Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Air pollution is a global health issue, and events like forest fires, agricultural burning, dust storms, and fireworks can significantly worsen it. Festivals involving fireworks and wood-log fires, such as Diwali and Holi, are key examples of events that impact local air quality. During Holi, the ritual of Holika involves burning of biomass that releases large amounts of aerosols and other pollutants. To assess the impact of Holika burning, observations were conducted from March 5th to March 18th, 2017. On March 12th, 2017, around 1.8 million kg of wood and biomass were openly burned in about 2250 units of Holika, located in and around the Varanasi city (25.23 N, 82.97 E, similar to 82.20 m amsl). As the Holika burning event began the impact on the Black Carbon (BC), particulate matter 10 & 2.5 (PM10 and PM2.5), sulphur dioxide (SO2), oxides of nitrogen (NOx), ozone (O-3) and carbon monoxide (CO) concentration were observed. Thorough optical investigations have been conducted to better comprehend the radiative effects of aerosols produced due to Holika burning on the environment. The measured AOD at 500 nm values were 0.315 +/- 0.072, 0.392, and 0.329 +/- 0.037, while the BC mass was 7.09 +/- 1.78, 9.95, and 7.18 +/- 0.27 mu g/m(3) for the pre-Holika, Holika, and post-Holika periods. Aerosol radiative forcing at the top of the atmosphere (ARF-TOA), at the surface (ARF-SUR), and in the atmosphere (ARF-ATM) are 2.46 +/- 4.15, -40.22 +/- 2.35, and 42.68 +/- 4.12 W/m(2) for pre-Holika, 6.34, -53.45, and 59.80 W/m(2) for Holika, and 5.50 +/- 0.97, -47.11 +/- 5.20, and 52.61 +/- 6.17 W/m(2) for post-Holika burning. These intense observation and analysis revealed that Holika burning adversely impacts AQI, BC concentration and effects climate in terms of ARF and heating rate.

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.

Global warming leads to the melting of permafrost, affects changes in soil microbial community structures and related functions, and contributes to the soil carbon cycle in permafrost areas. Located at the southern edge of Eurasia's permafrost region, the Greater Khingan Mountains are very sensitive to climate change. Therefore, by analyzing the bacterial community structure, diversity characteristics, and driving factors of soil profiles (active surface layer, active deep layer, transition layer, and permafrost layer) in this discontinuous permafrost region, this research provides support for the study of the carbon cycling process in permafrost regions. The results show that the microbial diversity (Shannon index (4.81)) was the highest at 0-20 cm, and the Shannon index of the surface soil of the active layer was significantly higher than that of the other soil layers. Acidobacteria and Proteobacteria were the dominant bacteria in the active layer soil of the permafrost area, and Chloroflexi, Actinobacteria, and Firmicutes were the dominant bacteria in the permafrost layer. Chloroflexi made the greatest contribution to the bacterial community in the permafrost soil, and Bacteroidota made the greatest contribution to the bacterial community in the active surface soil. The structure, richness, and diversity of the soil bacterial community significantly differed between the active layer and the permafrost layer. The number of bacterial species was the highest in the active layer surface soil and the active layer bottom soil. The difference in the structure of the bacterial community in the permafrost soil was mainly caused by changes in electrical conductivity and soil-water content, while that in the active layer soil was mainly affected by pH and soil nutrient indices. Soil temperature, NO3--N, and pH had significant effects on the structure of the bacterial community. The active layer and permafrost soils were susceptible to environmental information processing and genetic information processing. Infectious disease: the number of bacterial species was significantly higher in the surface and permafrost layers than in the other layers of the soil. In conclusion, changes in the microbial community structure in soil profiles in discontinuous permafrost areas sensitive to climate change are the key to soil carbon cycle research.

Significant increase in wintertime air temperature, especially the reduced cold extremes under climate change, might be beneficial to the winter survival of perennial crops. However, climate warming could result in less snowfall, reduced snow cover, as well as changes in climate conditions for fall hardening and winter thaws. How these changes might impact the risks of winter damages to overwintering crops, such as perennial forage crops requires a comprehensive assessment for proactively adapting to climate change in the agricultural sector, especially the beef and dairy industries. Based on the most up-to-date climate projections from a set of global climate models, we used a snow model and a suite of agroclimatic indices for perennial forage crops to assess potential changes in the risks of winter injury to perennial forage crops across Canada in the near-term (2030s), the mid-term (2050s), and the distant future (2070s). Our results show that the risk of exposure to extremely low temperatures (daily T-min < -15 degrees C) without snow protection is projected to decrease across Canada with improved conditions for fall hardening. However, winter thaws and rainfall are projected to increase, and this would increase the risk of winter injury due to loss of hardiness together with potential soil heaving and ice encasement.

Freezing/thawing indices are important indicators of the dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially in areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP after presenting changes in frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60 x 10(6) km(2) of permafrost in the QTP degraded to SFG in the 1960s-2000s, and the primary shrinkage period occurred in the 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at rates of 71.00 & DEG;C & BULL;d/decade and 34.33 & DEG;C & BULL;d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly, with values of 48.13 & DEG;C & BULL;d/decade and 40.37 & DEG;C & BULL;d/decade in the past five decades, respectively. Permafrost showed more pronounced changes in freezing/thawing indices since the 1990s compared to SFG. The changes in thermal regimes in frozen ground showed close relations to air warming until the late 1990s, especially in 1998, when the QTP underwent the most progressive warming. However, a sharp increase in the annual precipitation from 1998 began to play a more controlling role in thermal degradation in frozen ground than the air warming in the 2000s. Meanwhile, the following vegetation expansion hiatus further promotes the thermal instability of frozen ground in this highly wet period.

This paper takes the representative buried structure in permafrost regions, a transmission line tower foundation, as the research object. An inverse prediction is conducted in a scaled-down experimental system mimicking actual heat conduction of the frozen ground in a tower foundation. In permafrost regions, global warming and the heat transfer through the buried structures bring significantly adverse thermal effects on the stability of the infrastructures. In modeling the thermal effects, it has been a challenge to determine the ground surface boundary condition and heat source strength from the buried structures due to the complex climate and environmental conditions. In this work, based on the improved model predictive inverse method with an adaptive strategy, an inverse scheme is successfully implemented to simultaneously identify the time-varying surface temperature and the time-space-dependent heat source representing the buried structures. In this scheme, an adaptive time-varying predictive model is established by the rolling update of the sensitivity response coefficients according to the predicted temperature field to overcome the influence of nonlinear characteristics in the heat transfer process. The inverse method is verified by simulation and experimental data. According to the experimental inversion results, the reconstructed temperature distribution efficiently predicts the thermal state evolution of the permafrost foundation under seasonal freezing and thawing. It is found that, under the experimental conditions, the intensified thawing and freezing are significantly severe, e.g., the increased area ratio of active layer thickness is as high as 28% after building a tower, and the depth of permafrost table ranges from about 14 mm to about 38 mm, which could be detrimental to the stability and safety of the tower foundation. This study will provide valuable guidance for risk assessments or optimizing the design and maintenance of the real tower foundation and similar buried structures.

While the sentinel nature of freshwater systems is now well recognized, widespread integration of freshwater processes and patterns into our understanding of broader climate-driven Arctic terrestrial ecosystem change has been slow. We review the current understanding across Arctic freshwater systems of key sentinel responses to climate, which are attributes of these systems with demonstrated and sensitive responses to climate forcing. These include ice regimes, temperature and thermal structure, river baseflow, lake area and water level, permafrost-derived dissolved ions and nutrients, carbon mobilization (dissolved organic carbon, greenhouse gases, and radiocarbon), dissolved oxygen concentrations, lake trophic state, various aquatic organisms and their traits, and invasive species. For each sentinel, our objectives are to clarify linkages to climate, describe key insights already gained, and provide suggestions for future research based on current knowledge gaps. We suggest that tracking key responses in Arctic freshwater systems will expand understanding of the breadth and depth of climate-driven Arctic ecosystem changes, provide early indicators of looming, broader changes across the landscape, and improve protection of freshwater biodiversity and resources.

As a vital source of the climate change predictability, the snow depth predictability originates from its own persistence and the external forcing factors. In order to investigate the root of snow depth predictability at the North Hemisphere, this study conducted an ensemble of 20 simulations spanning 50 years with the Community Earth System Model (CESM). With a regression model constructed via the canonical correlation analysis method, we analyzed the temporal and spatial distribution characteristics of snow depth predictability on the global scale, as well as the effects of snow depth persistence and sea surface temperature (SST) on snow depth predictability. The results show that the predictability due to snow depth persistence depends on both season and location. The persistence of snow depth can reach more than 3 months in high latitude region. After considering the SST forcing, the predictability is increased in many parts of the Northern Hemisphere, such as northern North America, Europe, and central Siberia. The areas where SST significantly influences snow depth predictability mainly overlap the snow cover transition zones. We further investigated the possible pathways of the impact of SST on snow depth predictability, and found that in North America and Europe, SST improves the predictability mainly through affecting the surface temperature, while in central Siberia and eastern Europe, the pathway also includes snowfall and shortwave radiation, respectively. Additionally, we conducted a similar analysis with three other climate models from the Atmospheric Model Intercomparison Project phase 6 (AMIP6), and the results can also verify the conclusions of CESM ensemble simulations.

Acid mine drainage (AMD) is one of the leading causes of environmental pollution and is linked to public health and ecological consequences. Microbes-mineral interaction generates AMD, but microorganisms can also remedy AMD pollution. Exploring the microbial response to AMD effluents may reveal survival strategies in extreme ecosystems. Three distinct sites across a mine (inside the mine, the entrance of the mine, and outside) were selected to study their heavy metal concentrations due to significant variations in pH and physicochemical characteristics, and high-throughput sequencing was carried out to investigate the microbial diversity. The metal and ion concentrations followed the order SO42 , Fe, Cu, Zn, Mg, Pb, Co, Cr, and Ni from highest to lowest, respectively. Maximum sequences were allocated to Proteobacteria and Firmicutes. Among archaea, the abundance of Thaumarchaeota and Euryarchaeota was higher outside of mine. Most of the genera (23.12 %) were unclassified and unknown. The average OTUs (operational taxonomic units) were significantly higher outside the mine; however, diversity indices were not significantly different across the mine sites. Hierarchical clustering of selective genera and nMDS ordination of OTUs displayed greater segregation resolution inside and outside of mine, whereas the entrance samples clustered with greater similarity. Heterogeneous selection might be the main driver of community composition outside the mine, whereas stochastic processes became prominent inside the mine. However, the ANOSIM test shows a relatively even distribution of community composition within and between the groups. Microbial phyla showed both positive and negative correlations with physicochemical factors. A greater number of biomarkers were reported outside of the mine. Predictive functional investigation revealed the existence of putative degradative, metabolic, and biosynthetic pathways. This study presents a rare dataset in our understanding of microbial diversity and distribution as shaped by the ecological gradient and potential novelty in phylogenetic/taxonomic diversity in AMD, with potential biotechnological applications.