Snow depth influences harvest of a boreal ungulate more than socio-economic factors: Implications for food security in a changing climate.

Climate change may affect the ability of hunters to harvest wildlife and, hence, threaten food security of local people. However, few studies have investigated the relative influence of environmental conditions on wildlife harvest rates. We harnessed a 24-year dataset of harvest dates for a boreal ungulate in a region where climate change is having pronounced impacts on snow depth, precipitation, and temperatures to investigate the effect of weather on harvest rates. We used generalized linear models and a model selection framework to examine the influence of weather covariates (snow depth, mean daily temperature, precipitation) and socio-economic factors (gasoline and red meat prices, employment rates, and moose [Alces americanus] harvest) on harvest rates of bison (Bison bison) in Yukon, Canada, at two temporal scales: annual and daily. At an annual scale, snow depth was the only covariate that was important in explaining bison harvest. No socioeconomic variables improved our model beyond the null. At the daily scale, snow depth and mean daily temperature influenced bison harvest rates, with a 1 SD increase resulting in a 14 % and 9 % increase in daily harvest rates, respectively. Increased snow depth facilitates ease of travel in remote, roadless areas by snowmobile to locate bison and truncates movements of bison, resulting in increased harvest rates. Decreased snow depth due to climate change will impact hunter access to boreal ungulates and food security for northern people. More broadly, our data suggests that in some socioecological systems, environmental covariates have a greater influence on wildlife harvest rates than socioeconomic factors and need to be considered in future studies to better understand and predict harvest rates.

Construction industry workers' compensation injury claims due to slips, trips, and falls - Ohio, 2010-2017.

PROBLEM: Compared to other industries, construction workers have higher risks for serious fall injuries. This study describes the burden and circumstances surrounding injuries related to compensable slip, trip, and fall (STF) claims from private construction industries covered by the Ohio Bureau of Workers' Compensation. METHODS: STF injury claims in the Ohio construction industry from 2010-2017 were manually reviewed. Claims were classified as: slips or trips without a fall (STWOF), falls on the same level (FSL), falls to a lower level (FLL), and other. Claim narratives were categorized by work-related risk and contributing factors. Demographic, employer, and injury characteristics were examined by fall type and claim type (medical-only (MO, 0-7 days away from work, DAFW) or lost-time (LT, ≥8 DAFW)). Claim rates per 10,000 estimated full-time equivalent employees (FTEs) were calculated. RESULTS: 9,517 Ohio construction industry STF claims occurred during the 8-year period, with an average annual rate of 75 claims per 10,000 FTEs. The rate of STFs decreased by 37% from 2010 to 2017. About half of the claims were FLL (51%), 29% were FSL, 17% were STWOF, and 3% were "other." Nearly 40% of all STF claims were LT; mostly among males (96%). The top three contributing factors for STWOF and FSL were: slip/trip hazards, floor irregularities, and ice/snow; and ladders, vehicles, and stairs/steps for FLL. FLL injury rates per 10,000 FTE were highest in these industries: Foundation, Structure, and Building Exterior Contractors (52); Building Finishing Contractors (45); and Residential Building Construction (45). The highest rate of FLL LT claims occurred in the smallest firms, and the FLL rate decreased as construction firm size increased. Discussion and Practical Applications: STF rates declined over time, yet remain common, requiring prevention activities. Safety professionals should focus on contributing factors when developing prevention strategies, especially high-risk subsectors and small firms.

A first assessment of microplastic contamination in the snow of Ankara, Turkey.

Microplastics (MPs), affecting aquatic and terrestrial ecosystems, have spread globally. The atmosphere is known as a pollutant acceptor and carrier among other ecosystems. However, the fate and amount of microplastics in the atmosphere have been the subject of less research. Therefore, it is quite important to study the amount and properties of microplastics in atmospheric fallout. The main purpose of this article is to discover microplastics in fresh snow samples collected in three different regions of Ankara and to identify potential sources of supply. The morphologies and compositions of microplastics were analyzed and characterized using scanning electron microscopy (SEM/EDS). µ-Raman spectroscopy was used to reveal the various polymer types of the selected samples. As a result, microplastics were found in all snow samples. Among the nine snow samples examined, 537 particles were recognized as MPs. The average abundance of MPs in snow samples was 59.66 items L-1. Fibers, fragments, films, and circular forms were found in all snow samples. Fragments predominated for all samples (50.08%), followed by films (28.54%), fibers (16.86%), and circulars (4.50%). The proportion of small plastics was quite high when compared to the large plastics captured by snow. Smaller MP particles found in the snow had more variety, suggesting that the microplastics in the snow samples have been broken down by long-range transport and deposition. Six different polymer types were discovered in the snow samples in this study. The most frequently identified polymer was polyethylene (31%), succedded by polystyrene (28%), and polypropylene (21%). Polyethylene terephthalate (12%), polyvinyl chloride (5%), and nylon were present in smaller proportions.

Contamination Pattern and Risk Assessment of Polar Compounds in Snow Melt: An Integrative Proxy of Road Runoffs.

To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for 489 chemicals using liquid chromatography high-resolution mass spectrometry with target screening. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig wastewater treatment plant (WWTP) during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). Consistent patterns of traffic-related compounds dominated the chemical profile (58 compounds in concentrations from 1.3 ng/L to 75 µg/L) and among them were 2-benzothiazole sulfonic acid and 1-cyclohexyl-3-phenylurea from tire wear and denatonium used as a bittern in vehicle fluids. Besides, the analysis unveiled the presence of the rubber additive 6-PPD and its transformation product N-(1.3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6-PPDQ) at concentrations known to cause acute toxicity in sensitive fish species. The analysis also detected 149 other compounds such as food additives, pharmaceuticals, and pesticides. Several biocides were identified as major risk contributors, with a more site-specific occurrence, to acute toxic risks to algae (five samples) and invertebrates (six samples). Ametryn, flumioxazin, and 1,2-cyclohexane dicarboxylic acid diisononyl ester are the main compounds contributing to toxic risk for algae, while etofenprox and bendiocarb are found as the main contributors for crustacean risk. Correlations between concentrations in the WWTP influent and flow rate allowed us to discriminate compounds with snow melt and urban runoff as major sources from other compounds with other dominant sources. Removal rates in the WWTP showed that some traffic-related compounds were largely eliminated (removal rate higher than 80%) during wastewater treatment and among them was 6-PPDQ, while others persisted in the WWTP.

Towards a holistic paradigm for long-term snow avalanche risk assessment and mitigation.

In mountain territories, snow avalanches are a prevalent threat. Long-term risk management involves defining meaningful compromises between protection and overall sustainability of communities and their environment. Methods able to (i) consider all sources of losses, (ii) account for the high uncertainty levels that affect all components of the risk and (iii) cope for marked non-stationarities should be employed. Yet, on the basis of a literature review and an analysis of relations to Sustainable Development Goals (SDGs), it is established that snow avalanche risk assessment and mitigation remain dominated by approaches that can be summed up as deterministic, hazard oriented, stationary and not holistic enough. A more comprehensive paradigm relying on formal statistical modelling is then proposed and first ideas to put it to work are formulated. Application to different mountain environments and broader risk problems is discussed.

Winter condition variability decreases the economic sustainability of reindeer husbandry.

Wild and semidomesticated reindeer are one of the key species in Arctic and subarctic areas, and their population dynamics are closely tied to winter conditions. Difficult snow conditions have been found to decrease the calving success and survivability of reindeer, but the economic effects of variation in winter conditions on reindeer husbandry have not been studied. In this study, we combine state-of-the-art economic-ecological modeling with the analysis of annual reindeer management reports from Finland. These contain local knowledge of herding communities. We quantify the occurrence probabilities of different types of winters from annual management reports and analyze the effects of this variation in winter conditions on reindeer husbandry using an age- and sex-structured bioeconomic reindeer-lichen model. Our results show that difficult winters decrease the net revenues of reindeer husbandry. However, they also protect lichen pastures from grazing, thereby increasing future net revenues. Nonetheless, our solutions show that the variability of winter conditions overall decrease the net income of herders compared to constant winter conditions. Low lichen biomass appears to make reindeer management more sensitive to the effects of difficult winter conditions. We also found that it is economically sensible to use supplementary feeding during difficult winters, but the net revenues still decrease compared to average winters because of the high feeding costs. Overall, our analysis suggests that the increasing variability of winter conditions due to climate change will decrease net revenues in reindeer husbandry. This decrease will still occur even if the most extreme effects of climate change do not occur. This study shows that combining a state-of-the-art bioeconomic model and practitioner knowledge can bring compatible insights, ideas, results, and a bottom-up perspective to the discussion.

[Characteristics of climate change and its impacts on water resources in Qilian Mountains, China]. / ç¥è¿žå±±æ°”å€™å˜åŒ–ç‰¹å¾åŠå ¶å¯¹æ°´èµ„æºçš„å½±å“.

Qilian Mountains, is an important ecological function area, an important ecological security barrier, the river runoff region in Northwest China, as well as a sensitive area to global climate change and fragile area of ecological environment. The ecological environment in this area played an important role in the economic development of Northwest China. Based on the observation data of temperature and precipitation in Qilian Mountains, MOD10A2 snow products and the flow data of Shiyang River, Heihe River and Shule River, we systematically analyzed the characteristics of climate change from 1961 to 2020, and the impacts of climate change on water resources under the scenario of climate warming. The results showed that, from 1961 to 2020, the annual average temperature increased significantly, with the rate reaching 0.39 ℃·(10 a)-1. The warming rate was the highest in the western part of Qilian Mountains, followed by the middle and eastern regions. The warming trend was the strongest in winter and the lowest in spring. The average temperature changed abruptly in 1997. The annual average precipitation increased with flucturation, with a rate of 10 mm·(10 a)-1, which increased most obviously in the middle of Qilian Mountains. After 2004, it entered a rainy period, with a warm and humid trend. The precipitation in the four seasons showed an increasing trend and the increase of precipitation in summer contributed the most to the annual precipitation. Annual precipitation was dominated by interannual scale change, and the contribution rate of 2.8-year was approximately 64.3%. The snow cover of Qilian Mountains was obviously affected by temperature and snowfall, which was negatively correlated with summer temperature and positively correlated with snowfall. From 2016 to 2020, the temperature increase had slowed down in Qilian Mountains, the snowfall had increased, and the snow cover tended to increase. After 2000, the temperature and precipitation increased more obviously, the meltwater from glacier and snow increased, the mountainous runoff of Shiyang River, Heihe River and Shule River had an increasing trend. Our findings are of great significance to the construction of ecological civilization and coping with climate change in Qilian Mountains.

A Model between Near-Surface Air Temperature Change and Dynamic Influencing Factors in the Eastern Tibetan Plateau, China.

Climate change, characterized by global warming, is profoundly affecting the global environment, politics, economy, and social security. Finding the main causes of climate change and determining their quantitative contributions are key points to making climate decisions on responses to climate change. The Tibetan Plateau (TP) is sensitive to global climate change. Taking the 100 km buffer zones of 45 meteorological stations in the eastern TP as research objects, we conducted an experimental study on temperature change and its influencing factors. Using the least squares multivariate statistical analysis method, a model between the annual and seasonal standardized temperature change and its dynamic influencing factors in the past 20 years was established. The results showed that, in the eastern TP, temperature change was affected by different factors in different periods. Vegetation cover and snow cover were the most correlated factors to temperature change. The influence of carbon dioxide, vegetation cover, and water cover was subject to seasonal changes. Urban cover and bare land cover did not pass the t-test. This research not only provides a theoretical basis for the analysis of temperature change over the TP, but also points out the direction for the analysis of temperature change causes in three polar regions.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan.

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Dynamic Resource Allocation and Forecast of Snow Tourism Demand Based on Multiobjective Optimization Algorithm.

This paper aims to map the task of reliable transmission of wireless sensor networks. At the same time, this paper transforms the mapping problem of wireless sensor networks into a problem of reducing the energy consumption of mapping under many constraints such as reliability and scheduling length and uses discrete particle swarm optimization algorithm to map. For optimization, the algorithm performs iterative calculations to obtain the best mapping node for each operation so that the inertia coefficient of the existing particle swarm optimization algorithm is improved and linearly minimized with the number of iterations. When resource-demanding tasks need to allocate dynamic resources to multiple nodes to complete collaboratively, adding the mapping principle of the nearest node in the discrete particle swarm optimization mapping reduces the energy consumption of communication between tasks. An in-depth analysis of the influencing factors of the ice and snow tourism market shows that the per capita disposable income of urban residents and the number of urban residents have a significant impact on the ice and snow tourism market demand. In addition, regression analysis and demand-based forecasting are important methods to analyze the scale and development trend of tourism. At the same time, it shows an important position in the purpose of urban tourism and regional market share so as to provide a basis for decision-making in tourism destination marketing. This paper mainly studies and analyzes the wireless sensor network and further introduces it into dynamic resource allocation and ice and snow tourism, which can promote the continuous development of dynamic resource allocation and ice and snow tourism.

Application of "OTSU"-an image segmentation method for differentiation of snow and ice regions of glaciers and assessment of mass budget in Chandra basin, Western Himalaya using Remote Sensing and GIS techniques.

In this study, an image segmentation algorithm ("OTSU") is applied for differentiation of snow/ice regions followed by interpretation of snowlines and estimation of mass budget of glaciers in Chandra basin, Western Himalaya, India between 2014 and 2020. The observations strongly suggest that the OTSU method can be used to differentiate the snow and ice regions on a glacier accurately from any satellite image, irrespective of the sensor characteristics. Also, this method suits well to delineate the snowlines for large sample of glaciers, other than the manual interpretation and semi-automated methods. The estimates of mass budget of the glaciers are observed varying from - 1.20 ± 0.51 m w.e to almost 0.64 ± 0.51 m w.e, with a total loss of - 61.91 ± 6.70 m w.e of ice mass at basin scale during the observation period. Based on this study, it is highly recommended the application of OTSU method for the differentiation of snow/ice zones of glaciers and snowline demarcation at a large spatial scale in the harsh weather rugged terrain of the Western Himalaya.

Assessment of runoff in Chandra river basin of Western Himalaya using Remote Sensing and GIS Techniques.

The runoff of Chandra river basin in the Himalayan India was assessed using a hydrological model combined with satellite remote sensing observations. During a test period between 2000 and 2015, in situ measurements of runoff and meteorological parameters were conducted in the glacial catchment areas of Sutridhaka and Chhotashigri. A good agreement was found between the observed and predicted runoff (correlation R2 > 0.8). The hydrological model was then used to simulate the runoff of Chandra River for a period of 2000 to 2015. Almost 68% of the predicted runoff occurred during the ablation period (May to September). A sensitivity study of the Chandra basin hydrology to a predicted warming climate of 1 to 4 K, toward the end of the century suggests that increased production of glacial melt water would have more impact on runoff than potential increase in precipitation. During the monsoon months (of June to August), increased runoff is predicted due to enhanced glacial melting but the runoff in other months to be lower than present mean runoff, except for the summer months (March to July).

Cascading costs of snow cover reduction trend in northern hemisphere.

Environmental changes in response to global warming would conversely deplete the efficacy of our actions combating climate change, cultivating extra cost. Among them, the declining snow cover due to global warming would diminish its contribution to climate regulation, and further exacerbate global warming. This leads to a part of global carbon mitigation efforts acting virtually to neutralize the impact of snow cover reduction. It would have been otherwise saved to contribute to the goals of the Paris Agreement. In this respect, here we evaluate the economic impacts of snow cover reduction in the Northern Hemisphere in terms of the mitigation that virtually counteracts the loss of climate regulation pertinent to the snow cover reduction trend, to demonstrate the magnitude of the cascading costs of climate change. As different carbon mitigation approaches would lead to different economic impacts, we follow the general principles of the Paris Agreement and establish two responsibility-sharing scenarios. The results reveal the non-negligible global costs considering not only the impact incurred by the nations implementing carbon mitigation but also, in the context of globalization, the cascading effect magnified in the global supply chain. We also identify critical nations, sectors, and international trade pairs that would confront the most costs. The results urge hotspot nations and trade partners to actively participate in the enhanced global efforts through the Paris Agreement to reduce carbon emissions. This can not only mitigate its direct global warming effect, but also abate the impacts of collateral environmental deterioration, such as snow cover reduction, eventually for their own benefits.

A sensitive microextraction by packed sorbent-gas chromatography-mass spectrometry method for the assessment of polycyclic aromatic hydrocarbons contamination in Antarctic surface snow.

For the first time an eco-friendly method involving microextraction by packed sorbent (MEPS) coupled to gas chromatography-mass spectrometry (GC-MS) was developed for the determination of the 16 US-EPA priority pollutant polycyclic aromatic hydrocarbons (PAHs) as indicators of anthropogenic contamination in snow samples collected in polar regions. MEPS was carried out by using C8 sorbent material packed in a barrel insert and needle (BIN) and integrated in the eVol® semi-automatic device. For optimization purposes a Face Centred Design and the multicriteria method of the desirability functions were performed to investigate the effect of some parameters affecting the MEPS extraction efficiency, i.e. the number of loading cycles and the number of elution cycles. The developed MEPS-GC-MS method proved to be suitable for PAHs analysis at ultra-trace level by extracting small sample volumes achieving detection limits for 16 PAHs in the 0.3-5 ng L-1 range, repeatability and intermediate precision below 11% and 15%, respectively, and good recovery rates in the 77.6 (±0.1)-120.8 (±0.1)% range for spiked blank snow samples. Enrichment factors in the 64 (±7)-129 (±18) range were calculated. Finally, the proposed method was successfully applied to the determination of PAHs in surface snow samples collected in 2020-2021 from four locations of Northern Victoria Land, Antarctica. Local emission sources such as ships and research stations were found to influence PAHs concentrations in the surface snow.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India.

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The Boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear, polynomial, sigmoid, and RBF kernels were 84.1%, 86.6%, 43.0%, and 87.8%, respectively. We also identified locations where occurrences of avalanches are most likely. We conclude that parameter selection should be considered when applying ML methods in geosciences.

Costs of reproduction under experimental climate change across elevations in the perennial forb Boechera stricta.

Investment in current reproduction can reduce future fitness by depleting resources needed for maintenance, particularly under environmental stress. These trade-offs influence life-history evolution. We tested whether climate change alters the future-fitness costs of current reproduction in a large-scale field experiment of Boechera stricta (Brassicaceae). Over 6 years, we simulated climate change along an elevational gradient in the Rocky Mountains through snow removal, which accelerates snowmelt and reduces soil water availability. Costs of reproduction were greatest in arid, lower elevations, where high initial reproductive effort depressed future fitness. At mid-elevations, initial reproduction augmented subsequent fitness in benign conditions, but pronounced costs emerged under snow removal. At high elevation, snow removal dampened costs of reproduction by prolonging the growing season. In most scenarios, failed reproduction in response to resource limitation depressed lifetime fecundity. Indeed, fruit abortion only benefited high-fitness individuals under benign conditions. We propose that climate change could shift life-history trade-offs in an environment-dependent fashion, possibly favouring early reproduction and short lifespans in stressful conditions.

Weather and climate in the assessment of tourism-related walkability.

Walking is an important outdoor recreational and tourism activity, both in natural surroundings and in urban settings. Walkability is the extent to which the built environment promotes walking, and addresses issues such as comfort, connectivity, safety and aesthetic values. The paper explores a relatively overlooked domain of recreation- and tourism-related walkability: the extent to which weather and climatic conditions are incorporated into assessments of walkability and their influence on walking behaviour. Following a discussion of the assessment of walkability, the results of a scoping review of weather- and climate-related variables in walkability articles published up to June 2018 are presented. The review indicates there is little research on walkability from a tourist perspective, although there is substantial interest in walking for leisure and recreation. Four major themes were identified. The descriptive theme focuses on the general importance of weather and climate to walkability; the passive analyses the weather and climate conditions as barriers to, or as promoters of, walking; proactive studies refer to adapting to and managing weather, such as provision of shade or snow-clearing. The proactive perspective gains less attention than the passive analysis. Finally, statistical controlling was only employed by a minority of studies that adjusted their results to the impacts of weather conditions. The work concludes that the sub-field has been poorly served with respect to concept and variable definition and consistency of use mean that present knowledge is of limited scientific value. Strategies for future research are suggested.

Assessment of heavy metal contamination in the atmospheric deposition during 1950-2016 A.D. from a snow pit at Dome A, East Antarctica.

Antarctic trace element records could provide important insights into the impact of human activities on the environment over the past few centuries. In this study, we investigated the atmospheric concentrations of 14 representative heavy metals (Al, As, Cd, Co, Cu, Fe, K, Mg, Mn, Pb, Sb, Sr, Tl and V) from 174 samples collected in a 4-m snow pit at Dome Argus (Dome A) on the East Antarctic Plateau, covering the period from 1950 to 2016 A.D. We found great variability in the annual concentration of all metals. The crustal enrichment factors suggest that the concentrations of some heavy metals (Cd, Sb, Cu, As and Pb) were likely influenced by anthropogenic activities in recent decades. An analysis of source regions suggests that heavy metal pollution at Dome A was largely caused by human activities in Australia and South America (e.g. mining production, leaded gasoline). Based on the relationship between the trace elements fluxes and sea ice concentration (SIC), sea surface temperature (SST) and annual mean air temperature at 2 m above the ground (T2m), our analysis shows that deposition and transport of atmospheric aerosol at Dome A were influenced by circum-Antarctic atmospheric circulations.

Future winters present a complex energetic landscape of decreased costs and reduced risk for a freeze-tolerant amphibian, the Wood Frog (Lithobates sylvaticus).

Winter climate warming is rapidly leading to changes in snow depth and soil temperatures across mid- and high-latitude ecosystems, with important implications for survival and distribution of species that overwinter beneath the snow. Amphibians are a particularly vulnerable group to winter climate change because of the tight coupling between their body temperature and metabolic rate. Here, we used a mechanistic microclimate model coupled to an animal biophysics model to predict the spatially explicit effects of future climate change on the wintering energetics of a freeze-tolerant amphibian, the Wood Frog (Lithobates sylvaticus), across its distributional range in the eastern United States. Our below-the-snow microclimate simulations were driven by dynamically downscaled climate projections from a regional climate model coupled to a one-dimensional model of the Laurentian Great Lakes. We found that warming soil temperatures and decreasing winter length have opposing effects on Wood Frog winter energy requirements, leading to geographically heterogeneous implications for Wood Frogs. While energy expenditures and peak body ice content were predicted to decline in Wood Frogs across most of our study region, we identified an area of heightened energetic risk in the northwestern part of the Great Lakes region where energy requirements were predicted to increase. Because Wood Frogs rely on body stores acquired in fall to fuel winter survival and spring breeding, increased winter energy requirements have the potential to impact local survival and reproduction. Given the geographically variable and intertwined drivers of future under-snow conditions (e.g., declining snow depths, rising air temperatures, shortening winters), spatially explicit assessments of species energetics and risk will be important to understanding the vulnerability of subnivium-adapted species.

Assessment of varying changes of vegetation and the response to climatic factors using GIMMS NDVI3g on the Tibetan Plateau.

Under the context of global climate change, vegetation on the Tibetan Plateau (TP) has experienced significant changes during the past three decades. In this study, the spatiotemporal changes of growing season vegetation index (GSVI) on the TP were analyzed using various methods from pixel level to ecoregion level. In addition, a relative importance approach was employed to investigate the regulating effect of temperature and precipitation on vegetation. During the period of 1982-2012, vegetation on the TP was generally experiencing a greening trend, but with pronounced fluctuations. The interannual variation of the long-term GSVI was most significant in the Qaidam Basin and southern forest. At ecoregion scale, vegetation in the arid and frigid arid zones showed a browning tendency, with other ecoregions presenting greener trends. Over a large proportion of the TP, there exist change points in the GSVI time series, which were mainly concentered around the year 1996 and 2000. The Hurst exponent identified that a majority (88%) of the vegetation on the plateau would maintain a persistent trend in the future, which would mainly consist of undetermined development and greening trends. TP vegetation during the 1990s experienced more greening than in the 1980s or 2000s according to the interdecadal analysis. The long-term change in growing season vegetation was most positively correlated with the temperature during the same period, followed by the temperature in the preseason and postseason periods. There were more negative relationships of vegetation change with precipitation than with temperature. The relative contribution of the temperature to the vegetation changes exhibited an opposite spatial pattern to that of precipitation. Overall, the findings in this work provide an essential archive of decade-scale vegetation dynamics that may be helpful for projecting the future ecosystem dynamics on the Tibetan Plateau, such as the consistent greening.