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The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change. [ABSTRACT FROM AUTHOR]

Mountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated and compared four snow depth datasets over the Tianshan Mountains, including snow depth datasets from ERA5, ERA5‐Land, passive microwave, as well as a dynamically downscaled simulation by Weather Research and Forecasting (WRF) model. The snow depth was evaluated against in situ observations while the snow cover extent was evaluated by interactive multisensor snow and ice mapping system (IMS) snow cover. Furthermore, the snow‐related metrics, such as annual mean snow depth and snow cover days, were compared among the four datasets. The results showed that the spatial patterns of snow‐related metrics were relatively consistent among datasets, although discrepancies existed in magnitude. Additionally, the temporal variations in snow‐related metrics depended on the dataset employed. The simulated snow depth from WRF got the lowest bias in daily snow depth value against the in situ observations (mean bias = 0.12 cm, root mean square error = 2.08 cm), additionally, it had the best performance in classifying correct snow grids compared with IMS (probability of detection = 0.766). It also outperformed in annual mean snow depth and snow cover days by 47.2 and 13.3% compared with ERA5‐Land snow depth. This study highlights the confidence in characterizing both the spatial pattern and temporal variations of snow depth products based on WRF model simulation. [ABSTRACT FROM AUTHOR]

Snow is a key variable that influences hydrological and climatic cycles. Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes. However, there are still uncertainties in modeling snow resources over complex terrain such as mountains. This study employed the National Center for Atmospheric Research's Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains. Six tests were conducted based on different reanalysis forcing datasets and different land surface properties. The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests. The model produced a low bias in snow depth and snow water equivalent (SWE) regardless of the forcing datasets. Additionally, the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters. However, no significant improvement in accuracy was found in the date of snow depth maximum and melt rate. The best performance was achieved using ERA5 with modified land cover and vegetation parameters (mean bias = -4.03 mm and -1.441 mm for snow depth and SWE, respectively). This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains. [ABSTRACT FROM AUTHOR]

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas. [Display omitted] • Cold season precipitation from climate model exhibited better performance than non-model in the high-elevation regions. • CPM precipitation showed higher accuracy than gray-zone resolution simulations and ERA5-land in the high-elevation regions. • SD simulation driven by CPM (WRF-Morrison) precipitationhad the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day). [ABSTRACT FROM AUTHOR]

The patterns of snow accumulation and ablation were largely influenced by climate change. Current studies on the snow cover characteristics were mainly based on remote sensing retrievals in the Tianshan Mountains, however, they suffered from short record observations. This study quantified the climatology and variations in snowpack patterns related to snow depth from 1961 to 2015 at 48 meteorological stations across the Tianshan Mountains, spanning the elevations from 312 to 3543 m asl. Moreover, this study analyzed the contribution of the air temperature and precipitation during snow accumulation season to snow onset day, as well as the contribution of effective daily >0 °C accumulative temperature and snow depth maximum during snowmelt season to snow end day, respectively. The results showed that the snow depth maximum and snow cover days exhibited similar distribution, i.e., decreasing pattern in a northwest-to-southeast direction. Significant trend toward a shortened snow cover days was identified by 2.7 days decade−1 at the high altitude of the north slope, although it exhibited a non-significant increasing trend in low-middle altitude regions. A higher snow depth maximum was observed mainly in the north slope by 2.08 cm decade−1 without any significant elevation-dependency. There existed a positive sensitivity of snow onset day to air temperature with 0.64 days °C−1 during snow accumulation season, but an opposite sensitivity of snow end day to effective accumulative temperature with 0.82 days °C−1 during snow melt season. In addition, a positive sensitivity of snow end day to snow depth maximum was revealed with 1.84 days cm−1 during snowmelt season. • Significant trend toward a shortened snow cover days was identified by 2.7 days decade−1 at the high altitude of the north slope during 1961–2015 period. • A higher snow depth maximum was observed mainly in the north slope by 2.08 cm decade−1 during 1961–2015 period. • Opposite sensitivities were found between snowpack and temperature during different snow seasons. [ABSTRACT FROM AUTHOR]

The study of parameterization schemes is important for land surface models (LSMs) due to the uncertainties existed in simulating snow. However, previous studies have mainly assessed the qualitative sensitivity of parameterization combinations on snow simulations in LSMs. To better understand the parameterization schemes and reduce the model uncertainties in simulating snow depth, 24 ensemble experiments were conducted in this study using the Weather Research and Forecasting (WRF) model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP) model based on four physical subprocesses: snow surface albedo (ALB); precipitation partitioning between rain and snowfall (SNF); snow/soil temperature time (STC); surface layer turbulence (SFC) schemes. Additionally, the sensitivities of the four parameterizations in simulating the snow depth were quantitatively assessed. The results showed that the WRF/Noah-MP model underestimated snow depth, with underestimation in the snow accumulation season and overestimation in the early snow accumulation and snow ablation seasons. The Sobol sensitivity revealed the dominant role of the ALB and STC subprocesses in the simulation of the snow depth. The sensitivities of the four physical subprocesses varied in the different snow seasons and at different altitudes. Additionally, all the subprocesses exhibited interactions when simulating the snow depth in the Tianshan Mountains in this study, except for the ALB in the snow ablation season and the STC at >1000 m. Distinct differences existed in the snow depth simulations using the different selected schemes with the ALB, STC, and SFC subprocess parameterization. Comparison of the parameterization schemes of the four subprocesses revealed that the ALB, STC, and SFC subprocess parameterizations mainly affected the snow depth simulation through the heat fluxes, while the SNF affected it through the air temperature. • Parameterizations sensitivity was quantitatively assessed by Sobol analysis using WRF/Noah-MP model over the Tianshan Mountains. • The dominant roles of the ALB and STC subprocesses in the simulation of the snow depth were identified compared with the SNF and SFC subprocesses. • The sensitivities of STC and ALB varied with different altitudes and snow season. • Interactions were found among all of the subprocesses in simulating snow depth in the Tianshan Mountains. [ABSTRACT FROM AUTHOR]

Snowfall is a critical component of the hydrological cycle and water resources supply in mountainous areas. The snowfall regime varies according to time and space, and its uncertainties exist under climate change conditions. It is a necessary process to monitor the moisture characteristics of snowfall so as to investigate the information on snowfall, but a separate study on the snow moisture characteristics did not sufficiently describe over the Tianshan Mountains (TS). This study investigated the spatiotemporal variability of snowfall in the TS during the cold season from 1982 to 2018 based on the Weather Research and Forecasting (WRF) model simulations and analyzed moisture characteristics of snowfall using the regional moisture budget analysis. The results demonstrated that the snowfall amounted to 84.53 mm in the TS during the cold season and experienced a slight increase (P > 0.05). A significant increase in snowfall was noticed in the Eastern TS and the high-altitude regions. Western Eurasia and the Arabian Sea were primary moisture sources of snowfall, and the abundant moisture was transported by the mid-latitude westerly and the southwesterly to the interior. The anomalous cyclone over Europe transported more moisture flux to Central Asia. Meanwhile, the anomalous cyclone of Mongolia and the anticyclone in southwestern Central Asia caused a declining moisture divergence in the TS and an increase of the net moisture budget, which resulted in a snowfall and snow mass augmentation. The obtained study outcomes might improve understanding of snowfall information and provide a scientific reference for water resource management under a warming climate. • Snowfall amounted to 84.53 mm in the Tianshan Mountains during the cold season and experienced a slight increase. • Western Eurasia and the Arabian Sea were primary moisture sources of snowfall, and the abundant moisture was transported by the mid-latitude westerly and the southwesterly to the interior. • A declining moisture divergence in the Tianshan and an increase of the net moisture budget resulted in a snowfall and snow mass augmentation. [ABSTRACT FROM AUTHOR]

• The real-time key vegetation parameters were updated in the WRF/Noah-MP model in the Tianshan mountains. • More realistic vegetation parameters could improve the performance of snow simulation in the WRF/Noah-MP, especially in the forest regions. The snowpack evolution has a significant impact on the water cycle and energy exchange at the watershed and regional scales, especially in the mountainous area with complex topography and land surface properties. An accurate description of the vegetation parameters for the regional climate model (RCM) coupled with the land surface model (LSM) is necessary to achieve a more accurate simulation of the mountainous snow process. However, the default vegetation options could not update real-time in the RCM LSMs, causing large uncertainties in the snow mass estimation. Thus, this study investigated the effect of the key vegetation parameters on the snow simulation in the Tianshan Mountains (TS) through real-time updates with remotely sensed leaf area index (LAI), green vegetation fraction (FVEG) and land cover (LC) products in the Weather Research and Forecasting (WRF) model coupled with the Noah LSM with Multiparameterization Options (Noah-MP). The results demonstrated that more realistic vegetation parameters could improve the performance of snow simulation in the WRF/Noah-MP, especially in the forest regions. The underestimated vegetation parameters of the integrated remote sensing products caused an increased surface albedo and less snow interception, particularly in the snow ablation period, and less vegetation density could also reduce the net longwave radiation emitted from the canopy at the surface, causing a lower near-surface temperature and less snowmelt. Additionally, less snow interception and melted snow contributed to a larger snow water equivalent on the ground, such as in the Western TS and the high-altitude regions of the Ili Valley. The updating vegetation parameters' approach will help to provide information so as to accurately model the snow resources in the mountainous areas. [ABSTRACT FROM AUTHOR]

More extreme precipitation is expected to occur in the arid and wet regions due to climate warming. This intensification will increase the risk of floods, soil erosion, and droughts in the world's dry regions. Exploring the precipitation's irregularity and the daily precipitation concentration in Central Asia (CA) has significant implications for regional agricultural development and ecosystem services. However, the irregularity of precipitation was poorly understood throughout the entire CA due to the sparse meteorological observations. Using the daily Multi-Source Weighted-Ensemble Precipitation (MSWEP-V2.2) from 1979 to 2016 across CA, this study investigated the spatiotemporal variations of the daily precipitation concentration index (CI) and the precipitation diversity and analyzed their attributes and implications. The results demonstrated that the CI was significantly negatively correlated with the precipitation frequency and amount. The distribution of the precipitation frequency diversity (H fre) and the precipitation amount diversity (H amount) showed an opposite pattern with the CI, implying that the low H fre and H amount values were corresponding to a high precipitation concentration. The high CI values were found in the Tarim Basin and near the Aral Sea, revealing a strong irregularity in precipitation. The decrease regarding the light precipitation frequency or increase in the torrential precipitation frequency generated the increasing precipitation frequency diversity, corresponding to a widespread significant reduction of the CI in CA. The decreased CI and increased precipitation diversity over past decades were likely related to the warming phase of the Atlantic Multidecadal Oscillation. The shift from light to extreme precipitation will trigger the risk of flooding and soil erosion and bring about big challenges for water resources management in the arid and semi-arid regions. • The high daily precipitation index values were observed in the regions of Aral Sea and southeastern Tarim Basin. • The opposite distribution were found between the precipitation diversity and daily precipitation index. • The increase in precipitation frequency diversity caused the decrease in daily precipitation concentration. • The AMO has a significantly negative influence on the daily precipitation concentration at annual scale. [ABSTRACT FROM AUTHOR]

• The effects of land-use change on fractions and stability of SOC were investigated. • LPI in topsoil and subsoil showed opposite responses to land-use change. • LPII and RP stocks declined under cultivation but increased following afforestation. • SOC stability decreased after cultivation and increased after afforestation. • Cropland afforestation contributed to soil restoration by sequestering SOC. Soil organic matter is a heterogeneous and complex entity that consists of a diverse range of compounds. However, the responses of soil organic carbon (SOC) fractions with different biochemical stabilities to land-use change are inadequately addressed. In this study, soil samples (0–60 cm) were collected with a 10 cm interval from grassland (GS), cropland (CS), woodland (WS), and orchard (OS) using paired-site approach in a typical semi-arid agro-pastoral ecotone in the Ili River Valley, Central Asia, to: (1) clarify the vertical changes in soil labile (LPI and LPII) and recalcitrant C pools (RP) after GS conversion to CS and CS conversion to WS and OS; and to (2) evaluate the impact of land-use change on SOC stability. The results indicated that LPI stocks in topsoil (0–30 cm) and subsoil (30–60 cm) showed opposite responses to land-use change. In contrast, LPII and RP stocks in both soil layers significantly decreased after conversion of GS to CS, and significantly increased after CS afforestation. These results demonstrated that RP in subsoils could also be altered by land-use change. In general, conversion from GS to CS decreased the recalcitrance index of SOC (RI SOC), which increased after conversion from CS to WS. The results implied that cultivation decreased the stability of SOC, causing the depletion of SOC stock, whereas CS conversion to WS enhanced the stability of SOC, promoting SOC sequestration. The negative correlations between RI SOC , pH, and electrical conductivity (EC 1:5) suggested that soil pH and salinity were potential indicators reflecting the biochemical recalcitrance of SOC. Since both soil pH and EC 1:5 showed decreasing trends after conversion from CS to WS and OS, the results suggested that afforestation on CS contributed to mitigate soil salinization while promoting SOC sequestration in this semi-arid agro-pastoral ecotone. [ABSTRACT FROM AUTHOR]