Occurrence Of Snow Research Articles

Major factors affecting the snow simulations by the JULES in New Zealand

AbstractSnow frequently occurs over New Zealand. The seasonal snow accumulation can be about 3 m deep in early spring at some locations. Thus, for numerical weather prediction and climate modelling over New Zealand, a reliable, sophisticated snow model that can properly describe the physical processes associated with snow cover is required. In the past, owing to a lack of snow observations, modelling and verification of snow processes was limited over New Zealand. In the present study, the multilayer snow scheme of the Joint UK Land Environment Simulator (JULES)—the land surface model used in New Zealand regional weather and climate models—was used to simulate the snow water equivalent (SWE) and snow density at two sites in the South Island of New Zealand. The model captured seasonal and interannual variability in snow accumulation and melt. However, large errors in the simulated SWE (up to 300 mm) were found during snowmelt periods and in relatively warm winters when a significant amount of liquid water was present in snow. Sensitivity tests showed that errors in the simulated long wave radiation (a negative bias of about 6 W/m2), snow albedo (0.05–0.10) and air temperature for snow occurrence ( T c, about 0.5 K) were the major factors causing large errors in the simulated SWE. In addition, the simulated snow density was lower than observed. The relatively warm and humid maritime climate of New Zealand appears to make snow simulation in New Zealand more sensitive to long wave radiation, snow albedo and Tc than for continents.

D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps

ABSTRACTSoil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon.

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.

A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.)

Abstract. A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In situ data consist of wind (speed and direction), snow depth and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of snow particle counters (2010–2016). Observations span the period from 1 December to 31 March for each winter season from 2000–2001 to 2015–2016. The time resolution has varied from 15 min until 2014 to 10 min for the last years. Atmospheric data from the meteorological reanalysis are also provided from 1 August 2000 to 1 August 2016. A digital elevation model (DEM) of the study area (1.5 km2) at 1 m resolution is also provided in RGF 93 Lambert 93 coordinates. This data set has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the CRYOBS-CLIM observatory (the CRYosphere, an OBServatory of the CLIMate), which is a part of the national research infrastructure OZCAR (Critical Zone Observatories – Application and Research) and have been a Global Cryospheric Watch Cryonet site since 2017. The data are available from the repository of the OSUG data centre https://doi.org/10.17178/CRYOBSCLIM.CLB.all.

Flood event attribution and damage estimation using national‐scale grid‐based modelling: Winter 2013/2014 in Great Britain

A sequence of major flood events in Britain over the last two decades has prompted questions about the influence of anthropogenic greenhouse gas emissions on flood risk. Such questions are difficult to answer definitively, as a range of other factors are involved, but modelling techniques allow an assessment of how much the chance of occurrence of an event could have been altered by emissions. Here the floods of winter 2013/2014 in Great Britain are assessed by combining ensembles of climate model data with a national‐scale hydrological model and, for one severely impacted river basin (the Thames), a detailed analysis of flood inundation and the increased number of residential properties placed at risk. One climate model ensemble represents the range of possible weather under the current climate, while 11 alternative ensembles represent the weather as it could have been had past emissions not occurred. The pooled ensemble results show that emissions are likely to have increased the chance of occurrence of these floods across much of the country, with a stronger influence on longer duration peaks (~10 days or more) than for shorter durations (consistent with observations). However, there is substantial variation in results between alternative ensembles, with some suggesting likely decreases in the chance of flood occurrence, at least in some regions of the country. The influence on flows and property flooding varies spatially, due to both spatial variation in the influence on precipitation and variation in physical properties that affect the transformation of precipitation to river flow and flood impacts, including flood defences. This complexity highlights the importance of using hydrological modelling to attribute hydrological impacts from meteorological changes. Changes in snow occurrence in a warming climate are also shown to be important, with effects varying spatially.

Studies on woloszynskioid dinoflagellates IX: ultrastructure, cyst formation and phylogeny of the ‘red-snow’ alga Borghiella pascheri (Suchlandt) Moestrup (= Glenodinium pascheri, Woloszynskia pascheri, Gyrodinium nivalis)

ABSTRACTRed snow caused by dinoflagellates is a phenomenon rarely reported, described from the European Alps from 1914 onwards, and subsequently observed outside Europe on several occasions in Ontario, Canada. Considerable taxonomic confusion exists regarding the identity of the organism(s) causing red snow, but the most recent occurrence in 2016 in Ontario has now allowed detailed studies, including LM, SEM, TEM and molecular sequencing of the causative species. We conclude that the two species originally described as the cause of red snow, Glenodinium pascheri and Gyrodinium nivale, are synonymous and that the appropriate name for the organism is Borghiella pascheri (syn. Woloszynskia pascheri) as suggested by Moestrup & Calado in the recent volume of the Süsswasserflora. The central part of Borghiella pascheri cells is tomato red and filled with numerous organelles, whose ultrastructure indicates modified chloroplasts. Lack of cultures has prevented chemical characterization of the red pigment. Formation of temporary cysts was common in the samples. Transformation of the motile cells into temporary cysts was followed in detail, and the cysts were shown to be surrounded by the fused inner membranes of the amphiesmal vesicles, which became the cell membrane of the cysts, covered by the fused pellicle precursors. The cell membrane from the motile cell was discarded together with the outer amphiesmal vesicle membrane and the thin thecal plates, and the temporary cysts were therefore not surrounded by any pattern of vesicles. Sexual reproduction resulted in the formation of hypnozygotes. Although the species possessed several unusual features, DNA sequencing showed it to belong to Borghiella. The culture established in 1965 from the Botanical Garden in Göttingen, Germany and generally identified as Woloszynskia pascheri belongs to a separate species of Borghiella, to be described separately.The occurrence of red snow caused by dinoflagellates is discussed.

Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Cumuliform snowfall seasonal variability is studied using a multi‐year CloudSat snowfall rate and cloud classification retrieval dataset. Microwave radiometer sea ice concentration datasets are also utilized to illustrate the intimate link between oceanic cumuliform snowfall production and decreased sea ice coverage. Three metrics are calculated to illustrate seasonal cumuliform snowfall signatures: (a) cumuliform snowfall frequency of occurrence, (b) mean cumuliform snowfall rate, and (c) fraction of snowfall attributed to cumuliform snowfall events. Distinct seasonal cumuliform snowfall cycles are observed over the Northern Hemispheric oceans. Cumuliform snowfall frequency of occurrence (mean snowfall rate) peaks in months SON (DJF) at most latitudes. Maximum mean cumuliform snowfall rates exceed 300 mm/year in various North Atlantic Ocean locations, with DJF exhibiting the largest areal extent of higher snowfall rates. Cumuliform snow occurrence fraction frequently exceeds 0.5, but regional seasonal sensitivity is observed where transient sea ice coverage exists. Annual snowfall rate fraction attributed to cumuliform snow does not vary appreciably during SON, DJF and MAM north of ∼70°N, but seasonal zonal variability is evident south of this latitudinal threshold. Land cumuliform snowfall features do not universally display strong seasonal signals. Southern Hemisphere seasonal results indicate a strong mean cumuliform snowfall rate maximum (minimum) in JJA (DJF) accompanied by a seasonal latitudinal shift in the snowfall rate peak. Maximum regional snowfall rates exceed 300 mm/year over a broader area compared to the Northern Hemisphere. Cumuliform snowfall production is again strongly linked to seasonal sea ice coverage. Southern Hemispheric cumuliform snowfall occurrence and snowfall rate fraction seasonality is not as obvious as in the Northern Hemisphere, but some latitudinal zones experience ∼5–20% seasonal variability in these quantities. Typical cumuliform snowfall fractions range from 0.4 to 0.6 in the prominent cumuliform snowfall belt covering most of the Southern Ocean. Some subtle seasonal cumuliform snowfall signatures are observed over Antarctica.

In this issue of Weather

Although we may have an image of snow as a typical feature of winter in Britain, its occurrence is variable and, of course, few records go back beyond the establishment of formal observation networks, little more than 100 years ago. However, we are fortunate that keen amateur observers, as well as many parish records, included the occurrence of snow. The establishment of a long‐term climatological series is also important to our growing knowledge of climate change. The winter of 1614/15 (new calendar) saw deep snow in many areas, as revealed by Lucy Veale, Georgina Endfield and James Bowen in ‘The “Great Snow” of winter 1614/1615 in England’, beginning on p. 3.Large bodies of water often remain relatively warm well into the winter months – not least the Caspian Sea, the main inland water body of western Asia. The surrounding continent becomes very cold by the end of January, but the southern Caspian Sea may be more than 10 degC warmer, remaining unfrozen. This is a great source of humidity for convection, and potentially unstable northeasterly air masses may bring significant snow to its southwestern coasts. This was the case in winter 2014, as described by Parvin Ghafarian, Nafiseh Pegahfar and Elaheh Owlad in ‘Multiscale analysis of lake‐effect snow over the southwest coast of the Caspian Sea (31 January‐5 February 2014)’ on p. 9.Snowfall is a feature of winter on the slopes of the Himalaya. As elsewhere, accumulations and its significance vary. However, Kashmir experienced significant snowfall, causing loss of life and damage at this time last year. This is described by Mohammd Rafiq and Anoop Kumar Mishra in ‘A study of heavy snowfall in Kashmir, India in January 2017’ on p. 15.On p. 20, we move on to look at the effect of a mid‐latitude storm. Storms bring not only strong winds, but usually, copious rainfall and the associated risk of floods. This was the case from the storm of 22–24 December 2013 in the British Isles. The study by Bruno de Campos and Vanessa Silveira Barreto Carvalho, ‘The effect of the Welsh mountains on rainfall distribution over Wales and England: a case study of a severe extratropical storm’ looks at the role of orography in the development and amount of rainfall.The Yellow River and its tributaries occupy much of northern China, rising high in the mountains of Qing‐Hi, meandering eastwards into the East China Sea. On p. 27, ‘Spatio‐temporal temperature variations in the Chinese Yellow River basin from 1981 to 2013’ by Lei Wu, Xia Liu and Xiaoyi Ma studies this area, mapping temperature changes across the area in recent times.

Operational implementation and evaluation of a blowing snow scheme for avalanche hazard forecasting

In alpine terrain, blowing snow events strongly affect the local evolution of the avalanche danger and must be taken into account by avalanche hazard forecasters. This study presents the implementation and the evaluation of the blowing snow scheme Sytron into the operational chain for avalanche hazard forecasting (named S2M) used in the main French mountain ranges. S2M-Sytron provides information on blowing snow occurrence and intensity per 300-m elevation bands and aspects for several regions of the French mountains. The wind forcing is provided by the meteorological analysis system SAFRAN. S2M-Sytron was evaluated for winter 2015/16 at 11 automatic stations measuring wind speed and blowing snow fluxes in the French Alps. The system detects 55% of blowing snow days with less than 10% of false alarms. S2M-Sytron captures the occurrence of blowing snow events with and without concurrent snowfall. Improvements are obtained when considering an updated parameterization for the properties of falling snow which reduces the threshold velocity for freshly fallen snow. Using observed wind speed instead of SAFRAN wind speed to drive Sytron shows further improvements at stations where SAFRAN wind speed differs from the observations due to local topographic features. Overall, S2M-Sytron provides a regional blowing snow assessment but cannot fully reproduce the local intensity of blowing snow events.

Neve efetiva ou apenas referência? relação entre neve e fluxo turístico a Gramado(RS)

Gramado é o polo turístico do estado do Rio Grande do Sul. Apresenta um clima subtropical úmido (Cfb), com invernos nos quais são frequentes temperaturas abaixo de 0°C e geadas, havendo também possibilidade de ocorrência de neve. O estudo realizado faz uma comparação entre a ocorrência de neve e o fluxo turístico em Gramado/RS. Foram usados os dados da estação meteorológica automática (INMET) de Canela/RS, registrados para os períodos de outono/inverno dos anos de 2009 a 2012. Para o mesmo período foram também obtidos e organizados dados do fluxo de veículos das três praças de pedágio que dão acesso à cidade de Gramado. Os dados diários foram justapostos de forma a poderem ser comparados. Também se analisou os calendários de eventos para o mesmo período. Constatou-se que o fluxo de veículos nas praças de pedágio se relaciona mais com os dias dos finais-de-semana com eventos programados, do que com a neve anunciada ou ocorrida. O estudo realizando demonstra que, em Gramado, o fenômeno natural “neve” cede cada vez mais lugar ao artefato “neve” resultante do engenho humano.

Blowing snow detection from ground-based ceilometers: application to East Antarctica

Abstract. Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30 m height. Results show that 78 % of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36 % of the time at Neumayer (2011–2015) and 13 % at PE station (2010–2016). Blowing snow occurrence peaks during the austral winter and shows around 5 % interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92 % of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300 m a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

Maximum temperature drove snow cover expansion from the Arctic, 2000\u20132008

Investigating annual phenology of snow cover around the circumpolar Arctic is of significance for better grasping the effect of environment variation on global climate change, and previous studies found that temperature is the kernel climate feature interlinking with snow onset. However, how temperature closely drives snow cover expansion has not been fully exploited. Our analysis based on the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) (i.e., SSM/I-SSMIS) data during 2000–2008 showed that the snow onset date (Do) was primarily driven by the maximum temperature approximately at the 22nd day in advance (termed as the optimal period, OP) in the Northern Hemisphere. Specifically, the spatial patterns of the Do trends are similar with the previous finding, e.g., east Europe and west Asia exhibiting the most notable Do delay, and the OP days in latitude show the principal trend of first decreasing and then increasing. These characteristics can be attributed to the variation of the maximum temperature feature in latitude. Overall, the derivation of the statistical rules of temperature driving snow cover expansion from the Arctic facilitates predicting the occurrence of snow and understanding various terrestrial processes.

Some attributes of snow occurrence and snowmelt/sublimation rates in the Lesotho Highlands: environmental implications

We present attributes of snow occurrence and dissipation rates (melt and sublimation) for the Lesotho Highlands, based on remotely-sensed MODIS images from 2003–2016. Multi-temporal imagery is used, with SNOMAP and NDSI algorithms applied to MODIS Rapid Response images. The spatial extent of snow loss was determined by daily repeat measurements of snow coverage, which was calculated from each filtered and trimmed MODIS SNOMAP image using the ArcGIS Spatial Analyst tool. Results indicate an average of 11.5 snowfalls per annum for the years 2003–2016; with snow longevity averaging ca. 10 days following individual snowfalls in mid-winter. Snow cover on the highest south-facing slopes persists longest during the months of June to August, in particular along the southern Drakensberg where it averages ca. 58 days during these 3 months. Mean daily melt increases by 1.6% per 1°C rise during the first 5 days post-snowfall, and by 3.3% per 1°C rise for 6 to 10 days post-snowfall. However, snowmelt rates are spatially highly variable given other factors such as wind deflation and wind-induced sublimation. The observed snow trends have important implications for biosystem functioning, regional climate and hydrology, earth surface processes, and rural livelihoods in the Lesotho Highlands.

Regime of snow cover in the Baikal region under the climate change

Spatial and temporal variability of dates of snow cover setting-up and loss (dates of the snow cover appearance and disappearance, and formation and destruction of stable snow cover) and duration of stable snow occurrence on a territory of the Baikal region were analyzed from data of observations made at meteorological stations in 1981–2010. Compared to the previous long-term period prior to 1980, mean monthly air temperatures rose in every month during the cold season. Significant changes in dates of the snow cover setting-up were observed only at individual stations, while the duration of the snow occurrence does not show such changes. Typical period of fluctuations for the last characteristic is the quasi-biennial, and more rare it is 3-4 years. In relation to the previous multi-year period, the largest variability of dates of the snow-cover setting-up as well as increase of number of dates and so duration of its occurrence (up to 19 days) take place on the South-West‑ ern coast of Lake Baikal (Bolshoe Goloustnoe). In plain and mountain regions of the Baikal region, variability of these dates does not exceed 10–15 days. Compared to the earlier period (before 1980), it was noticed that the snow appear earlier and disappear later (with the difference of about five days). In most cases, deviation of ear‑ lier dates of formation and destruction of stable snow cover is within 10 days. During 1981–2010, a number of days with snow cover increased in river valleys of the taiga belt in the Eastern Sayan (GMR Verchnyaya Gutara) by 11 days, while in the mountain Khamar-Daban (GMS Khamar-Daban) – by 15 days.

Snow occurrence time on the Russia’s territory in the early 21st century (from satellite data)

Time of the snow cover appearance, existence and disappearance on the Russia’s territory in the early 21st century (2000–2015) was corrected using the MODIS/Terra satellite data (the 8-day discreteness, and the 0.5×0.5° resolution). The satellite data errors were estimated from data of the ground stations observations. The errors were found to be maximal in autumn and minimal in spring. The relationship between the snow cover characteristics and the climate ones was investigated using data obtained at the ground-based stations together with correlation between dates of snow appearance and loss and the climate parameters. The dependences obtained were tested by means of correlation and regression analysis over the longitudinal sectors. Significant coefficients of correlation (the Student criterion of probability was equal to 0.95) were found between time of the snow cover presence and dates of the temperature drop below 0 °С and the amount of days with negative temperatures. Changes in the climate characteristics result in that due to decreasing of the solid precipitation in winter time the snow presence duration becomes shorter over the European part of Russia and in the Western Siberia. The shortening in the Middle Siberia is caused by the spring warming. Durations of the snow occurrence in the Far East area are different. On the Chukotka peninsula the duration is longer because of the autumn fall in temperature while in the Kamchatka region the snow occurrence time is shorter due to significant decrease of a period with negative temperatures in both the autumn and spring seasons.

Snow occurrence changes over the central and eastern United States under future warming scenarios.

Changes of snow occurrence across the central and eastern United States under future warming for the late 21st century are investigated by applying an empirical hyperbolic tangent function to both observed and downscaled high spatial resolution (~12.5 km) daily temperature and precipitation, to compare the historical (1981–2000) and future (2081–2100) snow occurrence. The observed distributions of snow frequency show that snow-rain transition zones are mainly zonally distributed, since they are largely determined by temperature, with slight shifts to the south over the Appalachian Mountains. The snow-rain transition zone is located around 38–46°N for November and March, and 32–42°N for winter months (DJF). These observed patterns are reproduced well for the historical period by an ensemble average of multiple general circulation models (GCMs). The probabilistic projections show that the snow-rain transition zone will shift to the north under the background of global warming at magnitudes of 2–6 °C, indicating that large areas will experience a partial, or even a very large, loss of snow occurrence in the future. The northward shifts are about 2° latitude under the representative concentration pathways 4.5 (RCP4.5) scenario and 4° latitude under the RCP8.5 scenario. The percentages of the area losing snow occurrence are also assessed.

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

Abstract. Mountain areas in Mediterranean regions constitute key monitoring points for climate variability and its impacts, but long time datasets are not always available due to the difficult access to high areas, relevant for capturing temperature and precipitation regimes, and the predominance of cloudy remote sensing images during the snow season. Sierra Nevada National Park (South Spain), with altitudes higher than 3500 m a.s.l., is part of the Global Change in Mountain Regions network. Snow occurrence just 40 km from the seaside determines a wide range of biodiversity, a snowmelt fluvial regime, and the associated ecosystem services. This work presents the local trend analysis of weather variables at this area together with additional snow-related variables. For this, long term point and distributed observations from weather stations and remote sensing sources were studied and used as input and calibration datasets of a physically based snow model to derive long term series of mean and maximum daily fraction of snow covered area, annual number of days with snow, annual number of days with precipitation, mean and maximum mean daily snow water equivalent, and snowmelt and evaporation volumes. The joint analysis of weather and snow variables showed a decrease trend in the persistence and extent of the snow cover area. The precipitation regime, rather than the temperature trend, seems to be the most relevant driver on the snow regime forcing in Mediterranean areas. This poses a constraint for rigorous scenario analysis in these regions, since the precipitation pattern is poorly approximated by climatic models in these regions.

ANÁLISE ATMOSFÉRICA EM MULTICAMADAS EM SITUAÇÕES DE NEVE NO SUL DO BRASIL

A climatology for thickness, temperature, absolute and relative humidities, and vertically-integrated water vapor is presented for a number of distinct atmospheric layers during snow episodes reported in Southern Brazil from January 1979 to March 2011, totaling 64 events. Data from “The Climate Forecast System Reanalysis” (CFSR) were used in the analysis of aforementioned variables for the following atmospheric layers: 850-1000 hPa, 7001000 hPa, 500-1000 hPa, and 700-850 hPa. The snow episodes were subdivided into three sets according to the local surface elevation. The thicknesses of the atmospheric layers found for the snow events in southern Brazil were considerably deeper than the corresponding ones documented in the literature for North America. In addition, analysis of the vertical distribution of water vapor, temperature and relative humidity in the lower troposphere indicated that the atmospheric layer with favorable conditions to snow production is relatively shallow, being below 850hPa. Moreover, the threshold value of thickness for the 850-1000h Palayer during snow occurrence in southern Brazil was around 133 dam, being considerably higher than its counterpart value documented for North America, highlighting the role played by the local topography in setting the conditions that are favorable for snow occurrence in Brazil.

A new perspective on Australian snow

AbstractThe Australian Alps have a unique climatological, ecological and hydrological environment and play a key role in water supply for southeastern Australia. Using resort observations we compile a new and robust snow accumulation data set. Both maximum snow depth and total snow accumulation have declined over the last 25 years. A significant decreasing trend was observed for the total number of light snow days, whereas the total number of heavy snow occurrences has remained constant. Maximum temperatures are highly related to all snow variables. It is suggested that global warming is already impacting light snowfall events, while heavy events are less affected.

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.