Average Snowfall Research Articles

Weather Patterns, Patient, and Appointment Characteristics Associated with Cancellations and No-Shows in a Glaucoma Clinic

ABSTRACT Purpose To identify factors that are associated with no-shows and cancellations in a glaucoma clinic. Methods Retrospective observational study of patients seen at a glaucoma clinic over a two-year period (6/2017–5/2019). Demographics and clinic information were recorded from the electronic medical record. A total of 36,810 visits from 7,383 patients were studied. Weather data was collected from the National Centers for Environmental Information. Distance analysis was calculated utilizing Bing Maps application programming interface (API) on Microsoft Excel. Visits were divided into three groups based on appointment status: kept, cancelled, and no-show. Results Bivariate analysis found a statistically significant difference in various factors amongst patients based on appointment status. Patients <15 miles from clinic had a higher rate of no-show, but a lower rate of cancellations compared to those farther (p < 0.0001) Using multivariable logistic regression, the following factors were associated with the likelihood of patient cancellation: average snowfall (Odds Ratio = 1.37); presence of storm event (OR = 1.12), new visit (OR = 1.82), follow-up appointments (OR = 1.90), and travel distance > 15 miles (OR = 1.11). The following factors were associated with patient no-show: resident clinic (OR = 1.79), new visit (OR = 2.24), follow-up appointments (OR = 2.18), age (OR = 0.99), average snowfall (OR = 1.27), presence of storm event (OR = 1.41), average windspeed (OR = 0.98), and travel distance > 15 miles (OR = 0.67). Conclusion Patient age, gender, travel distance, appointment type, and weather were all significantly associated with rates of patient cancellations and no-shows. These risk factors could lead to interventions to improve appointment adherence and patient retention. Weather is an under-analyzed factor in patient follow-up rates that warrants further investigation.

Classification of risk levels for snow damage estimation considering socioeconomic factors in South Korea

In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

A Study on Avalanche-Triggering Factors and Activity Characteristics in Aerxiangou, West Tianshan Mountains, China

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability >χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Synoptic control over winter snowfall variability observed in a remote site of Apennine Mountains (Italy), 1884–2015

Abstract. This work presents a new, very long snowfall time series collected in a remote site of Italian Apennine mountains (Montevergine Observatory, 1280 m above sea level). After a careful check, based on quality control tests and homogenization procedures, the available data (i.e. daily height of new snow) have been aggregated over winter season (December–February) to study the long-term variability for the period 1884–2020. The main evidence emerging from this analysis lies in (i) the strong interannual variability of winter snowfall amounts, (ii) the absence of a relevant trend from the late 19th century to the mid-1970s, (iii) the strong reduction in the snowfall amount and frequency of occurrence from the mid-1970s to the end of the 1990s and (iv) the increase in average snowfall amount and frequency of occurrence in the past 20 years. Moreover, this study shed light on the relationship between the snowfall variability observed in Montevergine and the large-scale atmospheric circulation. Six different synoptic types, describing the meteorological scenarios triggering the snow events in the study area, have been identified by means of a cluster analysis, using two essential atmospheric variables, the 500 hPa geopotential height and the sea level pressure (both retrieved from the third version of the Twentieth Century Reanalysis dataset, which is available for the 1884–2015 period). Such patterns trace out scenarios characterized by the presence of a blocking high-pressure anomaly over Scandinavia or the North Atlantic and by a cold air outbreak involving both maritime and continental cold air masses. A further analysis demonstrates that the identified synoptic types are strongly related with different teleconnection patterns, i.e. the Arctic Oscillation (AO), the Eastern Atlantic Western Russia (EAWR), the Eastern Mediterranean Pattern (EMP), the North Atlantic Oscillation (NAO) and the Scandinavian pattern (SCAND), that govern the European winter atmospheric variability. The relevant decline in snowfall frequency and amounts between the 1970s and 1990s can be mainly ascribed to the strong positive trend of AO and NAO indices, which determined, in turn, a decrease in the incidence of patterns associated with the advection, in central Mediterranean area, of moist and cold arctic maritime air masses. The recent increase in average snowfall amounts can be explained by the reverse trend of the AO index and by the prevalence of a neutral or negative EAWR pattern.

Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III &gt; IV &gt; I &gt; II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

Statistical characteristics of the spatial distribution of wind and snowfall in the Beijing\u2013Tianjin\u2013Hebei Region

In the current design specification of building structure, the basic wind pressure and basic snow pressure are two independent values, and it is impossible to acquire both of these values when snow and wind occur at the same time. Taking parameters such as snowfall intensity, snowfall amount, wind speed, and wind direction as indicators, the value of the combined distribution of wind and snowfall in the Beijing–Tianjin–Hebei region of China was extracted. A joint distribution map of the daily average snowfall among the top-ten largest consecutive snowfall events and the daily average wind scale from the first day of snowfall to the fifth day after the snowfall were obtained. The study found that after a heavy snowfall in the Zhangjiakou area, the accumulated wind power was large and, although the wind speed was favorable for the occurrence of snowdrifts, the snowfall was light. After a heavy snowfall in the Shijiazhuang area, the accumulated wind power was small, and the probability of snowdrift formation was low. In the eastern regions of Cangzhou, Beijing, Tianjin, Tangshan, and Qinhuangdao, the accumulated wind force was relatively large after a heavy snowfall, and the probability of windblown snow was relatively high.

Arctic Clouds and Precipitation in the Community Earth System Model Version 2.

The Arctic climate is changing rapidly, warming at about twice the rate of the planet. Global climate models (GCMs) are invaluable tools both for understanding the drivers of these changes and predicting future Arctic climate evolution. While GCMs are continually improving, there remain difficulties in representing cloud processes which occur on scales smaller than GCM resolution. Since clouds influence the Arctic energy and water cycles, their accurate representation in models is critical for robust future projections. In this work, we examine the representation of Arctic clouds and precipitation in the Community Earth System Model (CESM) with the Community Atmosphere Model (CAM), comparing the newly released version (CESM2 with CAM6) with its predecessor (CESM1 with CAM5). To isolate changes in the Arctic mean state, we compare preindustrial control runs. Arctic cloud ice has decreased slightly, while cloud water has increased dramatically in CESM2. Annual mean liquid‐containing cloud (LCC) frequency has increased from 19% in CESM1 to 51% in CESM2. Since LCCs strongly modulate downwelling radiation at the surface, their increase has led to an increase in mean downwelling longwave (+22 W m) and corresponding decrease in downwelling shortwave (23 W m) radiation. The mean Arctic surface temperature increased from 257 K in CESM1 to 260 K in CESM2, with the largest seasonal difference in winter (+6 K). Annual average snowfall has decreased slightly (1 mm month), while rainfall has increased (+5 mm month).

An attempt to explain recent changes in European snowfall extremes

Abstract. The goal of this work is to investigate and explain recent changes in total and maximum yearly snowfall from daily data in light of current global warming or the interdecadal variability of atmospheric circulation. We focus on the period 1979–2018 and compare two different datasets: the ERA5 reanalysis data and the E-OBSv20.0 data, where snowfall is identified from rainfall by applying a threshold to temperature. We compute changes as differences from quantities computed for the periods 1999–2018 and 1979–1998. On the one hand, we show that the decline in average snowfall observed in almost all European regions is coherent with previous findings and can be linked to global warming. On the other hand, we observe contrasting changes in maxima and sometimes disagreement in the sign of changes in the two datasets. Coherent positive trends are found for a few countries in the Balkans. These have been investigated in details by looking at modifications in the atmospheric weather patterns as well as local thermodynamic factors concurring to large snowfall events. We link these changes to the stronger prevalence of Atlantic Ridge or blocking patterns associated with deeper cyclonic structures over the Adriatic and Tyrrhenian seas. These cyclones find warmer surfaces and large availability of humidity and convective available potential energy (CAPE), thus producing large snowfall amounts, enhanced by the Stau effect on the Balkan topography.

Quantifying spatiotemporal occupancy dynamics and multi‐year core‐use areas at a species range boundary

AbstractAimMany species face large‐scale range contractions and predicted distributional shifts in response to climate change, shifting forest characteristics and anthropogenic disturbances. Canada lynx (Lynx canadensis) are listed as threatened under the U.S. Endangered Species Act and were recently recommended for delisting. Predicted climate‐driven losses in habitat quality and quantity may negatively affect the north‐eastern Minnesota lynx population, one of the six remaining resident populations in the contiguous United States. We develop a large‐scale monitoring protocol and dynamic occupancy modelling framework to identify multi‐year core‐use areas and track spatiotemporal occurrence at the southern periphery of the species range.LocationNorth‐eastern Minnesota lynx geographic unit, Superior National Forest and designated critical habitat, Minnesota, USA.MethodsSpatially and temporally replicated snow track surveys were used to collect lynx detection/non‐detection data across five winters (2014–15 to 2018–19) covering &gt;17,000 km within the 22,100 km2 study area. We used a dynamic occupancy model to evaluate lynx occupancy, persistence, colonization and habitat covariates affecting these processes.ResultsLynx occupancy probabilities displayed high spatial and temporal variability, with grid cell‐specific probabilities ranging from 0.0 in periphery regions to consistently near 1.0 in multi‐year core‐use areas, indicating low turnover rates in those areas. Lynx colonization and persistence increased in areas with more evergreen forest and greater average snowfall, while forest characteristics (3–5 and 10–30 m vegetation density) had mixed relationships with occupancy dynamics. We identified 55 grid cells classified as multi‐year core‐use areas across relatively contiguous regions of high average snowfall and per cent conifer forest.Main conclusionsOur study demonstrates a landscape‐scale multi‐year monitoring programme assessing the effects of habitat characteristics and anthropogenic factors on species distributional changes and landscape‐level occupancy dynamics. Our framework incorporating landscape‐scale resource selection, core‐use area concepts and dynamic occupancy models provides a flexible approach to identify population‐level mechanisms driving species persistence and key areas for conservation protection.

Theoretical snow period and assessment of skiing climate suitability: With Changbai Mountain Ski Resort as an example

To analyze the climatic characteristics of snow resources and quantitatively evaluate the climatic suitability of skiing, we proposed the concept of theoretical snow period from the perspective of climate based on the synoptic principle. We set threshold values of different suitability degrees of three indices closely related to skiing, including air temperature, wind speed, and precipitation. The conversion function of each index was designed after normalization. Based on grey relational theory and Euclidean distance method, we established evaluation model of ski sports climate suitability index, with Changbai Mountain Ski Resort as an example. The results showed that snow resource in Changbai Mountain area was rich. From 1981 to 2018, the average snowfall during the theoretical snow period was 64.6 mm. Under the background of climate change, the average number of snow days and snowfall decreased slightly over the years, with the starting time of snow season being delayed and the ending time being advanced. The number of snow days in the early winter (from the starting time of snow season to the end of December) was significantly less than that in the later winter (from the next January to the end of snow season). Climate in Changbai Mountain was highly suitable for skiing during the snow period. The most suitable and relatively suitable days with respect to air temperature, wind speed and precipitation accounted for 91.9%, 91.8%, and 94.6% of the total, respectively. The cumulative number of days for ski comprehensive weather suita-bility accounted for 99.7%, indicating that most days were suitable for skiing. The concept of theoretical snow period in this study had made up for the problems caused by the lack of meteorological observation data of the first and last snow event on the study of snow resources. The climate suitabi-lity evaluation model of skiing could help make management decision for the development and operation of ski resorts and scientific support for skiing enthusiasts.

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective.

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth's atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global precipitation Measurement (GPM) Core Observatory satellite and CloudSat's Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow-rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow-rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM's Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)-snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR-DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z-S approach.

The sensitivity of snowfall to weather states over Sweden

Abstract. For a high-latitude country like Sweden snowfall is an important contributor to the regional water cycle. Furthermore, snowfall impacts surface properties, affects atmospheric thermodynamics, has implications for traffic and logistics management, disaster preparedness, and also impacts climate through changes in surface albedo and turbulent heat fluxes. For Sweden it has been shown that large-scale atmospheric circulation patterns, or weather states, are important for precipitation variability. Although the link between atmospheric circulation patterns and precipitation has been investigated for rainfall there are no studies focused on the sensitivity of snowfall to weather states over Sweden.In this work we investigate the response of snowfall to eight selected weather states. These weather states consist of four dominant wind directions together with cyclonic and anticyclonic circulation patterns and enhanced positive and negative phases of the North Atlantic Oscillation. The presented analysis is based on multiple data sources, such as ground-based radar measurements, satellite observations, spatially interpolated in situ observations, and reanalysis data. The data from these sources converge to underline the sensitivity of falling snow over Sweden to the different weather states.In this paper we examine both average snowfall intensities and snowfall accumulations associated with the different weather states. It is shown that, even though the heaviest snowfall intensities occur during conditions with winds from the south-west, the largest contribution to snowfall accumulation arrives with winds from the south-east. Large differences in snowfall due to variations in the North Atlantic Oscillation are shown as well as a strong effect of cyclonic and anticyclonic circulation patterns. Satellite observations are used to reveal the vertical structures of snowfall during the different weather states.

Climate of Canaan Valley

Abstract In this paper, we present and examine climate data from 1944 to 2002 for Canaan Valley, WV, including average, extreme, and monthly and seasonal temperature, and precipitation and snowfall amounts. The data, collected over decades by several dedicated National Weather Service cooperative observers, indicate that Canaan Valley's “cash crop” may indeed be its climate. The Canaan Valley has summer temperatures similar to those found in northern New England, an average seasonal snowfall higher than any large city in the US, and a shorter growing season than that of Fairbanks, AK. We highlight the area's exceptional climate and compare it to other well known locations. We also present and assess climate trends, including some relationships to the El Nino Southern Oscillation state, in Canaan Valley's 57-year record.

Impacts of changes in climate on mountain water resources of Himachal Pradesh

The study examined the impacts of climate change on water resources, viz., surplus / deficit water balance, snowfall trends and surface water flow of major rivers of Himachal Pradesh. The analysis on surplus water balance clearly showed decreasing trends in all the agro-climatic zones during past three decades. Maximum availability of surplus water balance period showed a shift during July to August and registered a decrease of 35.7 percent. The low hill regions exhibited water surplus during kharif season and water deficit during rabi season, while, mid hill regions exhibited water deficit during kharif season and water surplus during rabi season during past three decades. The Mann-Kendall trend test of snowfall indicated a decrease in monthly average snowfall from September to December. The trend analysis further showed increasing trends of snowfall during January and February indicating delay in snowfall during winters. The decreasing trends of snowfall were observed during September to December and March to May indicating shrinking winter period. The significant decreasing trend of snowfall was observed during December and May. The trend test revealed significant decrease of surface water flow of river Beas at Manali and Parvati at Aut in Kullu district during all the months. The water flow of river Sutlej at Songtong in Kinnaur district also showed significant decreasing trends during winter months. Similarly, the annual water inflow of the Sutlej River at Bhakhra Dam showed decreasing trend also. Thus, the studies clearly indicated that water resources have been impacted due to changes in climatic conditions in mountains of Himachal Pradesh during past three decades.

The increasing incidence of snowboard-related trauma

PurposeTo investigate injuries among children and adolescents who participate in downhill sports. MethodsWe collected trauma registry data (January 1999-May 2006) from a level 1 pediatric trauma center with an average snowfall of 28 in (71 cm)/y. Cases were analyzed for injury mechanism, injury type, organ injured, Injury Severity Score, age, sex, and whether or not an operation was required. ResultsThere were 57 snowboarders and 22 skiers admitted during the study period. Forty-one (72%) of snowboarders and 16 (73%) of skiers required operations; 32 (56%) of snowboarders and 9 (41%) of skiers sustained fractures; and 14 (25%) of snowboarders and 6 (27%) of skiers sustained abdominal injuries. (P = NS for all comparisons). Serious splenic injuries were more common in snowboarders (14% vs 4%), but the difference was not statistically significant. All skiing injuries occurred at recreational facilities (commercial skiing areas), whereas 12% of snowboard injuries occurred at home, other residence, or public parks (P = .08). The most striking finding is the rising number of snowboarding injuries and the relatively stable rate of skiing injuries (see graph). ConclusionsAs the popularity of snowboarding rises, snowboarding injuries in children are increasing. Pediatric surgeons should be wary of the “snowboard spleen.”

Differential Snowpack Accumulation and Water Dynamics in Aspen and Conifer Communities: Implications for Water Yield and Ecosystem Function

Early succession aspen and late succession conifer forests have different architecture and physiology affecting hydrologic transfer processes. An evaluation of water pools and fluxes was used to determine differences in the hydrologic dynamics between stands of quaking aspen (Populus tremuloides) and associated stands of mixed conifer consisting of white fir (Abies concolor), Douglas-fir (Pseudotsuga menziesii), and Engelmann spruce (Picea engelmannii). In 2005 and 2006, measurements of snow water accumulation, snow ablation (melt), soil water content, snowpack sublimation, and evapotranspiration (ET) were measured in adjacent aspen and conifer stands. Peak snow water equivalent (SWE) averaged 34–44% higher in aspen in 2005 (average snow fall) and 2006 (above average snow fall), respectively, whereas snow ablation rates were greater in aspen stands (21 mm day−1) compared to conifer stands (11 mm day−1). When changes in soil water content (due to over-winter snowmelt) were combined with peak snow accumulation in 2006, aspen had greater potential (42–83%) water yield for runoff and groundwater recharge. Snowpack sublimation during the ablation period was not significantly different between meadow, aspen, and conifer sites and comprised less than 5% of the winter precipitation. Extended conifer transpiration in spring and fall did not contribute to large differences in water yield (<28 mm y−1). Summertime ET rates were higher in aspen plots (3.6 mm day−1) than in conifer plots (2.7 mm day−1), and differences in net ET largely reflected soil column porosity. This study shows that the largest differences in annual water yield between aspen and conifer stands result from differences in SWE and net summertime ET. Although SWE and accumulation of water in soil was greater in aspen, it was partly offset by greater net annual ET losses in aspen.

Biomass Yield and Biofuel Quality of Switchgrass Harvested in Fall or Spring

Seasonal time of switchgrass (Panicum virgatum L.) harvest affects yield and biofuel quality and balancing these two components may vary depending on conversion system. A field study compared fall and spring harvest measuring biomass yield, element concentration, carbohydrate characterization, and total synthetic gas production as indicators of biofuel quality for direct combustion, ethanol production, and gasification systems for generation of energy. Switchgrass yields decreased almost 40% (from about 7–4.4 Mg ha−1) in winters with above average snowfall when harvest was delayed over winter until spring. The moisture concentration also decreased (from about 350–70 g kg−1) only reaching low enough levels for safe storage by spring. About 10% of the yield reduction during winter resulted from decreases in tiller mass; however, almost 90% of the yield reduction was due to an increase in biomass left behind by the baler. Mineral element concentrations generally decreased with the delay in harvest until spring. Energy yield from gasification did not decrease on a unit biomass basis, whereas ethanol production was variable depending on the assessment method. When expressed on a unit area basis, energy yield decreased. Biofuel conversion systems may determine harvest timing. For direct combustion, the reduced mineral concentrations in spring‐harvested biomass are desirable. For ethanol fermentation and gasification systems, however, lignocellulose yield may be more important. On conservations lands, the wildlife cover provided by switchgrass over the winter may increase the desirability of spring harvest along with the higher biofuel quality.

An exploration of the controls of pre-instrumental streamflow using multiple tree-ring proxies

Previous studies have used tree-ring chronologies from several species to develop reconstructions of precipitation, temperature, streamflow and glacier mass balance for sites in Banff National Park, Alberta. This study examines the variability in a >300-year summer streamflow reconstruction for the Bow River at Banff in conjunction with changes in the major contributors to streamflow (glacier melt, winter and summer pecipitation). Reconstructed winter mass balance for Peyto Glacier is used as a surrogate for winter precipitation and April–August precipitation is reconstructed for Banff. Streamflow variability correlates most highly with winter precipitation and periods of high flow follow above average snowfall in the previous winter (high winter balance) and in some cases also with above normal summer precipitation. A clear response to changes in summer mass balance at Peyto Glacier (i.e. summer glacier melting) cannot be identified in this summer discharge record. Problems developing physically realistic flow reconstructions for snowmelt dominated rivers from summer sensitive tree-ring chronologies are also discussed.

Interactions between mass balance, atmospheric circulation, and recent climate change on the Djankuat Glacier, Caucasus Mountains, Russia

This paper reports recent changes in the mass balance record from the Djankuat Glacier, central greater Caucasus, Russia, and investigates possible relationships between the components of mass balance, local climate, and distant atmospheric forcing. The results clearly show that a strong warming signal has emerged in the central greater Caucasus, particularly since the 1993/1994 mass balance year, and this has led to a significant increase in the summer ablation of Djankuat. At the same time, there has been no compensating consistent increase in winter precipitation and accumulation leading to the strong net loss of mass and increase in glacier runoff. Interannual variability in ablation and accumulation is partly associated with certain major patterns of Northern Hemisphere climatic variability. The positive phase of the North Pacific (NP) teleconnection pattern forces negative geopotential height and temperature anomalies over the Caucasus in summer and results in reduced summer melt, such as in the early 1990s, when positive NP extremes resulted in a temporary decline in ablation rates. The positive phase of the NP is related to El Niño‐Southern Oscillation, and it is possible that a teleconnection between the tropical Pacific sea surface temperatures and summer air temperatures in the Caucasus is bridged through the NP pattern. More recently, the NP pattern was predominantly negative, and this distant moderating forcing on summer ablation in the Caucasus was absent. Statistically significant correlations are observed between accumulation and the Scandinavian (SCA) teleconnection pattern. The frequent occurrence of the positive SCA phase at the beginning of accumulation season results in lower than average snowfall and reduced accumulation. The relationship between the North Atlantic Oscillation (NAO), Arctic Oscillation, and accumulation is weak, although positive precipitation anomalies in the winter months are associated with the negative phase of the NAO. A stronger positive correlation is observed between accumulation on Djankuat and geopotential height over the Bay of Biscay unrelated to the established modes of the Northern Hemisphere climatic variability. These results imply that the mass balance of Djankuat is sensitive to the natural variability in the climate system. Distant forcing, however, explains only 16% of the variance in the ablation record and cannot fully explain the recent increase in ablation and negative mass balance.