Glacier Changes And Regional Climate: A Mass And Energy .
5384JOURNAL OF CLIMATEVOLUME 21Glacier Changes and Regional Climate: A Mass and Energy Balance Approach*SUMMER RUPPERDepartment of Geological Sciences, Brigham Young University, Provo, UtahGERARD ROEDepartment of Earth and Space Sciences, and Quaternary Research Center, University of Washington, Seattle, Washington(Manuscript received 6 September 2007, in final form 24 January 2008)ABSTRACTThe mass balance of a glacier is a complex consequence of the combination of atmospheric variables thatcontrol it. However, the understanding of past, present, and future glacier states is often predicated on verysimplified representations of the mass balance–climate relationship. Here, a full surface energy and massbalance (SEMB) model is developed to explore the relationship between glacier equilibrium-line altitudes(ELAs) and climate at a regional scale. This model is applied to central Asia because of the diverse climateregimes and glacier history. The model captures the pattern in ELAs well; the seasonal cycle in energybalance terms are comparable to studies on individual glaciers in central Asia, and the proportionality factorrelating melt to temperature is within the range of those reported for individual glaciers within the area. Inregions where precipitation is low, ablation at the ELA is dominated by sublimation. Conversely, whereprecipitation is high, ablation at the ELA is dominated by melt and surface runoff. In turn, the sensitivityof the ELA to changes in climate is strongly tied to the dominant ablation process. In particular, ELAs inmelt-dominated regions are most sensitive to interannual variability in air temperature, while ELAs insublimation-dominated regions are most sensitive to interannual variability in precipitation. Glaciers insublimation-dominated regions are acutely sensitive to even small changes in atmospheric variables. Finally,changes in clouds are shown to be important in all regions through their influence on the shortwave andlongwave radiative fluxes, which dominate the surface energy balance at the ELA.1. IntroductionIn many parts of the world, reconstructed histories ofglacier extents form the primary record of past climatechanges (e.g., Porter 1977; Porter and Orombelli 1985;Gillespie and Molnar 1995; Lowell et al. 1995; Kaufmanet al. 2004). Many researchers argue these histories reflect global-scale climate changes, such as the Little IceAge and the Younger Dryas (e.g., Grove and Switsur1994; Denton and Hendy 1994; Lowell et al. 1995; Bradley 2000). The conclusions and interpretations aboutsuch global connections in the climate system rest on* Supplemental material related to this paper is available at theJournals Online Web site: nding author address: Summer Rupper, Departmentof Geological Sciences, Brigham Young University, S389 ESC,Provo, UT 84602.E-mail: summer rupper@byu.eduDOI: 10.1175/2008JCLI2219.1 2008 American Meteorological Societyassumptions of how sensitively glaciers respond to characteristic climate variations.Despite their obvious importance, there is still muchambiguity about how glaciers respond to climate. Atface value, glaciers appear to be among the moststraightforward natural recorders of climate: a glaciersimply reflects the difference between accumulation ofsnow and ablation (i.e., mass loss). However, numerousenvironmental factors control accumulation and ablation: avalanches or wind-blown snow, rain versus snow,surface albedo variations, hillside shading, and cloudiness are just some of the examples (e.g., Paterson 1999;Kayastha et al. 1999). Moreover, if climate changes arereconstructed based on the change in a glacier’s terminus position, glacier dynamics are also part of the signal.Where glaciers within an entire region are behavingin the same way (i.e., advancing or retreating synchronously), there can be confidence that local factors specific to any single glacier are not dominating the response and that regional-scale climate variations are at
15 OCTOBER 2008RUPPER AND ROEwork. In this study, we pursue this regional-scale approach.Several methods have been employed to explore thelink between glacier mass balance and climate, and at avariety of scales ranging from single glaciers and basinsto whole complexes of glaciers across large regions(e.g., Braithwaite 1995; Wagnon et al. 2003; Kessler etal. 2006). The larger the spatial scale of interest, thesimpler the method for estimating ablation tends to be.For example, at scales larger than a single basin, ablation is typically assumed to be proportional to somemeasure of summertime temperature (e.g., Fountain etal. 1999; Braithwaite et al. 2003; Oerlemans 2005). Suchablation parameterizations are appealing in their simplicity and the results from using them can be easilyinterpreted. However, this simplified approach to estimating ablation neglects sublimation as well as otherpotentially important variables such as radiation, windspeed, relative humidity, and cloudiness. The Oerlemans (2005) reconstructions of temperature from glacier length changes assume that, in the aggregate, the169 mid- and low-latitude glaciers used in the study areresponding only to changes in ablation, which is assumed to directly reflect temperature; all other atmospheric variables are neglected. This type of approachrequires use of a melt factor that relates the measure ofsummer temperature to the ablation rate. This meltfactor is an empirically determined parameter. Yetstudies of different glaciers suggest melt factors varyfrom place to place by about a factor of up to 5 (e.g.,Paterson 1999; Kayastha et al. 2003; Zhang et al. 2006).A more physically based approach to modeling ablation is to perform a self-consistent calculation of theenergy balance at the glacier surface. This has the advantage that all relevant atmospheric variables are included and that both sublimation and surface meltingare a product of the calculation. Mass balance modelsthat calculate ablation using the energy balance approach have been applied successfully to single glaciersand basins (e.g., Kayastha et al. 1999; Plummer andPhillips 2003; Molg and Hardy 2004). For example,Kayastha et al. (1999) show that a glacier in the Nepalese Himalaya is sensitive to changes in both temperature and precipitation. Energy balance studies alsosuggest that tropical glaciers are sensitive to subtlechanges in moisture-related variables, such as cloudiness, relative humidity, and precipitation (e.g., Hastenrath 1994; Kaser et al. 2004; Molg and Hardy 2004).These examples highlight the need for a better understanding of how glacier–climate interactions vary fromregion to region. With this in mind, there are two questions that need to be addressed: First, can we understand the relative importance of accumulation and ab-5385lation in controlling the mass balance of glaciers at regional scales? Second, how do the observed patterns ofglacier response reflect regional changes in climate?This paper presents a surface energy and mass balance model that provides a framework within which toaddress these questions. We focus on central Asia.There are two reasons for doing so: first, glaciers arefound throughout Asia in a variety of climate settings,and this allows for diverse tests of the model’s abilities;second, the glacier history of central Asia is strikinglydifferent than that of the high-latitude ice sheets (e.g.,Gillespie and Molnar 1995), and we do not yet understand the mechanisms controlling these glacier changes(Rupper et al. 2007, manuscript submitted to Quat.Res., hereafter RU). This study aims to address thefirst-order climate controls on regional-scale glaciermass balance. The surface energy and mass balancemodel is able to capture the regional differences in glacier mass balance and is in agreement with observationsand model studies of individual glaciers. An analysis ofthe energy fluxes reveals the causes of these regionaldifferences, and sensitivity tests explore where thecause of glacier changes can be confidently understoodand other regions where greater uncertainty exists.2. Glaciers and climateThe equilibrium-line altitude (ELA) is defined as thealtitude on a glacier where annual accumulation equalsannual ablation. It is considered to be one of the mostuseful glaciological measures for reconstructing climate(e.g., Porter 1975; Fountain et al. 1999; Paterson 1999).All else being equal, if the ELA lowers, the glacieradvances: whereas, if it rises, the glacier retreats. Whileother glacier characteristics such as length depend onmyriad factors such as ice dynamics and bed geometry,the ELA is directly related to climate and provides acommon measure whereby changes can be compareddirectly from one region to another.a. Central Asian glaciersThe glacier history across central Asia has been characterized in terms of the reconstructions of paleoELAs. Gillespie et al. (2003) identify three regions thatcapture the spatial and temporal variability of the glaciers during the last glacial cycle. These three zoneshave distinctly different glacier histories, and each isdifferent again from that of North America, Europe,Greenland, and Antarctica (Gillespie et al. 2003; RU).The three regions are shown in Fig. 1. The western zoneextends from the Kyrgyz Tien Shan down to the Karakoram and into central Tibet. The northern zone in-
5386JOURNAL OF CLIMATEFIG. 1. Shaded areas of gray represent the general outline of theeastern, western, and northern zones, as defined by the glacierhistory. These are the regions over which statistics are calculatedfor each zone (Table 4). Contours are the NCEP–NCAR reanalysis elevations (Kalnay et al. 1996); 500-m contour interval; zerocontour not shown. Coastlines are in gray.cludes the regions of the Tien Shan in Xinjiang province, China, and the mountainous areas of central Mongolia. Finally, the eastern zone includes the portion ofthe Himalaya Mountains around Nepal and eastern Tibet. We demonstrate in the sections that follow that thesize of each of these zones corresponds approximatelyto the spatial scale expected of patterns in regional climate in the modern climate. For example, there is astriking degree of temporal variability in precipitationand temperature between the three zones (Fig. 2, discussed below). It is perhaps not surprising that the glacier history also varies on these spatial scales (Gillespieand Molnar 1995; RU).b. Central Asian climate variabilityGlaciers are found on high topography throughouteach of the three zones of central Asia and in verydiverse climates: glaciers in the Himalayas are fed bythe intense summer monsoon precipitation and a wintertime storm track; glaciers nestled along the easternside of the Karakoram face the extreme dryness of thedesert; and glaciers clinging to the peaks of the Mongolian Altai are exposed to seasonal cycles in temperature as large as 40 C. Modern interannual climate variability can be used to explore how the sensitivity ofthese glaciers to climate and climate change differsacross these diverse climates.We focus first on characterizing regional variabilityin the modern climate, using the National Centers forEnvironmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis datasetVOLUME 21(Kalnay et al. 1996; Kistler et al. 2001). NCEP–NCARreanalysis uses an analysis/forecast system to performdata assimilation using past data from 1948 to thepresent. We use the daily output available on a 2.5 gridin our analyses. We consider two atmospheric quantities relevant for glaciers: total precipitation and positivedegree days (PDDs—explained in the next paragraph).Topography is strongly smoothed by the coarse resolution of the reanalysis (2.5 2.5 ), and thus rain at thesurface elevation of a reanalysis grid point would likelynot be rain at the ELA of a glacier within that gridpoint. We assume that all precipitation falling at theELA is snow. A fully consistent accounting of the difference between rain and snow at the ELA would alsoinvolve calculating the refreeze of rain within the snowpack and the resulting heat input. We neglect theseeffects given our focus on regional scales and first-orderquestions. The implications for the conclusions are addressed in sensitivity studies in section 5 and in thediscussion in section 6.PDDs are the sum of daily mean air temperatures(Ta) that are above zero:PDD 兺H共T 兲,a共1兲where H is 0 for Ta ⱕ 0 and Ta for Ta 0; the sum istaken over the calendar year. Thus PDD has units of( C days). In simple treatments, total ablation is assumed to be proportional to PDDs; the constant ofproportionality is known as the melt factor and is calibrated to observed melt at glaciers (e.g., Hoinkes andSteinacker 1975; Braithwaite 1995). The method issometimes modified to include different melt factorsfor snow and ice and to allow for percolation and refreeze of meltwater within the snowpack (Casal et al.2004; Zhang et al. 2006).Figure 2 shows total annual precipitation anomalies(mm yr 1) and PDD anomalies for each year from 1948to 2006 for each of the three zones using the NCEP–NCAR reanalysis dataset (Kalnay et al. 1996). This figure highlights the differences in temporal variability between the three regions of interest. Lumping all threeregions together (Fig. 2a) there is no apparent pattern.Separately, however, it is clear that each zone has different climate variability (Figs. 2b–d). The eastern zoneis characteristic of monsoonal climates, having highprecipitation variability and low PDD variability. Thenorthern zone is the most continental climate, with lowprecipitation rates and hence low precipitation variability, contrasting with high PDD variability. In the western zone both PDD and precipitation variability is high.Thus Fig. 2 emphasizes that there are important regional differences in the climate variables that affectglaciers.
15 OCTOBER 20085387RUPPER AND ROEFIG. 2. Scatterplots of annual average precipitation anomalies (mm yr 1) vs positive degree day anomalies ( Cday yr 1) for each grid point in the NCEP reanalysis dataset for the years 1948 to 2006. (a) central Asia, (b) thewestern zone, (c) the northern zone, and (d) the eastern zone. The different shape to the scatter in each regiondemonstrates the differences in climate variability. Means and standard deviations averaged over each of the threezones are provided in Table 4.We next evaluate how this regional climate variability within central Asia translates into the glacier response. The following section describes the energy balance modeling framework used to achieve this.3. Surface energy balance and mass balance modelWe emphasize that the main goal is to understandhow the ELA changes in response to climate changes. Itmatters less, therefore, that the modeled absolute ELAsimulates the observed ELA. If the model correctlyreflects the most important terms in the energy balanceat the ELA, then the model can be used to evaluate therelative sensitivity of the ELA to changes in atmospheric variables. A particular strength of this approachis that, while the modeled absolute ELA is very sensitive to model parameters (i.e., albedo, surface roughness, etc.), the change in ELA for a change in climate isrelatively insensitive to the magnitude of the parameters (supplemental appendix B, available online athttp://dx.doi.org/10.1175/2008JCLI2219.s1).This SEMB model has two nested balance algorithms, a surface energy balance and a mass balance. Bydefinition, the ELA is the elevation at which these twobalances both apply. For specified climate inputs, themodel solves for the elevation at which a snow-coveredsurface would be in energy and mass balance. In otherwords, we ask the question “[I]f there were a glacierwithin a given reanalysis grid point, what would theelevation of its ELA be?”a. Surface energy balance modelThe surface energy balance model follows closely theapproach taken by Kayastha et al. (1999), Wagnon etal. (2003), and Molg and Hardy (2004) but is modifiedsuch that it is suitable for application to larger regions.The basic surface energy balance equation used isQm S L Qs Ql Qg ,共2兲where Qm is the energy available for melting the snow/ice surface, S is the net shortwave radiation flux ab-
5388JOURNAL OF CLIMATETABLE 1. Model parameters and constants, their values whereappropriate, and units for all equations described in section 3 andsupplemental appendix A.VOLUME 21TABLE 2. List of the model variables and units used in theequations described in section 3 and supplemental appendix A.Variables C1cp opoL LsLmtkzzomzohzovgABHZParameters and constantsValueUnitsAlbedo of the glacier surfaceStefan–Boltzmann constantEmissivity of the glaciersurfaceLongwave emissivity constantSpecific heat of air atconstant pressureDensity of air at standard sealevelPressure of air at standardsea levelLatent heat of vaporizationLatent heat of sublimationLatent heat of fusionTimevon Kármán constantMeasurement level above thesurface of wind, relativehumidity, and temperatureScalar roughness length ofmomentumScalar roughness length oftemperatureScalar roughness length ofrelative humidityGravitational constantConstantConstantScale heightElevation0.65.67 10 81W m 2 K 40.5 to 0.91010hPa 1J kg 1 K 11.2910132.5142.8480.334kg m 3hPaMJ kgMJ kg 1MJ kg 1monthsm0.5mm0.5mm0.5mm9.817.67243.58pesTTampDn *RiRHZps 10.42TaTseaDsMonthly mean air temperature at the ELAMonthly mean surface temperature at the ELAEvaporation vapor pressure of the airTurbulent-transfer coefficient for stableconditionsPressure of air at the ELASaturation vapor pressure at the surfaceMean annual air temperatureAmplitude in the seasonal cycle of airtemperatureTransfer coefficient for neutral stabilityWind speedFriction velocityBulk richardson numberRelative humidityHeight of the ELAPressure of air at the surfaceUnits C ChPahPahPa C Cm s 1m s 1%mhPaAlbedo is set equal to 0.6, intermediate between that ofpure ice and fresh snow (e.g., Paterson 1999). In realityalbedo varies both in space and time, and it is unrealistic to know its value for all glacier surfaces across thelarge region of interest. However, the effect of albedovariations is evaluated in sections 5 and 6. Since albedois held fixed, variability in absorbed shortwave radiation in the model is governed only by variability incloudiness.m s 2 C Ckmkmc. Longwave radiationsorbed at the surface, L is the net longwave radiationflux, Qs is the sensible heat flux, Ql is the latent heatflux, and Qg is the heat conduction at the glacier surface. Since Qg is a small term in the seasonal energybalance (e.g., Calanca and Heuberger 1990; Kayastha etal. 1999; Ohmura 2001), it is neglected from here on.Since we assume that all precipitation falls as snow atthe ELA, the heat flux supplied by precipitation is zero.All downward fluxes are positive. Tables 1 and 2 list allvariables and parameters used in the equations, in theorder they appear in the text. For clarity, only themain equations are discussed in the body of the text.Supplemental appendix A (available online at http://dx.doi.org/10.1175/2008JCLI2219.s1) gives a more thorough discussion of equations and parameters used.b. Shortwave radiationThe shortwave radiation absorbed by the glacier surface, S, is dependent upon the incident shortwave (S )and the albedo ( ):S S 共1 兲.共3兲Net longwave radiation is equal to the sum of theoutgoing and incoming longwave radiation,L L L .共4兲Here L is calculated using the Stefan–Boltzmann lawL sT 4s共5兲and is therefore a function only of the surface temperature (Ts). Emissivity ( s) of the snow/ice surface is assumed to be equal to one and is the Stefan–Boltzmanconstant: L is controlled largely by the air temperature above the surface but is also dependent on radiative properties of the atmosphere itself, especially atmospheric relative humidity and clouds.There are many empirical formulas employed to calculate incoming longwave radiation. We follow theequation suggested by Duguay (1993)L T 4a共C1 C2ea兲,共6兲where Ta and ea are the near-surface (2 m) air temperature and vapor pressures, respectively. To test the sen-
15 OCTOBER 2008sitivity of the results to assumptions in the longwavecalculation, the constants C1 and C2 were determined intwo different ways: First, they were assumed to beequal to the average reported for individual glaciers,0.55 and 0.017, respectively (e.g., Duguay 1993; Molgand Hardy 2004). The contribution from C2ea was typically less than 5%, and so is neglected. This suggeststhat relative humidity is not as important as other radiative properties, such as clouds. For the secondmethod, used for the standard case presented here, themonthly mean C1 is determined using the NCEP–NCAR incoming radiation at the surface, which yieldssimilar values to Duguay (1993). This calibrated C1 istherefore the emissivity of the lower troposphere in thelongwave band and is therefore dependent on temperature, cloudiness, etc. Both methods for calculating C1and C2 yielded similar results.d. Sensible and latent heat fluxesA commonly employed method for calculating turbulent heat fluxes is the so-called bulk method based onthe Monin–Obukhov similarity theory. For sensibleheat,Qs Dscp o5389RUPPER AND ROEp共T Ts兲, wherepo a共7兲Ts and Ta are the surface temperature and near-surfaceair temperature; cp is the specific heat at constant pressure; o is the density of air at sea level; Ds is the turbulent transfer coefficient, dependent upon wind speed,roughness lengths, and a correction for buoyant versusmechanical mixing; and p/po is the atmospheric pressure at the ELA divided by atmospheric pressure atstandard sea level. This reflects that the atmosphere’sability to carry heat is dependent on its density.For latent heat,Ql Ds o1L 共e es兲.po ,s a共8兲If the surface temperature is zero or above, then thelatent heat of vaporization (L ) is used, while if it isbelow zero, than for sublimation (Ls) is used: es is thevapor pressure at the surface, which is assumed to besaturated and so es depends only on the Ts; ea is thevapor pressure of the above surface air and depends onboth Ta and relative humidity. Therefore the latent heatflux is a function of Ta, Ts, and relative humidity.e. Mass balance and seeking the ELATo facilitate discussion of the algorithms used tosolve for the ELA, one more constraint from the reanalysis data needs to be addressed. The ELA is anannual mean property. To close the equations and obtain a unique value for the ELA, the relationship between the temperatures at each time step in the annualcycle must be prescribed:冉 冊Ta T 共z兲 Tamp cos 2 t,12共9兲and T is the mean annual 2-m air temperature at theELA, with T being a function of the height z of theELA. The amplitude of the annual cycle in air temperature, Tamp, is prescribed using the reanalysis output. Weuse monthly time steps in what is presented here. Several calculations were made using daily time steps, butthis did not substantially change the results.The surface energy balance and mass balance modelsare combined to solve for the ELA. The energy balanceportion of the model calculates the energy available formelt (Qm) every month from (2), following the methodof Kayastha et al. (1999). Using an iterative solver, Tsconverges toward a value such that all fluxes are balanced (i.e., Qm is equal to zero). If the resulting Ts isgreater than zero, then Ts is reset to zero and Qm isrecalculated. This recalculated Qm represents the energy available for melt. Melt is then equal to L 1m Qm formonths where Ts equals 0 C. Additionally, evaporationoccurs when Ts equals 0 C and ea es and is calculatedas L 1 Ql. When Ts is less than 0 C and ea es, sublimation occurs at a rate of L 1s Ql. The total ablation permonth is then the sum of the monthly sublimation,evaporation, and melt.In the mass balance iterative algorithm, the modelseeks the elevation at which the T results in a totalannual ablation (calculated using the surface energybalance method above) that exactly equals the totalannual accumulation. In the mass balance portion ofthe model, the T is sought for which ablation equalsaccumulation. Once this T is found, the climatologicallapse rate is applied to determine the elevation of theELA relative to the surface of the reanalysis grid point.The final model output includes monthly 2-m air temperature, surface temperature, all energy balanceterms, sublimation, melt, and evaporation at the equilibrium line for a given set of climate variables andmodel parameters.4. Surface energy balance and mass balance modelresultsWe briefly recap the procedure. The basic goal is tocharacterize how regional patterns of climate variabilitytranslate into regional patterns of ELA sensitivity. Taking NCEP–NCAR reanalysis output, we ask the question “[I]f there were a glacier within a given reanalysisgrid point, what would the elevation of its ELA be?” In
5390JOURNAL OF CLIMATEVOLUME 21b. Sublimation or melt?FIG. 3. Colors are the elevation (m) of the equilibrium linecalculated using the SEMB model; 500-m contour interval; zerocontour not shown. Coastlines are in gray.other words, using the SEMB model we solve for theelevation at which a snow-covered surface would be inmass and surface energy balance.a. Equilibrium-line altitudesThe pattern in the ELAs calculated from the NCEP–NCAR climatology using the SEMB model is shown inFig. 3. Despite the coarse resolution of the reanalysisoutput and the uncertainty in model parameters, thegeneral pattern in the modeled ELAs is reasonable.Figure 4a shows a high-resolution digital elevationmodel (DEM) with colors marking peaks that wouldintersect the modeled ELAs. Glaciers are likely to existwhere the ELA intersects topography. Figure 4b showsthe same DEM and ELA intersection, but with pinkdots marking actual glaciers (Raup et al. 2007). Thebiggest discrepancy is that the model predicts more extensive glaciers than observed in the interior of the plateau. This is perhaps due to the coarse resolution of theNCEP–NCAR climatology, which allows for greaterpenetration of the monsoonal rains over the TibetanPlateau. There are also too few glaciers in the northernzone. This is also likely due to the coarse resolution anduncertainty in the magnitude of precipitation in the region. We emphasize that our primary interest is in howpatterns in climate change lead to patterns of ELAchange, so we are less interested in simulating the climatological mean ELA. However, it is at least reassuring that the SEMB model reproduces broadly whereglaciers are found in the current climate.One of the advantages of applying a self-consistentsurface energy balance approach is the ability to assessthe relative importance of sublimation and melt in theablation process. The fractional contribution of melt tothe total ablation at the ELA calculated from theNCEP–NCAR climatology is shown in Fig. 5. There areplaces where ablation occurs almost entirely by meltand others by sublimation. In particular, a qualitativecomparison of the pattern in the fractional contributionof melt (Fig. 5) to the spatial pattern in annual averageprecipitation (Fig. 10a) indicates that, at the ELA, meltdominates where precipitation is high and sublimationdominates where precipitation is low.The relationship between the ablation pattern andprecipitation pattern is highlighted in Fig. 6. The scatterplot shows that melt dominates regions where precipitation is greater than 0.5 m yr 1 and sublimationdominates in regions where precipitation is less than 0.25 m yr 1. The shapes around the dots in Fig. 6represent the three different zones. These shapes highlight the regions dominated by sublimation and thosethat are dominated by melt. In particular, the easternzone is entirely dominated by melt at the ELA. Boththe western and northern zones include regions ofdominantly melt and sublimation. The reason for thegeneral relationship between ablation and precipitationcan be seen from a comparison of the seasonal cycle inthe surface temperature and energy balance (discussedbelow). The rapid transition between melt and sublimation is discussed further in section 6.c. Seasonal cycle in climate and energy balancetermsWe contrast the surface energy and mass balances atthe ELA for two reanalysis grid points, one in the meltdominated region and one in the sublimationdominated region. The two points are denoted by Aand B, respectively, in Fig. 5. Importantly, the resultsdiscussed for points A and B are not unique to thosepoints; the results are similar for the other grid points inmelt- and sublimation-dominated regions.First, for gridpoint A in the melt-dominated region,the total precipitation is 3.5 m yr 1 and peaks stronglyduring the summer monsoon (Fig. 7, top panel). Tomelt this depth of ice takes a flux of approximately 50W m 2 averaged over the year. In contrast, because theenergy required to sublimate is 8.5 times greater thanthat to melt, it would require approximately 425 Wm 2 to sublimate the total accumulation. It is thereforemuch more efficient for the system to melt the 3.5 myr 1. At the ELA, in order to balance this amount of
15 OCTOBER 2008RUPPER AND ROE5391FIG. 4. Grayscale is a high-resolution DEM of central Asia. Colored shading from blue to redhighlights those regions where and by how much the topography intersects the modeled ELAs.(lower) Locations of actual glaciers are highlighted in pink. This is not intended to be a test of themodel skill.accumulation, the SEMB model determines that thesurface temperature must be equal to 0 C for approximately five months (i.e., the length of the melt season),shown in the top panel of Fig. 7. Therefore the annual-mean surface temperature at the ELA must be higher,and the resulting ELA is lower.The seasonal cycle of the various energy fluxes areshown in the bottom panel of Fig. 7. For the 5-month
5392JOURNAL OF CLIMATEFIG. 5. Fractional contribution of melt t
mass balance. The surface energy and mass balance model is able to capture the regional differences in gla-cier mass balance and is in agreement with observations and model studies of individual glaciers. An analysis of the energy fluxes reveals the causes of these regi
The Malaspina Glacier in Alaska is a classic example of this kind of glacier. A cirque glacier is a small valley glacier that is confined to the high basin (cirque) at the upper end of a mountain valley. A snowfield is a perennial mass of ice and snow too small to move like a glacier. A visitor to Glacier National Park can see many cirque glaciers
Before 1750, Mendenhall Glacier was an advancing glacier; that is, more snow nourished the glacier than melted. At that time, it was two and one-half miles down the valley from its present position. Slightly warmer temperatures increased the rate of melt and the glacier began to recede. Today, the Mendenhall Glacier is continuing to recede slowly.
Glacier Mass Balance The purpose of this exercise is to illustrate features about glaciers mass balance and changes in glacier geometry. The glacier of interest here is South Cascade Glacier located on the crest of the North Cascade Range of Washington. It takes about 2.5 - 3 hours to drive to the nearest trailhead from Seattle.
AN OVERVIEW OF GLACIERS, GLACIER RETREAT, AND SUBSEQUENT IMPACTS IN THE NEPAL, INDIA AND CHINA. 2. India: Glaciers, glacier retreat and its impacts on Fresh Water Regime 29 Introduction 29 Physical and climatological characteristics 30 WWF. WWF. glacier retreat AN OVERVIEW OF GLACIERS, GLACIER RETREAT, China. NEPALINDIA. NEPAL.
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1 Overview of glacier classification systems and instructions to homogenize glacier data compilation for GLIMS Glacier Inventory 1.1 Introduction The detailed classification of a glacier is an important task during a GLIMS analysis session. To describe
in the northeastern area of Glacier National Park. Outside, awe-inspiring majesty. Inside, a magnificent towering lobby. MANY GLACIER HOTEL Built by the Great Northern Railway in 1914, Many Glacier Hotel is situated on the shores of Swiftcurrent Lake and looks out to
Grinnell Glacier is located within Glacier National Park in the state of Montana (Figure 1). It is a notable glacier that was named after a famous conservationist named George Bird Grinnell .The glacier has undergone heavy monitoring due to its significant retreating snowpack
