), the aerosol-cloud interactions led to the reduction in rainfall (both frequency and intensity wise) and enhanced the dry days, however in the future projections, the reduction in aerosol emissions leads to a wetter climate (increase in wet days and rainfall intensity) over India.