Figure 7. (a) Each “true” manually mapped ice cliff on Canwell Glacier is shown as a circle sized proportionally to map-view sur- face area and plotted against the mean ice cliff surface slope and the percentage of “thin” debris cover on the ice cliff face. The color scale shows the true positive rate from the automated ice cliff map- ping method derived for each ice cliff. FP ice cliffs, defined as iso- lated shapes that are solely FP area and do not share a boundary with a “true” ice cliff, are colored grey and abbreviated as FP on the axis label. Two ice cliffs (C1 and C2) are shown to illustrate how “thin” debris cover was mapped and provide context to the data presented in panel (a). Panels (b) and (c) are oblique views of the 29 July 2016 Canwell Glacier orthomosaic with manually gen- erated ice cliff outlines shown in orange. Panels (d) and (e) are the same views with the orthomosaic processed to identify only debris cover on ice cliff faces. C1 is nearly 100 % debris-covered which could draw its classification as an ice cliff into question. C2 is one of the more “clean” ice cliffs within the Canwell Glacier study area but is still covered by a non-trivial amount, > 50 %, of debris. C1 shows linear englacial debris bands that contribute to the ice cliff face debris accumulation. Location shown in Fig. 2.
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ABSTRACT. Mass losses originating from supraglacial ice cliffs at the lower tongues of debris-covered glaciers are a potentially large component of the mass balance, but have rarely been quantified. In this study, we develop a method to estimate ice cliff volume losses based on high-resolution topographic data derived from terrestrial and aerial photogrammetry. We apply our method to six cliffs monitored in May and October 2013 and 2014 using four different topographic datasets collected over the debris- covered Lirung Glacier of the Nepalese Himalayas. During the monsoon, the cliff mean backwasting rate was relatively consistent in 2013 (3.8 ± 0.3 cm w.e. d −1 ) and more heterogeneous among cliffs in 2014 (3.1 ± 0.7 cm w.e. d −1 ), and the geometric variations between cliffs are larger. Their mean back- wasting rate is significantly lower in winter (October 2013 – May 2014), at 1.0 ± 0.3 cm w.e. d −1 . These results are consistent with estimates of cliff ablation from an energy-balance model developed in a previous study. The ice cliffs lose mass at rates six times higher than estimates of glacier-wide melt under debris, which seems to confirm that ice cliffs provide a large contribution to total glacier melt. KEYWORDS: debris-covered glaciers, ice cliffs, terrestrial and aerial photogrammetry, structure for motion, ice volume losses
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Large ice cliffs also exist at the terminus and are responsi- ble for exacerbated retreat compared to where the terminus is gently sloping and debris mantled. The situation of a termi- nal cliff at the glacier mouth combined with terminus retreat is also found at Gangotri glacier for example (Bhambri et al., 2012; Bhattacharya et al., 2016), whereas some glaciers with a stable terminus (e.g. Khumbu, Miage) do not show such terminal cliffs (Bolch et al., 2008a; Diolaiuti et al., 2009). Remarkably, depressions and irregular surface topography near the terminus were already indicated in the Siegfried map from 1879. Certain ice cliffs on Zmuttgletscher have reached > 25 m in height and have persisted for over 2 decades. The consequences of ice cliffs at Zmuttgletscher are (i) a more chaotic pattern of surface elevation changes due strong local ablation, (ii) debris redistribution through cliff back- wasting and fluvial transport, (iii) and stronger surface el- evation changes at the lower part of the tongue (especially downstream of topographic steps). As a result, the elevation change is still stronger towards the terminus than without ice cliffs.
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To estimate debris cover thickness from thermal satel- lite imagery an empirical approach was used. Mihalcea et al. (2008) showed the strong correlation between ASTER- derived LST and debris thickness. To obtain a map of debris thickness distribution we investigated the relation between those two parameters (Fig. 4). Unfortunately, our data did not reveal a significant correlation. To find out how sensitively the model responds to different debris patterns the three dif- ferent regressions ((a) an exponential regression (b) a linear regression through the origin and (c) a power law regression) shown in Fig. 4 were tested. The ASTER image was resam- pled to a pixel size of 10 m × 10 m so that the ablation model is able to resolve small features like ice cliffs.
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observed to form by ice exposure from debris slumping, surface subsidence into englacial voids and calving into supraglacial ponds and lakes (Kirkbride, 1993; Benn and others, 2001; Sakai and others, 2002). On Ngozumpa Glacier >75% of ice cliffs in the lower ablation area bor- dered supraglacial lakes or ponds in 2010. Factors influen- cing the development and persistence of ice cliffs on Lirung Glacier were investigated by Sakai and others (2002). They found that melt rates on non-calving ice cliffs depended predominantly on solar radiation and therefore on the orientation of the ice cliff. South-facing cliffs received more shortwave radiation at the top of the cliff than the bottom due to shading. North-facing cliffs receive less short- wave radiation and their energy balance is dominated by long wave receipts, which are greater towards the base. As a result, north-facing ice cliffs tend to be larger, steeper, gen- erally debris free and therefore longer lived than south-facing cliffs. Although initial backwasting could be rapid on south- facing cliffs, ice faces become less steep and, once an angle
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temporal coverage of remote-sensing data. However, none of these studies have successfully reproduced field measure- ments without using empirically determined relationships or calibration factors (Mihalcea et al., 2008a; Foster et al., 2012). These methods are therefore best suited for debris- covered glaciers for which the necessary measurements to compute the relationships or factors are available, and their applicability for regional-scale studies such as this one is un- certain. Thus, important future steps for glacier CMB studies in the Karakoram include increasing the accuracy and spatial detail of the debris thickness field and its physical properties; improving our understanding of moisture fluxes between the debris and the atmosphere and accounting for subgrid-scale surface heterogeneity (e.g., by introducing a treatment of ice cliffs; Reid and Brock, 2014). Nonetheless, by providing an estimate of the controlling influence of debris, these simula- tions contribute to a greater understanding of glacier behav- ior in the Karakoram.
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Changing the precipitation forcing, from that of Ohmura and Reeh (as in EISMINT-3) to ERA-40, results in an increase in equilibrium ice-sheet surface extent of 2.1%. However, there is almost no effect on the ice-sheet volume. This can be explained by the fact that all precipitation that falls is as- sumed to fall as snow in the annual PDD scheme. Since the temperature forcing has no effect on the amount of snow, it is the quantity and distribution of precipitation that re- sults in the difference in ice surface extent. Figure 3 shows that the annual precipitation is up to two times greater on the eastern and western margins of Greenland for ERA-40 compared with Ohmura and Reeh (1991). The accumula- tion rate is greatest in south-east Greenland for both pre- cipitation datasets but with the high values extending fur- ther north along the eastern margin for ERA-40. The ex- tra precipitation falling over the western and eastern margins coupled with a positive temperature-elevation feedback re- sults in growth and extension of the ice-sheet into previously ice-free regions. However, the precipitation falling over cen- tral and north Greenland is three times less for ERA-40, re- sulting in less accumulation in the interior and lower maxi- mum altitude of the ice sheet. These opposing effects result in similar ice-sheet volumes. However, Hanna et al. (2006) show that ERA-40 is ∼ 50% too “dry” in the central northern parts of Greenland, as validated using ice-core data. Fur- thermore, it seems increasingly likely that both the Ohmura and Reeh (1991) and ERA-40 precipitation datasets underes- timate precipitation and accumulation in south-east Green- land, where recent regional climate model results suggest much higher than previously observed precipitation rates (Et- tema et al., 2009; Burgess et al., 2010).
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To quantify the difference in ice edge position found be- tween the AMSR-E and NIC ice chart, perpendicular lines were drawn at 50 km intervals along the NIC ice edge to the AMSR-E ice edge defined by the ice concentration. Nearly perpendicular lines were drawn along the ice edge to measure the distance between NIC and AMSR-E. Since the ice edge for both the NIC ice edge and the AMSR-E edge are “ran- dom (and uncorrelated) wavy lines”, it is difficult to choose between representations that measures the distance in a con- sistent way between the two ice edges. We therefore chose to use parallel lines in a constant horizontal orientation on the image between the two rather than say, a due south ori- entation at all locations. Both methods are equally arbitrary, since it is easy to find locations where they would give ei- ther greater or less distance than the other method, suggest- ing these differences would average out to a similar value when taken over many measurements and would give sim- ilar max and min values also. By using a large number of parallel lines however of equal small separation, a total area between the two can be easily calculated by summing the trapezoidal areas formed by the two parallel lines and the separation distance (constant at 50 km) along the respective ice edges of those parallel lines. This method is also easier to implement on the GIS platform used for the analysis than a constant direction vector. Table 2 shows the Max and Min distances, Mode (most frequently observed distance), Mean, and Standard Deviation for the entire region (Fig. 5). Ta- bles 3 and 4 show no particular differences when the data are broken into two sectors (Bellingshausen-Amundsen Sea and Ross Sea) for this analysis. While there is a range of sev- eral hundred kms and some cases (negative Min distances) where the AMSR-E estimated edge was north of the NIC ice edge because of the measurement geometry, the mean
Abstract. In this study the dynamics and sensitivity of Hans Tausen Iskappe (western Peary Land, Greenland) to climatic forcing is investigated with a coupled ice flow–mass bal- ance model. The surface mass balance (SMB) is calculated from a precipitation field obtained from the Regional Atmo- spheric Climate Model (RACMO2.3), while runoff is calcu- lated from a positive-degree-day runoff–retention model. For the ice flow a 3-D higher-order thermomechanical model is used, which is run at a 250 m resolution. A higher-order solu- tion is needed to accurately represent the ice flow in the outlet glaciers. Under 1961–1990 climatic conditions a steady-state ice cap is obtained that is overall similar in geometry to the present-day ice cap. Ice thickness, temperature and flow ve- locity in the interior agree well with observations. For the outlet glaciers a reasonable agreement with temperature and ice thickness measurements can be obtained with an addi- tional heat source related to infiltrating meltwater. The simu- lations indicate that the SMB–elevation feedback has a major effect on the ice cap response time and stability. This causes the southern part of the ice cap to be extremely sensitive to a change in climatic conditions and leads to thresholds in the ice cap evolution. Under constant 2005–2014 climatic con- ditions the entire southern part of the ice cap cannot be sus- tained, and the ice cap loses about 80 % of its present-day volume. The projected loss of surrounding permanent sea ice and resultant precipitation increase may attenuate the future mass loss but will be insufficient to preserve the present-day ice cap for most scenarios. In a warmer and wetter climate the ice margin will retreat, while the interior is projected to thicken, leading to a steeper ice cap, in line with the present- day observed trends. For intermediate- ( + 4 ◦ C) and high-
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Fig. 8. The drainage of a supraglacial pond interaction at Cliff E (a). The drained supraglacial pond provided an opportunity to reconstruct the historic bathymetry (b and c). The data gap at the deepest part of the pond (intersecting with Profile 1) was caused by the remnant presence of water, which had not drained, estimated to be <1 m in depth. Point cloud profiles revealed subaerial ice cliff retreat and thermos-erosional
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coastal zone of the Southern coast of Crimea is related to the penetration of the Azov Sea waters along the shelf during spring. Particularly, it is the main reason of an annual minimum of salinity in this area in the given period. Additional opportu- nities for studying the water dynamics near the Kerch Strait are given by a known property of increased turbidity of the Azov Sea waters in comparison with the Black Sea ones. It should be noted that the Kerch Strait itself is also a powerful source of suspended matter. Its coasts on a considerable extent are composed of abrasion-landslide clay cliffs . According to some estimates, abrasion of the cliffs and benches gives solid material in the amount of 340,000 m 3 per year, or
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In this study, three different examples of failure in subhor- izontally bedded limestone cliffs are discussed. The fail- ures are characterized by pre-existed vertical joints passing through thick limestone. The Yudong rockslide originated in a limestone cliff edge and left a moderately inclined rupture plane, implying shear failure through the hard rock. Rock collapse caused by compression fracture and tensile splitting of the rock mass near the interface between the hard cap and weak stratum occurred at Zengzi Cliff. The Wangxia Cliff failure showed a slow rock slump sheared through the un- derlying incompetent rock mass along a curved surface. The mechanism of toe breaking mainly depends on the strength characteristics of the rock mass.
The September minimum ice extent is of particular interest to researchers because of its close relationship to ice volume (which is not currently directly measurable), and because its value defines the difference between a perennial ice cover and a seasonal ice cover. Should the Arctic become nearly, or completely, ice-free in September, there would be serious implications for wildlife both in sea and on land, and for na- tive Arctic peoples. A seasonal ice cover would also open the Arctic to shipping for one or more months of the year, and exacerbate current international tensions over Arctic waters. The speed of melting of ice during the summer, and hence June and July ice extent, is also closely related to the Septem- ber minimum extent. Lower ice extent in these months results in more solar energy being absorbed by the mixed ocean layer. This will tend to be released to the atmosphere dur- ing autumn and early winter, with consequences for the re- gional climate. A number of recent studies (Strey et al., 2010; Overland and Wang, 2010; Francis et al., 2009) have sug- gested that there will also be consequences for weather in mid-latitudes during these seasons.
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DOI: 10.4236/fns.2018.98071 971 Food and Nutrition Sciences drates content but higher in vitamins, provitamin A, antioxidants, and minerals as well as low in cost . Vegetables are not widely used as ice cream as flavor- ing and coloring agents. Interestingly, the use of some non-acid vegetable such as carrot and pumpkin for ice cream production may eliminate adding commer- cial flavoring and coloring agents and may prevent technical challenges that are associated with the nature of using acidic fruit juice. Various interactions are possible when acidic juices are mixed with milk protein such as protein aggrega- tion, peptide precipitation and polyphenols and proteins interactions which lead to form of polyphenols-protein complexes  . Carrot and pumpkin could be converted to value-added products if processed properly when incorporated into ice cream dessert to improve its physical, nutritional and organoleptic properties.
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and englacial), massive segregated-intrusive ice and buried snowbanks. The appearance and structure of the buried mas- sive ice body are similar to those of englacial ice typically observed at the margin of glaciers, ice caps or ice sheets. The buried massive ice body has a whitish appearance ow- ing to its high concentration of air bubbles. Coarse-bubbly ice is the most abundant type (90 %–95 %) of englacial ice found in glaciers (Allen et al., 1960). Our results also show that the cross-sectional area of the crystals of the buried mas- sive ice is smaller than that of neighbouring C-93 glacier ice, but there is no significant difference in their circularity ra- tio (Mann–Whitney–Wilcoxon test, p = 0.89). However, the difference in ice crystal size is not unforeseen since the ones from glacier ice can show variations of the order of a few millimetres to several centimetres in diameter (Gow, 1963; Thorsteinsson et al., 1997). Patterns of preferred crystal ori- entation combined with the occurrence of deformation fea- tures in the form of debris bands suggest that the ice has been subjected to long-continued shear stress caused by the mo- tion of a glacier (Benn and Evans, 2010; Lawson, 1979). The debris bands cross-cutting the buried glacier ice are compa- rable to those observed in the terminus zone of Stagnation Glacier on Bylot Island, where basal sediments were trans- ported to the glacier surface through shear planes (Moorman and Michel, 2000).
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Studies have been done to estimate ice thickness from the IceSat LA data. They showed that the ice thickness has sig- nificantly decreased from 2007 to 2008, which is in agree- ment with analysis of RA data from Envisat (Kwok et al., 2009). Ideally, LA and RA data should be collected si- multaneously in order to obtain direct estimates of the snow depth, as demonstrated in airborne campaigns (Leuschen and Raney, 2005; Connor et al., 2009). Simultaneous LA and RA satellite sensors are not planned during the CryoSat-2 mis- sion, thus snow data on Arctic sea ice have to be obtained from climatic estimates and new field observations. Another possibility is to construct daily fields of snow depth using available climatology and snowfall from ECMWF meteoro- logical products for partitioning the total freeboard into its snow and ice components, as described by Kwok and Cun- ningham (2008).
To assess how the calculated brine zones compare to obser- vations of true brine extent, we map out the locations of pre- vious firn cores and radar surveys which can help to vali- date the presence of liquid brine (Fig. 2). The map shows all recorded firn cores on floating ice which have pene- trated below sea level, most of which contained no brine. The exceptions are a number of ice cores on McMurdo Ice Shelf (Heine, 1968; Kovacs et al., 1982; Risk and Hochstein, 1967), one on Lazarev Ice Shelf (Dubrovin, 1960) and one on Brunt Ice Shelf (Thomas, 1975). All of the boreholes in which brine has been observed lie within our predicted brine zones. The remaining ice cores, which penetrated below sea level but contained no brine, are listed in Table S4 in the Sup- plement. We use these to provide a false positive rate for each of the predicted brine zones, referring to the proportion of these ice cores which our results predict should have con- tained brine. The 750 kg m −3 zone has a false positive rate of 8 %, the 800 kg m −3 zone has a false positive rate of 42 %, and the 830 kg m −3 zone has a false positive rate of 67 %.
By calibrating our ice-sheet model on the Ekström Ice Shelf catchment, we aim to introduce commonly employed initial- isation techniques in large-scale ice-sheet modelling to ice- rise modelling. The advantage of the calibration is that but- tressing is simulated in a realistic fashion. Without the cal- ibration, large thinning/thickening rates would result in un- realistic model results. However, the calibration matches ob- served horizontal velocities with modelled horizontal veloc- ities without any constraints on vertical ice velocities. This leads to the situation that any errors in the horizontal veloci- ties propagate into the vertical velocity through mass conser- vation. As horizontal velocities in the divide region are close to zero, small errors in horizontal velocities have a large ef- fect on vertical velocities, and therefore we were unable to solve for the age field (Raymond stacks). In addition, due to computational constraints, only 10 equally spaced vertical layers could be employed. For an ice thickness of ∼ 900 m at the Halvfarryggen Ice Rise, this corresponds to a vertical res- olution of ∼ 90 m. While this vertical resolution is sufficient for our ice-rise divide migration purposes, a much higher vertical resolution ( ∼ 30–40 layers) would be necessary to model Raymond arches at the required detail (Drews et al.,
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in sea ice volume because detailed thickness observations have been lacking. Here, we assess changes in northern hemisphere sea ice thickness and volume using five years of CryoSat-2 measurements. Between autumn 2010 and 2012, there was a 14% reduction in Arctic sea ice volume, in keeping with the long-term decline in extent. However, we observe 33% and 25% more ice in autumn 2013 and 2014, respectively, relative to the 2010-2012 seasonal mean, offsetting earlier losses. The increase was driven by the retention of thick sea ice northwest of Greenland during 2013 which, in turn, was associated with a 5% drop in the number of days on which melting occurred conditions more typical of the late s In contrast, springtime Arctic sea ice volume has remained stable. The sharp increase in sea ice volume after just one cool summer indicates that Arctic sea ice may be more resilient than has been previously
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crepancies of 4–9 cm for ice cover; MLI (Rogers et al., 1995) on Harmon Lake, British Columbia, which had up to 6 cm er- ror for ice cover and 4 cm error for snow cover; and CLIMo (Duguay et al., 2003) on lakes in Barrow, Alaska (differences of 5–6 cm for ice thickness); Poker Flat, Alaska (mean abso- lute error of 2 cm for ice cover and underestimation of snow– ice thickness of 7 cm); and Churchill, Manitoba (ice thick- ness observations were within model values for the snow-free and 100 % snow-covered scenarios). Duguay et al. (2003) found that variability in snow density and snow accumula- tion play a significant role in ice thickness, which may ac- count for discrepancies between simulated and observed ice cover thicknesses in our study. In general, DYRESM-WQ-I predicts the ice and snow depths fairly accurately. Figure 3a shows the evolution of simulated ice and snow thickness with multiple ice and snow thickness measurements taken during the winter of 2009–2010. Once ice develops, water beneath the ice continues to freeze, adding to the ice thickness, as heat is conducted from water to the air. In this winter period, lake ice grew fast initially and then slowed substantially. Eventu- ally, the ice reaches its maximum thickness and stops grow- ing (Ashton, 1986). Once air temperatures begin to warm up, ice quickly thins until the ice-off date. Overall, the model is able to capture the growing ice cover as well as the tempo- rally variable snow cover found on the lake.
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