Winter Snowfall Research Articles

Severe winter conditions in the Citlaltepetl-Cofre de Perote mountain range, 2021-2022

The central border that divides the states of Puebla and Veracruz is characterized by being the highest territorial limit in Mexico; it is composed of three of the highest mountains in the country. This border reaches a length of 100 km, forming a watershed that continuously exceeds 3000 m in altitude. Due to its elevation, winter freezing conditions are frequent; however, due to changing climate patterns, in recent winter seasons, severe frosts and snowfalls have occurred at elevations well below the main peaks, affecting the health of thousands of vulnerable residents of both states surrounding this mountain range. Because the 2021-2022 winter season was one of the most intense in all North America, with severe impacts in Mexico as a result of freezing temperatures and snowfall, this work analyzes the winter conditions in 2021-2022 through the monitoring of frontal systems and the analysis of data from automatic meteorological stations near the study area. From the records, the lower limit of surface freezing was estimated for each arrival of cold air masses; the occurrence of snowfall was determined by remote sensing, and the area of snow cover was also calculated. At the same time, the population affected by freezing weather conditions was estimated. The surface freezing starts at elevations as low as 2259 m, covering an area of up to 2949 km2, which corresponds to the entire study region, affecting 139 towns in Veracruz and 163 in Puebla.

Spatiotemporal variation of modern lake, stream, and soil water isotopes in Iceland

Abstract. As global warming progresses, changes in high-latitude precipitation are expected to impart long-lasting impacts on Earth systems, including glacier mass balance and ecosystem structures. Reconstructing past changes in high-latitude precipitation and hydroclimate from networks of continuous lake records offers one way to improve forecasts of precipitation and precipitation–evaporation balances, but these reconstructions are currently hindered by the incomplete understanding of controls on lake and soil water isotopes. Here, we study the distribution of modern water isotopes in Icelandic lakes, streams, and surface soils collected in 2002, 2003, 2004, 2014, 2019, and 2020 to understand the geographic, geomorphic, and environmental controls on their regional and interannual variability. We find that lake water isotopes in open-basin (through-flowing) lakes reflect local precipitation, with biases toward the cold season, particularly in lakes with sub-annual residence times. Closed-basin lakes have water isotope and deuterium excess values consistent with evaporative enrichment. Interannual and seasonal variabilities of lake water isotopes at repeatedly sampled sites are consistent with instrumental records of winter snowfall; summer relative humidity; and atmospheric circulation patterns, such as the North Atlantic Oscillation. Summer surface soil water isotopes span the entire range of seasonal precipitation values in Iceland and appear to be consistently overprinted by evaporative enrichment, which can occur throughout the year, although the sampling depths were shallower than rooting depths for many plant types. This dataset provides new insight into the functionality of water isotopes in Icelandic environments and offers renewed possibilities for optimized site selection and proxy interpretation in future paleohydrological studies on this North Atlantic outpost.

Image analysis and LSTM methods for forecasting surficial displacements of a landslide triggered by snowfall and rainfall

AbstractLandslide-prone areas, predominantly located in mountainous regions with abundant rainfall, present unique challenges when subject to significant snowfall at high altitudes. Understanding the role of snow accumulation and melting, alongside rainfall and other environmental variables like temperature and humidity, is crucial for assessing landslide stability. To pursue this aim, the present study focuses first on the quantification of snow accumulated on a slope through a simple parameter obtained with image processing. Then, this parameter is included in a slope displacement prediction analysis carried out with long short-term memory (LSTM) neural network. By employing image processing algorithms and filtering out noise from white-shown rocks, the methodology evaluates the percentage of snow cover in RGB images. Subsequent LSTM forecasts of landslide displacement utilize 28-day historical data on rainfall, snow, and slope movements. The presented procedure is applied to the case of a deep-seated landslide in Italy, a site that in winter 2020–2021 experienced heavy snowfall, leading to significant snow accumulation on the slope. These episodes motivated a study aimed at forecasting the superficial displacements of this landslide, considering the presence of snow both at that time and in the following days, along with humidity and temperature. This approach indirectly incorporates snow accumulation and potential melting phenomena into the model. Although the subsequent winters were characterized by reduced snowfall, including this information in the LSTM model for the period characterized by snow on the slope demonstrated a dependency of the predictions on this parameter, thus suggesting that snow is indeed a significant factor in accelerating landslide movements. In this context, detecting snow and incorporating it into the predictive model emerges as a significant aspect for considering the effects of winter snowfall. The method aims to propose an innovative strategy that can be applied in the future to the study of the landslide analyzed in this paper during upcoming winters characterized by significant snowfall, as well as to other case studies of landslides at high altitudes that lack precise snow precipitation recording instruments.

Projection of snowfall and precipitation phase changes over the Northwest China based on CMIP6 multimodels

The change of precipitation phase could have profound influence on water cycle and ecological environment in Northwest China (NWC). It has been observed that snowfall is inclined to transition into rainfall under climate warming. However, the evolution process of precipitation phase in the future over the NWC remains poorly understanding. In this study, the performance of snowfall simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated by results of observation-based rain/snow separation, subsequently analyzing the changes in snowfall and precipitation phase under different climate scenarios over the NWC. The results indicated that most of CMIP6 models overestimate snowfall while underestimate the Snowfall to Precipitation Ratio (SPR) over the NWC. The performance of CMIP6 models is poorer in high-altitude mountainous region but relatively more robust in low-altitude areas. Superior performance of five models are selected for analysing changes of snowfall and precipitation phase in the future. Under the SSP1-2.6 scenario, the trend of changes in snowfall is not obviously in the future. Conversely, snowfall is projected to decline at rates of −1.04 mm·10a-1 and −2.94 mm·10a-1 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, with significant declines in high-altitude zones. The SPR will decrease at rates of −0.19 %·10a-1, −0.64 %·10a-1, and −1.50 %·10a-1 under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenario, respectively. The magnitudes in changes of snowfall and SPR show significant differences across sub-regions in the future, with low-altitude areas exhibiting upward trends in snowfall and SPR under certain conditions. Winter snowfall in the future shows an increased trend, while declines are observed in spring and autumn snowfall overall, with notable disparities between scenarios. The correlation between snowfall biases and biases of temperature and precipitation indicate that temperature is a pivotal factor contributing to precipitation phase bias. The findings are helpful for enhancing the simulation precision of hydrological processes and providing scientific strategies for future water resource management over the NWC.

Observed changes in the climate and snow dynamics of the Third Pole

The Third Pole (TP) is the world’s largest highland and has one of the biggest reservoirs of glacier ice mass and snow cover on the Earth. Three major Asian rivers (the Indus, Ganga and Brahmaputra) are nourished by the melting of glaciers and snow in Central Himalaya, which are inevitable for the socioeconomic sustainability and water security of South Asia. Here, we investigate the long-term (1980–2020) changes in snow depth and precipitation in TP, where major precipitation occurs in the form of rainfall in summer, and snowfall in winter and spring. The seasonal mean snow depth is deep (≥1 m) in winter and shallow (≤0.2 m) in summer. The average snowmelt and snow water equivalent are higher in the central and western Himalaya and Karakoram ranges in spring, which are the regions with most glaciers in TP. There is a significant positive trend in total precipitation, about 0.01–0.03 mm d−1 yr−1 in the central and eastern TP during the South Asian Summer Monsoon for the 1980–2020 period. Snowmelt is also increasing (>0.5 × 10−3 mm yr−1) in the western Himalaya during spring, which is consistent with the temperature rise (0.04–0.06 °C yr−1) there. In addition, there is a notable increase in the annual mean glacier melt (here, the water equivalent thickness) in TP (−1 to −5 cm w.e. yr−1), with its highest values in the eastern and central Himalaya (−3 to −5 cm w.e. yr−1), as estimated for the period 2003–2020. On top of these, by the end of the 21st century, the Coupled Model Intercomparison Project Phase 6 (CMIP6) projections show that there would be a significant decrease in snow depth and an increase in temperature of TP in all shared socioeconomic pathways (SSPs). Henceforth, the increasing trend in temperature and melting of snow/glaciers in TP would be a serious threat to the regional climate, water security and livelihood of the people of South Asia.

Rapid Warming Exacerbates Winter Drought Stress in Trees at High-Altitude Areas in Northeast China

Against the background of global warming, trees in high-latitude and high-altitude areas are more sensitive to rapid warming. Revealing the response patterns of trees at different altitudes to rapid warming in typical alpine mountain environments can help to predict the future distribution of forests in the region and the associated changes in the timberline. This study used tree-ring width data (band sampling) from Dahurian larch (Larix gmelinii Rupr.) along an altitudinal gradient (970–1409 m) on Oakley Mountain to establish 10 chronologies and to analyze the growth–climate response of larch to warming and altitudinal changes along a gradient. The results showed that before rapid warming, larch growth was strongly influenced mainly by precipitation in July–August, while after warming, the growth was controlled mainly by precipitation (snowfall) in winter (October–April) and showed a significant positive correlation with the SPEI in winter and a negative correlation with temperature in February–April (early spring). This indicates that the rapid warming event led to a drastic change in the water heat balance during the pre-growth period of the trees in the study area, which shifted the tree growth from being restricted by drought in the growing season before warming to being restricted by winter drought (i.e., the lagged effect of snowfall in the pre-growing season) after warming and that the trees at low altitudes suffered from more severe winter drought. In the future, as the global climate warms further, the growth of mountain larch at low altitudes (below 1200 m) will continue to decrease, and the coupling of winter snowfall and seasonal (February–April) warming will increase the upper altitude limit at which winter drought occurs for larch in the study area; additionally, larch at higher altitudes will be able to cope with this ecological process better.

Celerity of Ice Breakup Front in the Regulated Peace River, Canada, and Implications for the Recharge of the Peace–Athabasca Delta

Timely release of flow from upstream hydropower generation facilities on the Peace River can enhance potential ice-jam flooding near the drying Peace–Athabasca Delta (PAD), a Ramsar wetland of international importance and homeland to Indigenous Peoples. An important consideration in deciding whether and when to commence a release is the celerity of the breakup front as it advances along the Peace River. Relevant historical data for a key stretch of the river are analyzed to determine average celerities, which can vary by an order of magnitude from year to year. Seven breakup events are identified that might have been candidates for a release, and the predictability of associated celerities is explored in terms of antecedent hydroclimatic variables, including cumulative winter snowfall, snow water equivalent on 1 April, ice cover thickness, coldness of the winter, and freezeup level. It is shown that celerity can be predicted to within a factor of two or less, with the freezeup level giving the best results. Three of the seven “promising” events culminated in PAD floods and were associated with the three highest celerities. The empirical findings are shown to generally align with physical understanding of breakup driving and resisting factors.

Effects of extreme precipitation on bacterial communities and bioaerosol composition: Dispersion in urban outdoor environments and health risks

Concerns about contaminants dispersed by seasonal precipitation have grown due to their potential hazards to outdoor environments and human health. However, studies on the crucial environmental factors influencing dispersion changes in bacterial communities are limited. This research adopted four-season in situ monitoring and sequencing techniques to examine the regional distribution profiles of bioaerosols, bacterial communities, and risks associated with extreme snowfall versus rainfall events in two monsoon cities. In the early-hours of winter snowfall, airborne cultivable bioaerosol concentrations were 4.1 times higher than the reference exposure limit (500 CFU/m3). The concentration of ambient particles (2.5 μm) exceeded 24,910 particles/L (97 μg/m3), positively correlating with the prevalence of cultivable bioaerosols. These bioaerosols contained cultivable bacterial species such as pathogenic Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, and Escherichia coli. Bioaerosol concentrations increased by 53.0% during 50-mm snow extremes. Taxonomic analysis revealed that Pseudomonas, Staphylococcus, and Veillonella were the most abundant bacterial taxa in the initial snowmelt samples during winter precipitation. However, their abundance decreased by 87.6% as snowing continued (24 h). Reduced water base cation concentration also led to a 1.15-fold increase in the Shannon index, indicating a similar yet heightened bacterial diversity. Seasonally, Pedobacter and Massilia showed higher relative abundance (25% and 18%, respectively), presenting increased bacterial transmission to the soil. Furthermore, Pseudomonas was identified in 60% of spring snowstorm samples, suggesting long-distance dispersal of pathogenic bacteria. When these atmospheric aerosol particles carrying biological entities (0.65–1.1 μm) penetrated human alveoli, the calculated hazard ratio was 0.55, which as observed in inhalation exposures. Consequently, this study underscores the risk of seasonal precipitation-enhanced ambient bacterial transmission.

Convection-Permitting Future Climate Simulations for Bulgaria under the RCP8.5 Scenario

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change.

The Median Isn’t the Message: soil nutrient hot spots have a disproportionate influence on biogeochemical structure across years, seasons, and depths

Soil nutrient distribution is heterogeneous in space and time, potentially altering nutrient acquisition by trees and microorganisms. Ecologists have distinguished “hot spots” (HSs) as areas with enhanced and sustained rates of nutrient fluxes relative to the surrounding soil matrix. We evaluated the spatial and temporal patterns in nutrient flux HSs in two mixed-conifer forest soils by repeatedly sampling the soil solution at the same spatial locations (horizontally and vertically) over multiple seasons and years using ion exchange resins incubated in situ. The climate of these forests is Mediterranean, with intense fall rains occurring following summers with little precipitation, and highly variable winter snowfall. Hot spots formed most often for NO3− and Na+. Although nutrient HSs often occurred in the same spatial location multiple times, HSs persisted more often for PO43− NH4+, and NO3−, and were more transient for Ca2+, Mg2+, and Na+. Sampling year (annual precipitation ranged from 558 to 1223 mm) impacted the occurrence of HSs for most nutrients, but season was only significant for PO43−, NH4+, NO3−, and Na+, with HSs forming more often after fall rains than after spring snowmelt. The frequency of HSs significantly decreased with soil depth for all nutrients, forming most commonly immediately below the surficial organic horizon. Although HSs accounted for less than 17% of the sampling volume, they were responsible for 56–88% of PO43−, NH4+, and NO3− resin fluxes. Our results suggest that macronutrient HSs have a disproportional contribution to soil biogeochemical structure, with implications for vegetation nutrient acquisition strategies and biogeochemical models.Graphical abstract

Control of the temperature signal in Antarctic proxies by snowfall dynamics

Abstract. Antarctica, the coldest and driest continent, is home to the largest ice sheet, whose mass is predominantly recharged by snowfall. A common feature of polar regions is the warming associated with snowfall, as moist oceanic air and cloud cover increase the surface temperature. Consequently, snow that accumulates on the ice sheet is deposited under unusually warm conditions. Here we use a polar-oriented regional atmospheric model to study the statistical difference between average and snowfall-weighted temperatures. During snowfall, the warm anomaly scales with snowfall amount, with the strongest sensitivity occurring at low-accumulation sites. Heavier snowfall in winter helps to decrease the annual snowfall-weighted temperature, but this effect is overwritten by the event-scale warming associated with precipitating atmospheric systems, which particularly contrast with the extremely cold conditions that occur in winter. Consequently, the seasonal range of snowfall-weighted temperature is reduced by 20 %. On the other hand, the annual snowfall-weighted temperature shows 80 % more interannual variability than the annual temperature due to the irregularity of snowfall occurrence and its associated temperature anomaly. Disturbances of the apparent annual temperature cycle and interannual variability have important consequences for the interpretation of water isotopes in precipitation, which are deposited with snowfall and commonly used for paleotemperature reconstructions from ice cores.

Forest structure mediates occupancy and extinction of vertebrate prey species on the trailing edge of the boreal forest

Climate change is expected to shift the distributions of global biomes. High latitude biomes, such as the boreal forest, are among the most vulnerable and serve as a bellwether for the effects of climate change on other ecosystems. We examined patterns of occupancy, local extinction, and abundance in a guild of forest-associated vertebrate prey species on the southern margin of the boreal forest in Minnesota (USA). We used multi-season, multi-state, and multi-species occupancy modeling approaches to determine the effects of land cover, forest structural characteristics, and climatic variation on two boreal-associated species, spruce grouse (Canachites canadensis) and snowshoe hare (Lepus americanus), and a forest generalist, ruffed grouse (Bonasa umbellus). Occupancy, abundance, and extinction of spruce grouse was strongly associated with landscape-scale characteristics. In particular, deciduous forest cover may govern their southern range limit. Broadly, forest understory structure played a significant role in occupancy, extinction, and abundance patterns of all three species, with ruffed and spruce grouse generally associated with vegetation density in the lower and mid-canopy layer (5.00–15.00 m) and snowshoe hare associated with density in the shrub layer (1.37–5.00 m) in winter. Co-occurrence varied annually but was greatest in mixed forests during an uncharacteristically warm and snow-sparse year. Climatic variables (winter temperature and snowfall) were associated with extinction probabilities for all three species, but the effect was generally weaker than that of forest structure. Our results suggest that forest management practices that promote dense understory structure may help provide climate refugia for vertebrate prey species in boreal forest ecosystems.

Effects of Prognostic Number Concentrations of Snow and Graupel on the Simulated Precipitation over the Korean Peninsula

Abstract A new version of the Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and the one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula.

The Effects of Sudden Freezing on the Biochemical Status of Bamboo Leaves: A Case Study on Nine Species on a Subtropical Plateau

The differences in the response of the leaves of different bamboo types to sudden snowfalls in winter were analyzed in order to provide scientific references for the introduction and cultivation of cold-resistant bamboo species and to provide new theoretical information on bamboo afforestation and disaster reduction. A total of nine bamboo species were selected to analyze the physiological and chemical changes in the leaves caused by a sudden snowfall. The results showed that sudden snowfall in winter led to a decrease in the moisture, soluble sugar, and starch contents in the leaves of all of the bamboo species analyzed, but there were increases in the contents of proline, MDA, and H2O2 and in the ratios of AsA/DHA and GSH/GSSG. Both the enzymatic activities (SOD, POD, CAT, APX, DHAR, GPX, and GR) and non-enzymatic antioxidant contents (AsA and GSH) were increased after the snowfall, which indicated that the sudden snowfall caused an increase in the antioxidant abilities in the leaves of all bamboo species analyzed. Different bamboo species adopted different strategies for resisting the freezing damage caused by the sudden snowfall. The prevention and mitigation of snow disasters in winter can be scientifically carried out in bamboo forests according to their tolerance abilities.

The Leading Modes of Northern Eurasian Winter Snowfall Variability and the Potential Influencing Factors

Abstract This study investigates the dominant modes of the interannual variability of the northern Eurasian winter snowfall during 1982–2020 and explores their potential influencing factors and the associated physical processes. The first and second empirical orthogonal function (EOF) modes feature coherent snowfall anomalies over the high latitudes of Eurasia and western Siberia, respectively. Further analyses indicate that the anomalous atmospheric circulations play a major role in forming the snowfall variability, which could be further attributed to the influences of the atmospheric teleconnection patterns and Arctic sea ice variations. Specifically, the anomalous circulations related to the first EOF mode are mainly contributed by the effects of the teleconnections of the Polar–Eurasian and Scandinavian patterns. The formation of the second EOF mode has a close connection with the North Atlantic Oscillation and the Eurasian pattern. In addition, the sea ice variations over Baffin Bay exert a considerable influence on the snowfall anomalies related to the second EOF mode by exciting a wave train–like anomalous circulation. This effect is further verified by a numerical simulation. An empirical statistical model based on the above influencing factors can well explain the temporal evolutions of the two dominant modes, verifying the important value of our results to improve the understanding of interannual variability of northern Eurasian winter snowfall.

Role of Snowfall Versus Air Temperatures for Greenland Ice Sheet Melt‐Albedo Feedbacks

AbstractThe Greenland Ice Sheet is a leading contributor to global sea‐level rise because climate warming has enhanced surface meltwater runoff. Melt rates are particularly sensitive to air temperatures due to feedbacks with albedo. The primary melt‐albedo feedback, fluctuation of seasonal snowlines, however, is determined not only by melt but also by antecedent snowfall which could delay the onset of dark glacier ice exposure. Here we investigate the role of snowfall versus air temperatures on ice sheet melt‐albedo feedbacks using satellite remote sensing and atmospheric reanalysis data. We find several lines of evidence that snowline fluctuations are driven primarily by air temperatures and that snowfall is a secondary control. First, standardized linear regressions indicate that the timing of glacier ice exposure is nearly twice as sensitive to air temperatures than antecedent snowfall. Second, in 74% of the ablation zone by area, winter snowfall rates are not significantly correlated with winter air temperatures. This relationship implies that ice sheet melt due to climate warming is unlikely to be compensated by higher snowfall rates in the ablation zone. Third, we find no significant change in snowfall rates in the ablation zone during our 1981–2021 study period. Our findings demonstrate that snowfall is unlikely to reduce future ice sheet melt and that ice sheet meltwater runoff should be accurately predicted by air temperatures. Although given the importance of melt‐albedo feedbacks, ice sheet models that parameterize albedo or are coupled with regional climate models are likely to provide the most accurate projections of mass loss.

Experimental study on the effect of dust particle deposition on photovoltaic performance of urban buildings

The output characteristics of photovoltaic (PV) modules can be negatively affected by the density of snowfall in winter and ash accumulation in summer. In order to solve this problem, an experimental platform for PV power generation with four angles of 0°, 30°, 45° and 60° was built in Shenbei New District, Shenyang City, China. The characteristics of ash accumulation on the surface of PV modules were studied and analyzed by SEM and XRD. The effects of snowfall, ash accumulation density and PV module angle on the output power and power attenuation rate of PV modules were investigated. The experimental results show that snowfall had both positive and negative effects on PV modules. The output power of the PV module decreases as the ash density increases. Based on the dust coverage density and tilt angle in the test data, empirical formulas are proposed to predict the average power reduction rate.

Influence of Arctic sea ice and Interdecadal Pacific Oscillation on the recent increase of winter extreme snowfall in Northeast China

In recent decades, Northeast China (NEC) has witnessed a substantial 20% rise in the occurrence of intense winter snowfall, resulting in a notable increase in the overall snowfall accumulation. Using Empirical Orthogonal Function (EOF) analysis, this study identifies a dominant interdecadal variation of the winter extreme snowfall with a turning point around 2000. The analysis uncovers abnormal atmospheric circulations linked to the positive phase of the like-Scandinavian pattern and anomalous North Pacific anticyclone, providing favorable conditions for cold air outbreak and water vapor enhancement for the increase in extreme snowfall. The positive phase of the like-Scandinavian pattern exhibits a strong correlation with sea ice loss in the Baffin Bay, Greenland Sea, and Barents-Kara Sea (BGBK). This positive pattern is primarily sustained by a positive feedback mechanism stemming from sea-ice-atmosphere interactions. Specifically, the positive like-Scandinavian pattern leads to increased water vapor over the BGBK region, promoting sea ice loss through enhanced clear-sky downward long-wave radiation (CDLW) and increased surface turbulent heat flux. Conversely, a positive sea surface temperature anomaly prevails in the negative phase of the Interdecadal Pacific Oscillation (IPO), triggering an anomalous anticyclone over the North Pacific and East Asia, contributing to the observed increase in extreme snowfall over NEC. Furthermore, this study demonstrates a modulating relationship between the positive like-Scandinavian pattern and the anomalous anticyclone associated with the negative phase of the IPO, achieved through the triggering of Rossby wave trains, which were verified through numerical experiments using a atmospheric general circulation model. These results underscore the joint impact of interdecadal variability in Arctic Sea ice and the IPO on the decadal increase in winter extreme snowfall over NEC.

Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

Long‐term declines in insect abundance and biomass in a subalpine habitat

AbstractRecent reports of insect declines have caused concern among scientists and the public. Declines in insect abundance and biomass are ubiquitous across many climatic zones and have been largely attributed to anthropogenic land use intensification and climate change. However, there are few examples of long‐term continuous data in relatively undisturbed environments, as opposed to agricultural landscapes. We sampled insects weekly from 1986 to 2020 in a protected subalpine meadow in Colorado, which is embedded in an undisturbed natural landscape. During the study period, summers became warmer, while winters became drier. Insect biomass declined by ∼47% and abundance declined by ∼61.5% over the last 35 years. Insect declines occurred in concert with changes in climate, as some climate factors were correlated with insect abundance and biomass. Specifically, insect abundance was lower during years with less summer precipitation and winter snowfall, and to a lesser degree with warmer temperatures. In subalpine systems, changes in precipitation and warmer temperatures are expected to continue under climate change; thus, continued insect declines might be expected even in relatively undisturbed habitats.