Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an R of 0.62 (p < 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of -1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156(th) day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.

Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Precipitation comes in various phases, including rainfall, snowfall, sleet, and hail. Shifts of precipitation phases, as well as changes in precipitation amount, intensity, and frequency, have significant impacts on regional climate, hydrology, ecology, and the energy balance of the land-atmosphere system. Over the past century, certain progress has been achieved in aspects such as the observation, discrimination, transformation, and impact of precipitation phases. Mainly including: since the 1980s, studies on the observation, formation mechanism, and prediction of precipitation phases have gradually received greater attention and reached a certain scale. The estimation of different precipitation phases using new detection theories and methods has become a research focus. A variety of discrimination methods or schemes, such as the potential thickness threshold method of the air layer, the temperature threshold method of the characteristic layer, and the near-surface air temperature threshold method, have emerged one after another. Meanwhile, comparative studies on the discrimination accuracy and applicability assessment of multiple methods or schemes have also been carried out simultaneously. In recent years, the shift of precipitation from solid to liquid (SPSL) in the mid-to-high latitudes of the Northern Hemisphere has become more pronounced due to global warming and human activities. It leads to an increase in rain-on-snow (ROS) events and avalanche disasters, affecting the speed, intensity, and duration of spring snow-melting, accelerating sea ice and glacier melting, releasing carbon from permafrost, altering soil moisture, productivity, and phenological characteristics of ecosystems, and thereby affecting their structures, processes, qualities, and service functions. Although some progress has been made in the study of precipitation phases, there remains considerable research potential in terms of completeness of basic data, reliability of discrimination schemes, and the mechanistic understanding of the interaction between SPSL and other elements or systems. The study on shifts of precipitation phases and their impacts will play an increasingly important role in assessing the impacts of global climate change, water cycle processes, water resources management, snow and ice processes, snow and ice-related disasters, carbon emissions from permafrost, and ecosystem safety.

In this study, we used satellite observations to identify 10 typical dust-loading events over the Indian Himalayas. Next, the aerosol microphysical and optical properties during these identified dust storms are characterized using cotemporal in situ measurements over Mukteshwar, a representative site in Indian Himalayas. Relative to the background values, the mass of coarse particles (size range between 2.5 and 10 mu m) and the extinction coefficient were found to be enhanced by 400% (from 24 +/- 15 to 98 +/- 40 mu g/m3) and 175% (from 89 +/- 57 Mm-1 to 156 +/- 79 Mm-1), respectively, during these premonsoonal dust-loading events. Moreover, based on the air mass trajectory, these dust storms can be categorized into two categories: (a) mineral dust events (MDEs), which involve long-range transported dust plumes traversing through the lower troposphere to reach the Himalayas and (b) polluted dust events (PDEs), which involve short-range transported dust plumes originating from the arid western regions of the Indian subcontinent and traveling within the heavily polluted boundary layer of the Gangetic plains before reaching the Himalayas. Interestingly, compared to the background, the SSA and AAE decrease during PDEs but increase during MDEs. More importantly, we observe a twofold increase in black carbon concentrations and the aerosol absorption coefficient (relative to the background values) during the PDEs with negligible changes during MDEs. Consequently, the aerosol-induced snow albedo reduction (SAR) also doubles during MDEs and PDEs relative to background conditions. Thus, our findings provide robust observational evidence of substantial dust-induced snow and glacier melting over the Himalayas.

Atmospheric particulate matter (PM) as light-absorbing particles (LAPs) deposited to snow cover can result in early onset and rapid snow melting, challenging management of downstream water resources. We identified LAPs in 38 snow samples (water years 2013-2016) from the mountainous Upper Colorado River basin by comparing among laboratory-measured spectral reflectance, chemical, physical, and magnetic properties. Dust sample reflectance, averaged over the wavelength range of 0.35-2.50 mu m, varied by a factor of 1.9 (range, 0.2300-0.4444) and was suppressed mainly by three components: (a) carbonaceous matter measured as total organic carbon (1.6-22.5 wt. %) including inferred black carbon, natural organic matter, and carbon-based synthetic, black road-tire-wear particles, (b) dark rock and mineral particles, indicated by amounts of magnetite (0.11-0.37 wt. %) as their proxy, and (c) ferric oxide minerals identified by reflectance spectroscopy and magnetic properties. Fundamental compositional differences were associated with different iron oxide groups defined by dominant hematite, goethite, or magnetite. These differences in iron oxide mineralogy are attributed to temporally varying source-area contributions implying strong interannual changes in regional source behavior, dust-storm frequency, and (or) transport tracks. Observations of dust-storm activity in the western U.S. and particle-size averages for all samples (median, 25 mu m) indicated that regional dust from deserts dominated mineral-dust masses. Fugitive contaminants, nevertheless, contributed important amounts of LAPs from many types of anthropogenic sources.

Snow algae darken the surface of snow, reducing albedo and accelerating melt. However, the impact of subsurface snow algae (e.g., when cells are covered by recent snowfall) on albedo is unknown. Here, we examined the impact of subsurface snow algae on surface energy absorption by adding up to 2 cm of clean snow to surface algal blooms and measuring reflectivity. Surprisingly, snow algae still absorb significant energy across an array of wavelengths when snow-covered. Furthermore, the scale of this effect correlates with algal cell densities and chlorophyll-a concentrations. Collectively, our results suggest that darkening by subsurface snow algae lowers albedo and thus potentially accelerates snowmelt even when the algae is snow-covered. Impacts of subsurface algae on melt await assessment. This implies that snow algae play a larger role in cryosphere melt than investigations of surface-only reflectance would suggest. IMPORTANCE This study addresses a gap in research by examining the impact of subsurface snow algae on snow albedo, which affects snowmelt rates. Previous studies have focused on visible surface blooms, leaving the effects of hidden algae unquantified. Our findings reveal that snow algae beneath the surface can still absorb energy across various wavelengths, accelerating melt even when not visible to the naked eye. This suggests that spectral remote sensing can detect these hidden algae, although their biomass might be underestimated. Understanding how subsurface snow algae influence albedo and snowmelt is crucial for accurate predictions of meltwater runoff, which impacts alpine ecosystems, glacier health, and water resources. Accurate projections are essential for managing freshwater supplies for agriculture, drinking water, and other vital uses. Thus, further investigation into subsurface snow algae is necessary to improve our understanding of their role in snow albedo reduction and water resource management.

Snow amounts and duration are susceptible to climate change and may significantly affect plant diversity and biomass in grassland ecosystems. Yet, the combined effects of grassland use (type and intensity) and snow depth on plant diversity and productivity remain poorly understood. We established two complementary field experiments to explore the mechanisms driving the effects of grassland use (type and intensity) and snow manipulation on plant diversity and productivity in the meadow steppe. An experiment on grassland use type and snow manipulation showed that lower snow cover in winter reduced soil moisture in the snowmelt period, significantly increased the abundance of ammonia-oxidizing archaea and ammonia-oxidizing bacteria, and initiated nitrification earlier, resulting in the loss of soil available nitrogen, and then reduced the aboveground biomass of early grasses. An experiment on grassland mowing intensity and snow manipulation showed that moderate mowing intensity can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. Stochasticity has played a more important role in plant community assembly in higher intensity of grassland use. Based on our results, we recommend that optimal defoliation height can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. This study has potential benefits for optimizing sustainable production and maintaining ecosystem function under winter snowfall changes in the future across large regions of arid and semiarid grasslands. (c) 2024 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The impact of global climate change and human-induced nitrogen (N) deposition on winter weather patterns will have consequences for soil N cycling and greenhouse gas emissions in temperate deserts. Biological soil crusts (referred to as biocrusts) are crucial communities in soil and significant sources of nitrous oxide (N2O) emission in desert ecosystems and are sensitive to environmental changes. The contribution of bacteria and fungi to N2O production in drylands has been acknowledged. However, the effect of changes in snow cover and N deposition on the N2O production of different microbial groups of microorganisms is not yet clear. In this study, we examine the responses of fungi and bacteria mediated pathways involved in soil N2O production from biocrusts to longterm snow cover manipulation and N addition experiments in the Gurbantunggut Desert. These soils were incubated and subjected to biocide treatments (such as cycloheximide and streptomycin, and fungal and bacterial inhibitors), after which rates of potential nitrification and N2O production were measured. Compared with controls, snow removal treatments from bare sand, lichen crust and moss crust reduced background rates of N2O production by 29.41 %, 26.21 % and 20.49 %, respectively; N2O production rates were 1.53-fold higher in bare sand, 1.38-fold higher in lichen crust, and 1.56-fold higher in moss crust after N addition. The addition of streptomycin significantly reduced the potential nitrification rates of bare sand and biocrusts, indicating that bacteria may be important sources of NO3- production in biocrusts rather than fungi. Conversely, fungi were main sources of N2O production in biocrusts. Additionally, fungi also played a major role in N2O production in biocrusts after snow cover manipulation and N addition. Both snow cover manipulation and N addition treatment indirectly affected the N2O production in biocrusts by considerably affecting the content of substrate N and the abundance of microbial groups. Our research suggests that fungi are main contributors for denitrification in biocrusts, and that snow cover changes (removal snow and double snow) and N addition alter the contribution of biotic pathways responsible for N cycling.

Due to the impact of climate change, significant alterations in snowmelt have already occurred, which have been demonstrated to play a crucial role in photosynthetic carbon sequestration processes in vegetation. However, the effect of changes in snowmelt on light use efficiency (LUE) of grassland remain largely unknown in the permafrost region of Qinghai-Tibetan Plateau (QTP). By utilizing remote sensing data from 2000 to 2017, we conducted an analysis on the spatial and temporal patterns of LUE for various types of permafrost and grassland on the QTP. The LUE of the growing season was 1.1588 g CMJ(-1), displaying variations among different ecosystems: alpine steppe of seasonally frozen ground (ASS) > alpine meadow of seasonally frozen ground (AMS) > alpine meadow of permafrost (AMP) > alpine steppe of permafrost (ASP). Furthermore, our study demonstrated that decreasing snowmelt during the growing season had a negative impact on LUE through meteorological factors, elucidating its influence on LUE for approximately 40.65%, 34.06%, 41.05%, and 32.68% of ecosystems studied. Reduced snowmelt indirectly affects LUE by lowering air temperatures, vapor pressure deficit and solar radiation, while replenishing soil moisture. Additionally, changes in snowmelt can directly affect LUE by reducing the insulating properties of snow cover. Therefore, when estimating gross primary productivity (GPP) using remote sensing data based on LUE, it is essential to consider the impact of snowmelt. This will better represent vegetation phenology's response to climate change.

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.