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Empirical mass balance modelling of South Americantropical glaciers: case study of Antisana volcano,EcuadorCarla Manciatia, Marcos Villacísb, Jean-Denis Taupinc, Eric Cadierc, Remigio Galárraga-Sánchezb & Bolívar Cáceresd

a Institut de Recherche Pour le Développement, IRD, Whymper 442 y Coruña, Quito, Ecuadorb Department of Civil and Environmental Engineering, School of Civil and EnvironmentalEngineering, Escuela Politécnica Nacional, Quito, Ecuadorc Institut de Recherche pour le développement, IRD, UMR 5569 Hydrosciences, UniversitéMontpellier I et II, CNRS, Maison des Sciences de l’Eau, F-34000 Montpellier, Franced Instituto Nacional de Meteorología e Hidrología, INAMHI, Quito, EcuadorAccepted author version posted online: 10 Feb 2014.Published online: 01 Jul 2014.

To cite this article: Carla Manciati, Marcos Villacís, Jean-Denis Taupin, Eric Cadier, Remigio Galárraga-Sánchez & BolívarCáceres (2014) Empirical mass balance modelling of South American tropical glaciers: case study of Antisana volcano,Ecuador, Hydrological Sciences Journal, 59:8, 1519-1535, DOI: 10.1080/02626667.2014.888490

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Empirical mass balance modelling of South American tropical glaciers:case study of Antisana volcano, Ecuador

Carla Manciati1, Marcos Villacís2, Jean-Denis Taupin3, Eric Cadier3, Remigio Galárraga-Sánchez2

and Bolívar Cáceres4

1Institut de Recherche Pour le Développement, IRD, Whymper 442 y Coruña, Quito, Ecuadorcalifuz@gmail.com2Department of Civil and Environmental Engineering, School of Civil and Environmental Engineering, Escuela Politécnica Nacional,Quito, Ecuador3Institut de Recherche pour le développement, IRD, UMR 5569 Hydrosciences, Université Montpellier I et II, CNRS, Maison des Sciencesde l’Eau, F-34000 Montpellier, France4Instituto Nacional de Meteorología e Hidrología, INAMHI, Quito, Ecuador

Received 16 June 2011; accepted 30 July 2013; open for discussion until 1 February 2015

Editor D. Koutsoyiannis

CitationManciati, C., Villacís, M., Taupin, J.-D., Cadier, E., Galárraga-Sánchez, R., andCáceres, B., 2014. Empirical mass balancemodellingof South American tropical glaciers: case study of Antisana volcano, Ecuador. Hydrological Sciences Journal, 59 (8), 1519–1535. http://dx.doi.org/10.1080/02626667.2014.888490

Abstract Most Latin American glaciers are located in the tropical Andes. The melting processes of Glacier “15”on Antisana volcano were studied to understand the relationship between glacier retreat and natural climatevariability and global climate change. Glaciers on the Antisana volcano are crucial sources of water as they feedthe headwater rivers that supply Quito with potable water. The aim of this study was to build empirical modelsbased on multiple correlations to reconstruct the mass loss of glaciers over a period of 10 years at three scales:local (data recorded by meteorological stations located around the volcano), regional (data from stations locatedaround the country) and global (re-analysis data). Data quality was checked using graphical and statisticalmethods. Several empirical models based on multiple correlations were created to generate longer time series(42 and 115 years) of the mass balance for the glacier ablation zone. The long mass balance series were comparedwith the temperature variation series of the Earth’s surface in the Southern Hemisphere to estimate the relationbetween the mass balance and global warming. Our results suggest that the meteorological factors that bestcorrelate with mass balance are temperature and wind.

Key words Ecuador; Andes; Antisana volcano; glacier; ablation zone mass balance; empirical models

Modélisation empirique du bilan de masse des glaciers tropicaux d’Amérique du Sud: cas duvolcan Antisana, EquateurRésumé La plupart des glaciers d’Amérique latine sont situées dans les Andes tropicales. Les processus de fusiondu glacier « 15 » sur le volcan Antisana ont été étudiés pour comprendre la relation entre le recul des glaciers et lavariabilité naturelle du climat et / ou le changement climatique mondial. Les glaciers situés sur le volcan Antisanasont des ressources en eau essentielles car ils alimentent les rivières qui fournissent l’eau potable de Quito.L’objectif de cette étude était de construire des modèles empiriques sur la base de corrélations multiples afin dereconstituer la perte de masse des glaciers sur une période de dix ans à trois échelles : locale (données enregistréespar les stations météorologiques situées autour du volcan), régionales (données provenant de stations situées dansle pays) et mondiale (données de réanalyse). La qualité des données a été vérifiée à l’aide de méthodes graphiqueset statistiques. Plusieurs modèles empiriques basés sur les corrélations multiples ont été créés pour générer desséries plus longues (42 et 115 ans) du bilan de masse de la zone d’ablation du glacier. La longue série de bilans demasse a été comparée à la courbe de variation de la température de surface de la Terre dans l’hémisphère Sudpour estimer la relation entre le bilan de masse et le réchauffement climatique. Nos résultats suggèrent que lesfacteurs météorologiques présentant la meilleure corrélation avec le bilan de masse sont la température et le vent.

Mots clefs Equateur ; Andes ; volcan Antisana ; glacier ; bilan de masse de la zone d’ablation ; modèles empiriques

Hydrological Sciences Journal – Journal des Sciences Hydrologiques, 59 (8) 2014http://dx.doi.org/10.1080/02626667.2014.888490

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INTRODUCTION

Different climate mechanisms can amplify or reducethe effects of a change in climate variables. Forexample, the surge in greenhouse gas concentrationshas caused an increase in temperature in severalplaces on Earth (Callendar 1938, IPCC 2007), andas a consequence, snow and ice have begun to meltto a greater extent than under normal conditions.

General circulation models used by, among others,the Intergovernmental Panel on Climate Change (IPCC2007, Schmittner et al. 2008, Solomon et al. 2009) showthat in comparison with current temperatures, futuretemperature variations could be higher at the summit ofhigh mountains than at lower elevations (Bradley et al.2006). Mountain glaciers have been melting since theend of the Little Ice Age. However, in recent decades,increased glacier melting has been detected in theHimalayas, the Alps and the Tropical Andes (Wagnonet al. 2007, Bates et al. 2008, Dobhal et al. 2008, Husset al. 2010). Glacier melting was recently confirmed inthe tropical Andes (Vuille and Bradley 2000), but there isevidence that the glacier retreat began to accelerate in1980 in Peru, (Brecher and Thompson 1993, Pouyaudet al. 2005), in Bolivia (Francou et al. 2000, 2003) and inEcuador (Cáceres et al. 2006). Temperature is the mostlikely explanation for glacier retreat in recent decades(Kaser 1999). Furthermore, Sicart et al. (2008) andJomelli et al. (2009) reported that inter-annual variabilityof the glacier mass balance is mainly controlled byannual variations in air temperature.

Variables such as inter-annual precipitation haveapparently not changed much in the past 100 years inSouth America (Vimeux et al. 2009), although lessabundant precipitation has sometimes been observedat local scale. This fact, together with convectivecloud cover, probably explains the successive nega-tive mass balances and the glacier retreat on MountKilimanjaro in Tanzania (Hastenrath 2001), in the“Cordillera Blanca” in Peru between 1930 and 1950(Kaser and Georges 1997) and between 1950 and2000 (Pouyaud et al. 2005), and by glaciers inBolivia (Wagnon et al. 1999a, 1999b).

However, other factors that could contribute to anegative mass balance such as a decrease in solar radia-tion absorption due to less cloud cover should not beruled out (Sicart et al. 2003, 2007, Vuille et al. 2003,Francou et al. 2004, Gallaire et al. 2010). Furthermore,in most studies, variation in the energy balance of tropi-cal glaciers is dominated by net shortwave radiation,which is related to albedo, one of the variables control-ling melting (Wagnon et al. 1999a, 1999b).

Andean tropical glaciers react rapidly to extremeclimate variability (Francou et al. 2003, Pouyaudet al. 2005, Soruco 2008, Vuille et al. 2008a,2008b). For example, during a strong El Niñoevent, accelerated retreat of glaciers in Ecuador hasbeen observed with a time lag of 3–6 months(Francou et al. 2004).

In 1995, a long-term observatory was set up onGlacier 15 on Antisana volcano in Ecuador toimprove knowledge of the processes and variablesthat control glacier retreat in the tropical Andes.Favier et al. (2004a, 2004b), Francou et al. (2004)and Vuille et al. (2008a) observed that temperatureand precipitation are the main factors that explainmelting and the annual mass balance of Glacier 15.In addition, the almost non-existent seasonality ofprecipitation and of temperature implies that localvariations are responsible for variations in the massbalance. For this reason, Favier et al. (2004b) sug-gested that solid precipitation is the key variableinvolved in annual mass balance variability. Favieret al. (2004b) and Francou et al. (2004) observedthat liquid precipitation can occur on the ablationzone of Glacier 15 and that the snowline altitude isvery sensitive to air temperature. Favier et al. (2004a)suggested that the glacier retreat is due to the effect ofglobal warming on the tropical troposphere and, as aconsequence, that the increase in temperature, ratherthan the albedo, causes increased absorption of solarenergy.

So, if the variables that control the annual massbalance are known, is the same true at a monthly scale?Can measured climate variables, such as monthly tem-perature, be linked with monthly precipitation? Theaim of this study was to look for a correlation betweena 10-year long monthly mass balance series andmonthly temperature and precipitation data to buildstatistical models at three scales: local (climate datarecorded by meteorological stations located aroundthe volcano), regional (climate data from stationsaround the country) and global (re-analysis data).

STUDY AREA AND CLIMATIC SETTINGS

Glacier 15 is located on the active Antisana volcano(Fig. 1), the subject of this study. Antisana volcano islocated in the Eastern Cordillera of the EcuadorianAndes (0°28′S, 78°09ʹW) 40 km east of Quito and isone of the 17 glaciers inventoried by Hastenrath(1981). The glacier is oriented to the northwest andhas two parallel tongues called 15α (south) and 15β(north) (Favier et al. 2004a). Since 1995, annual

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topographic measurements have been made on theglacier, along with permanent monitoring using anetwork of snow stakes located along the α tongueand two meteorological stations, one located on the αglacier tongue at 4850 m a.s.l. and the other close tothe glacier. These data enabled us to estimate theglacier mass and its energy balance at annual andmonthly scales. Future changes in the extent ofGlacier 15 could have consequences for the ecosys-tems around the glacier, as it is known that glaciersregulate run-off, and the Antisana volcano contri-butes 30% of annual flow in the basin. The reductionin glacier run-off could affect this regulation capacity(Villacís et al. 2010). There could also be conse-quences for Quito, the capital of Ecuador, becausemeltwater from the glacier feeds the rivers that pro-vide the supply of potable water to several neigh-bourhoods in the city (Francou et al. 2004).

Our study area was the ablation zone of Glacier15α, which extends from 5300 m a.s.l. down to 4850m a.s.l. This zone was within the range of fluctuationof the equilibrium line during our study period. Theablation zone represents about 27% of the total areaof the glacier and is covered with a dense network ofsnow stakes (Francou et al. 2004, Favier et al. 2008).The Antisana study area is representative of theEastern Cordillera because it is directly exposed tothe humid wind from the Amazon basin. From 1995

to 2006, annual mean precipitation on Glacier 15ranged from 720 mm year-1 at 3930 m to1300 mm year-1 at 4850 m. However, the northwes-tern slope of Glacier 15 is in a relatively protectedposition and has less cloud cover and an annual meanprecipitation of 925 mm year-1 at 4785 m. The twomain periods of precipitation are from February toJune (45% of total annual precipitation) and fromSeptember to November (25%), and two relatively“dry” seasons: from December to January with 11%and from June to August with 19% of total annualprecipitation. In fact, there is no pronounced dryperiod, and the minimum monthly precipitation is>50 mm. Consistent with the pattern of precipitation,the convective cloud cover also has two maxima, onein March–April–May and a lesser one in September–October–November, separated by two minima inJune–July–August and in December–January–February. The average monthly temperature is rela-tively stable with no significant seasonal variations.Wind is the main seasonality factor in this part of theEcuadorian Cordillera. The meteorological year canbe divided into two periods: from April to September,when the winds come from the high and mid-tropo-sphere mainly from the east and are strong and fairlyconstant, and from October to March, when thewinds are weak and intermittent, particularly duringOctober–January (Francou et al. 2004).

81°0'0''W 80°0'0''W 79°0'0''W 78°0'0''W 77°0'0''W 76°0'0''W

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Fig. 1 Location of the Antisana volcano in Ecuador.

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Overall, the glacier had a negative mass balancefor the period 1995–2006, except for 1999–2000,when the glacier advanced 40 m, cf. the 159 maccelerated retreat between 1995 and 1998 (Cácereset al. 2006).

AVAILABLE DATA

Based on monthly and seasonal analysis of local andregional hydrometeorological data, and data on themelting of Glacier 15α, a first model of the glacierwas built and the variables linked with the retreatprocess were defined at a monthly scale.

Mass balance was calculated using data from thenetwork of stakes surveyed at the beginning of eachmonth. A topographic survey and monitoring of pitsand stakes located along tongue α have enabled esti-mation of the annual mass balance of Glacier 15 since1995 (Favier et al. 2004a, Francou et al. 2004). InJune 1994, 14 stakes were inserted into 10-m-deepholes on the central axis of the glacier between 5050and 4840 m. In the following year, more stakes wereadded on the north and south sides of the tongue tobetter capture spatiotemporal variations in seasonalablation. From then on, a minimum of 20 stakes wasregularly surveyed and removed every year in Januaryand relocated 20–30 m upstream. Each 50-m incre-ment of elevation includes at least four stakes. Themethod used to calculate the mass balance is describedin detail in the study by Francou et al. (2004). Theseauthors divided the ablation zone into elevation incre-ments, performed homogeneity tests between all thestakes located in one elevation increment and dis-carded points that were not representative. They alsoreport that new land surveys are performed every yearusing a theodolite with distance measuring equipmentto account for changes in surface elevation. Thismethod is still being used on Antisana Glacier 15today. The mass balance is measured in millimetresof water equivalent.

In this study, precipitation monitored around theAntisana volcano was considered from 1995 to 2006.These stations are managed by the GREATICEproject – Glaciers et Ressources en Eau desAndes Tropicales, Indicateurs Climatiques etEnvironnementaux (Glaciers and Water Resources inthe Tropical Andes, Climate and EnvironmentIndex), together with INAMHI – Instituto Nacionalde Meteorología e Hidrología del Ecuador (NationalInstitute of Meteorology and Hydrology of Ecuador),EPMAPS – Empresa Pública Metropolitana de AguaPotable y Saneamiento de Quito (Water andSanitation Utility Company) and the French IRD –Institut de Recherche pour le Développement(Research Institute for Development). The data weretreated statistically, and the information can be con-sidered as continuous during this period. Some datawere missing in the 10-year data series due to sensormaintenance, lost information or devices that werebroken by strong winds or the extreme climate con-ditions (Table 1). Missing data were replaced usingstatistical correlations between nearby stations.Figure 2 shows the location of the meteorologicalstations, Glaciar 00 (P0), Morrena 02 (P2),Totalizador 03 (P3), Antisana 04 (P4), y Mica 05(P5) and Humboldt 06 (P6) around the Antisanavolcano. Stations P5 and P6 are located in the“páramo” and at the foot of the glacier. The“páramo” is a complete and complex ecosystem thatis capable of storing large amounts of water due tothe type of soil in this environment and is generallylocated between 3500 and 4500 m.

Local daily precipitation data came from200-cm2 cross section standard raingauges (HOBOEvent raingauge), and monthly precipitation datacame from total raingauges. The total raingaugescomprised a tube of about 1.50 m in height and0.50 m in diameter, in which precipitation is col-lected. Monthly rainfall was measured by the differ-ence in water level in the cylinder. To preventevaporation, a thick layer of oil was poured into the

Table 1 Information on local stations located around Antisana volcano. The coordinates are expressed in the UTM WGS84system.

ID Name Longitude Latitude Altitude (m a.s.l.) Date station established % missing data

P0 Glaciar 817123 9947944 4850 Apr-01 0.0P2 Morrena 816805 9948201 4785 Jan-95 2.1P3 Totalizador 816271 9948470 4555 Jan-95 1.4P4 Antisana 815888 9948851 4455 Jan-97 5.0P5 Mica 809054 9942411 3930 Jan-01 0.0P6 Humboldt 810430 9943645 4059 Jan-95 11.1

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total raingauge. Precipitation data is given inmillimetres.

Mean monthly precipitation data and meanmonthly temperature were provided by INAMHIfrom 50 meteorological stations nationwide andwere the longest and the best series. Informationfor the 1995–2006 period was available as rawdata and corresponded to the same period forwhich data were available for the glacier. In addi-tion, some data used in this study were provided bythe Dirección de Aviación Civil (Office of CivilAviation) from airport weather stations. After statis-tical analysis, we retained only three stations whoselocation is shown in Fig. 1. Precipitation was mea-sured in millimetres, and temperature was measuredin °C.

National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis climate data were compiled fromthe International Research Institute for Climate andSociety website (http://iridl.ldeo.columbia.edu/) forthe same period as the local and regional data. There-analysis data used were Sea Level PressureAnomaly, also known as SOI (Southern OscillationIndex, dimensionless); this index expresses the dif-ference in pressure between Tahiti and Darwin,Australia. Other global variables were taken for thecoordinates 0.00°S and 77.5°W, such as T500, T600and T700 (500, 600 and 700 hPa temperatures in °C,respectively). The wind speed index was calculatedfrom zonal and meridional wind re-analysis(Index_u500hPa, expressed in m s-1). In addition,

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Fig. 2 Location of GREATICE project stations on the Antisana volcano.

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some El Niño data were used: NINO 1 + 2, NINO3 + 4 (El Niño Southern Oscillation index (ENSO)in 1 + 2 zone and 3 + 4 zone); all El Niño indexeswere calculated in °C (NWS 2010).

METHODOLOGY

Data were processed and an analysis was made of thecorrelations betweenmass balance and local and regionalhydrometeorological data atmonthly and seasonal scales.

Local precipitation

The simple linear correlation statistical method wasused to homogenize the local precipitation data forthe Antisana study site for the 1997–2004 period tofill the gaps in the time series and to look for correla-tions between meteorological stations (Lhuissier2005) and was completed with existing data for theperiod from 1996 to 2006. The best correlationsbetween stations were always for stations located ashort distance from one another. Correlation valuesand statistical significance values are listed inTable 2. These data were used to build the models.

Regional precipitation

At the regional level, available data were analysedand the percentage of missing information was deter-mined. Only regional stations with no more than 10%of missing data were retained for statistical analysis.

We used principal component analysis (PCA) toidentify groups of regional meteorological stationsand to complete the 10% of missing data. The PCAwas very useful to find groups of inter-correlatedstations. Then, based on the results of the PCA, weapplied the simple correlation method to the regionalstations to find equations that enabled us to fill thegaps in the data. Next, again using PCA, we includedthe mass balance of the ablation zone and the global

re-analysis data in the analysis to find links betweenall the variables and classify them into groups.Finally, with these groups, we used multiple correla-tions, a stepwise forward approach, to find equationswith a significant correlation between the mass bal-ance and the other data.

RESULTS AND DISCUSSION

Preparing input data

Local information A first zone was delimitedcomprising stations Glaciar 00 (P0), Morrena 02(P2), Totalizador 03 (P3) and Antisana 04 (P4).These stations belong to the highest zone in thenorthwest region of the volcano (Fig. 2) where pre-cipitation is greatest. Correlation coefficients (r2)between the stations in this group ranged from 0.64to 0.88 (Table 2). The lower monthly precipitationvalues recorded at Morrena 02 station could be due toits location as it is exposed to the wind, thus reducingthe proportion of snow and rainfall received by theraingauge, as explained by WMO (2006).

A second zone was created comprising the othertwo local stations: Mica 05 (P5) and Humboldt 06(P6), which are located on a more western part of thevolcano. The correlation coefficient (r2) between thestations was 0.73 (Table 2). Given the altitude andthe different origin of the humidity, this zone receivesless rainfall, resulting in a climatic differencebetween the two zones. The correlation coefficientbetween the two zones ranged from 0.31 to 0.42.

Based on the earlier discussion, we divided thelocal stations into two groups located in these two“climate zones” on Antisana volcano and calculatedtwo precipitation indexes representing the precipita-tion of each climate zone.

The precipitation indexes were chosen accordingto Cadier et al. (2007), who tested the P_Glaciar andP_Param precipitation indexes. The P_Glaciar indexincludes stations P0, P2, P3 and P4; the P_Glaciarindex for January 1996 is the mean precipitation ofP0, P2, P3 and P4 for January 1996 weighted by theaverage of the mean precipitation in January at sta-tions P0, P2, P3 and P4 for the whole period (1996–2006) (Table 1). The P_Param index includes stationsP5 and P6 and is derived similarly.

The averages of these precipitation indexes wereestablished for the previous 1, 2, 4, 6, 9 and 12months, and the correlations between the glaciermass balance and the above-mentioned indexeswere tested. The results showed that the glacier

Table 2 Determination of coefficient r2 between local plu-viometric stations. Period 1996–2006. Significant correla-tions are in italics. For all correlations, p < 0.01.

P0 P2 P3 P4 P5 P6P0 0.78 0.88 0.77 0.37 0.42

P2 0.78 0.80 0.64 0.31 0.38P3 0.88 0.80 0.77 0.36 0.42P4 0.77 0.64 0.77 0.33 0.37P5 0.37 0.31 0.36 0.33 0.73P6 0.42 0.38 0.42 0.37 0.73

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mass balance presents a better correlation with the 4,6, 9 and 12 previous months’ averages of theP_Param index (Table 3).

Global information Recent studies on inter-annual mass balance variability in the Bolivian andEcuadorian glaciers (Soruco 2008, Villacís 2008)documented this variation more precisely. These stu-dies confirmed that years with a more negative massbalance than a normal year coincide with strongENSO events in its warm phase. Based on previousstudies, the accelerated glacier retreat in this region isbelieved to be linked with the increase in the fre-quency and intensity of El Niño events in the pastthree decades (Francou et al. 1995). Likewise, astudy of mass balance variability in the CordilleraBlanca in Peru showed that the mass balance reactsto large-scale climate fluctuations related to climateconditions in the tropical Pacific (Kaser et al. 2003).Generally speaking, at an annual scale, the ENSO isresponsible for mass balance anomalies, which arepositive during strong La Niña events and negativeduring strong El Niño events (Vuille et al. 2008b).Based on these facts, we used global indexes todetermine their correlation with the ablation zonebalance at a global and historical scale. These indexeswere the NCEP/NCAR re-analysis data and theywere introduced into the models. The correlationcoefficient between these global indexes and theablation zone mass balance is shown in Table 4.The global indexes that showed the best and mostsignificant correlation coefficients with mass balance

were SOI, the temperature at 500 hPa, the tempera-ture at 600 hPa and the wind speed index. We testedwhether introducing a time lag in the global indexesreferring to El Niño, such as SOI, Niño 3 + 4 or Niño1 + 2, improved the correlation coefficients.

We found that with this method, only El Niño 3 + 4increased the correlation coefficients. A 3-month lag and4-month lag in the El Niño 3 + 4 index showed the bestcorrelations with the balance in the ablation zone of theglacier (Table 5). These results are in agreement withthose of Cadier et al. (2007) and Francou et al. (2004).

Statistical models

Using the same length of record (132 data) for eachanalysis in the 1996–2006 period, we then applied astepwise forward approach. This approach allows thenumber of variables used in the analysis to be mod-ified until a good correlation is found.

In this way, we found that among the 50 stationsused, there was a good correlation between the massbalance and mean monthly temperature at Izobambastation (M003, from 1962 to date) located in theInter-Andean Region, and with mean monthly rain-fall at Pilahuin station (M376, from 1964 to date),which is located in the same region (see Fig. 1). Theirdata quality and length of record are listed in Table 6.There was also a good correlation between massbalance and the global temperature at 500 and at600 hPa (Table 4), which takes into account theexplanation of the physical processes at Antisanaglacier given by Favier et al. (2004a, 2004b),Francou et al. (2004) and Vuille et al. (2008a). We

Table 3 Determination of coefficient r2 between P_Glaciarand P_Param indexes and the ablation zone mass balancewith time lags in months. Period 1996–2006. p valueswere always less than 0.01.

Time lag (months) 1 2 4 6 9 12

P_Glaciar 0.19 0.27 0.32 0.32 0.28 0.30P_Param 0.20 0.31 0.36 0.39 0.37 0.37

Table 4 Determination of coefficient r2 between the ablation zone mass balance and global re-analysis data (1996–2006).The p values show statistical significance.

Temp. at Index of wind speed(Index_u500hPa)

SOI pressureanomaly

El Niño3 + 4

El Niño1 + 2

500 hPa 600 hPa 700 hPa

p 0.001 0.001 0.001 0.001 0.001 0.0003 0.247r2 0.20 0.25 0.12 0.23 0.21 0.14 0.01

Table 5 Determination of coefficient r2 between the abla-tion zone mass balance and El Niño 3 + 4 index (1996–2006). The time lag is expressed in months. For all corre-lations, p < 0.01.

Time lag (months) 0 3 4 5 6 7

El Niño 3 + 4 0.14 0.29 0.29 0.26 0.22 0.18

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used the wind speed calculated from the zonal andsouthern wind components at 500 hPa and tempera-ture at 700 hPa, even though this last variable has alow correlation coefficient with mass balance(Table 4).

Next, we used the results of the previous analy-sis to reconstruct the historical mass balance in theglacier ablation zone. The oldest Ecuadorian meteor-ological station is Quito Observatorio (M054), whichwas in operation from 1891 to 1985. We linked thetemperatures at this station and at Izobamba station(M003) between 1962 and 1985 and used the corre-lation to reconstruct the data at Izobamba stationbetween 1891 and 1961 and then correlated themwith the mass balance of the glacier ablation zone.

The results were encouraging and a good fit wasobtained despite the fact that it would have beenbetter to directly relate temperatures at station M054with the mass balance, but this was unfortunately notpossible, as information at station M054 is availableonly up to 1985.

Model 1: Local and regional model

This model includes all the available variables suchas local rainfall indexes (dimensionless), regionalrainfall (mm) and temperature (°C). The model usestemperatures recorded at Izobamba station (for thepurpose of this paper, this variable was namedTM003), the P_Param4, P_Param6, P_Param9 andP_Param12 local indexes and rainfall recorded atPilahuin station (M376) from January 1996 toDecember 2006, a total of 132 data, i.e. the samerecord length as the record of the local variables.

First, the model was built with the differentP_Param indexes; we found that the r2 of the modelwith P_Param4 was 61%, with P_Param6 r2 = 63%,with P_Param12 r2 = 61% and with P_Param9 r2 =60%. The statistical significance of all the correlationcoefficients was less than 0.01. In this study, thecorrelation coefficients were more or less the samefor the four options, but we used the model with the

P_Param9 index so as to be able to compare ourresults with those of Francou et al. (2004) andCadier et al. (2007) for a shorter period. This com-parison revealed that the mass balance in the ablationzone could be influenced by climatic conditions suchas precipitation near the glacier and in the vicinity ofthe volcano.

This model explained 60% of the variance in theglacier balance and was significant within a confi-dence interval of 95%. It included temperaturesrecorded at the meteorological stations located inthe Inter-Andean region because temperature indir-ectly controls albedo and hence melting of the glacier(Favier et al. 2004a, 2004b, Francou et al. 2004). Theequation is as follows:

Balance ¼ 1178þ 430 � P Param9

þ 1:08 �M376� 161:8 � TM003

r2 ¼ 0:60

(1)

As the local variables refer to the same period of theglacier mass balance, this model cannot be used tomake any extrapolation in time.

When using Model 1, which includes local andregional variables, it is important to check that therelationship between the variables is not strongenough to cause model inconsistencies. If so, thevariables could interfere with each other, hence redu-cing the validity of the model, which consequentlycould not be used for extrapolation and interpolation.The relationships between variables are presented inTable 7.

Table 6 Information from regional stations that correlated well with the ablation zone mass balance. These stations wereused in the statistical models. The coordinates are expressed in the UTM WGS84 system.

Code Station names Longitude Latitude Altitude (m a.s.l.) Date stationestablished

Date stationremoved

% ofmissing data

M003 Izobamba 772372.30 9959896.00 3058 Feb-62 - 0.00M376 Pilahuin 779894.80 9980699.00 2789 Aug-64 - 2.80M054 Quito Observatorio 752543.90 9855950.00 3360 Jan-1891 Dec-85 0.20

Table 7 Test of independence of variables for Model 1,local and regional; r2 values with p values in parentheses.

Variable P_Param9 M376 TM003

P_Param9 0.04 (0.03) 0.24 (0.00)M376 0.04 (0.03) 0.09 (0.00)TM003 0.24 (0.00) 0.09 (0.00)

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Having independent variables is a precondition forcorrect modelling. The model is valid and the massbalance of the ablation zone is correlated with tem-perature at Izobamba station (M003), with precipita-tion at Pilahuin station (M376), and with the maininfluence of the local Páramo precipitation index(P_Param9). As can be seen in Fig. 3, the model isstatistically significant and its values are very close toobserved values. In addition, the signs in the equations,which are positive for rainfall variables and negativefor temperature, are coherent with the physical pro-cesses we mentioned in the Introduction Section.

These variables can thus be used to add informa-tion to the model to generate missing data in the massbalance. However, this did not enable the generationof a longer series than the observed series becauselocal rainfall data are only available from 1995onwards. Therefore, based on Model 1, two moremodels were built (Model 2 and Model 3, presentedlater in this paper), to attempt to obtain a singlemodel that could generate historical mass balancedata in the ablation zone.

Model 2: Full regional and global model

We attempted to find the best correlation using regio-nal variables and global variables. The P_Param plu-viometric index was not used in this analysis. Ourresults showed that the best correlation equation has

the following variables: temperature at 700 hPa (in°C), precipitation recorded at Pilahuin station (M376,in mm) and temperature recorded at Izobamba station(M003, in °C). Initially, the model was calibratedwith 132 points corresponding to the length of dataof the mass balance. The resulting equation was asfollows:

Balance ¼ 2343þ 1:73 �M376

� 158:1 � TM003� 93:7 � T700r2 ¼ 0:54

(2)

However, a high determination coefficient wasfound for the relation between the variables TM003and T700 (34% in Table 8). This could be considerednormal when dealing with average monthly tempera-tures over a limited area. Consequently, using bothvariables could be considered redundant for thismodel and so we decided to discard the T700 variable.

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Fig. 3 Observed and calculated balance at the ablation zone from Model 1 for the period 1996–2006. The solid black line isthe observed measured balance in the ablation zone and the dashed grey line is the balance calculated using equation (1),r2 = 0.60. The negative value of ablation zone mass balance was plotted to enable comparison of this figure with others.

Table 8 Test of independence of the variables for Model 2,regional and global; r2 values with p values in parentheses.

Variable TM003 M376 T700

TM003 0.10 (0.00) 0.34 (0.00)M376 0.10 (0.00) 0.01 (0.34)T700 0.34 (0.00) 0.01 (0.34)

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The resulting model has a lower correlation coef-ficient and explains 50% of the variance of the massbalance of the glacier. This correlation is statisticallysignificant within a confidence interval of 95%. Theequation is as follows:

Balance ¼ 2350þ 1:24 �M376

� 223:9 � TM003

r2 ¼ 0:50

(3)

As there are no inconsistencies in the equation,historical data from the meteorological stations wereused as input data in the model. Thus, mass balanceinformation was generated for the ablation zone toobserve its behaviour for the period when data wereavailable, i.e. from 1964 to 2006. For that purpose,all available information from these variables wasused.

The relation between variables in this model isrepresented by the coefficient of determination(Table 8). It is worth noting that the coefficients ofdetermination between the variables are low (lessthan 10%), as confirmed by the statistical signifi-cance, indicating a high degree of independencebetween the variables. The variables thus contributeinformation to the model. This model allows infor-mation on the mass balance in the ablation zone to beextrapolated by generating a new series of data after1964 and reveals the trend of the mass balance since

1964. Like Model 1, this model also has a positivesign for the precipitation variable and a negative signfor the temperature and so is coherent with the phy-sical processes.

From equation (3), several graphs of the calcu-lated balance in the ablation zone were plotted andcompared with the observed balance. Figure 4 showsthe fit between the model and the observed balancevalues for the ablation zone. The peaks calculatedfrom the equation are shorter than the observedpeaks. This means that even though the model has agood fit, in some cases the trends are less markedthan in the observed balance.

After comparing the trends revealed by themodel with respect to the annual average variationin the Earth’s surface temperature, both global tem-perature and temperature in the Southern Hemisphere(Jones and Salmon 2008), we decided to use varia-tions in the Earth’s surface temperature in theSouthern Hemisphere because the impact of globalwarming is lower than that expected in the NorthernHemisphere and worldwide (IPCC 2007). Figure 5plots the long mass balance series in the ablationzone calculated with equation (3) and the variationin the Earth’s surface temperature in the SouthernHemisphere; in the graph, temperature anomaly isplotted vs the negative value of the balance in theablation zone. Figure 5 shows that the two serieshave similar trends. The annual correlation coeffi-cient between the mass balance in the ablation zone

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Fig. 4 Comparison between the observed and calculated balance at the ablation zone using Model 2 (1996–2006). Thesolid black line is the observed measured balance in the ablation zone and the dashed grey line is the balance calculatedusing equation (3), r2 = 0.50. The negative value of the ablation zone mass balance was plotted to enable comparison of thisfigure with others.

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calculated with equation (3) and variation in theEarth’s surface temperature in the SouthernHemisphere for the period 1964–2006 is r2 = 0.67.

Model 3: Regional and simplified global model

Reconstruction of data from Izobamba sta-tion (M003) This model only uses temperature datafrom Quito Meteorological Station (M054, in °C) asinput data. The aim is to extrapolate the availableinformation to Izobamba station (M003). To ensurethe model would be feasible with input data fromQuito station, temperature data from Izobamba sta-tion were generated from 1891 on.

The data series from Izobamba station explains63.5% of the variance, is statistically significantwithin a confidence interval of 95%, and uses 287data (common data series between Izobamba stationand Quito station, from February 1962 to December1985). It is written:

TM003 ¼ 1:537þ 0:737 � TM054

r2 ¼ 0:64(4)

Outline of Model 3 Based on the series gener-ated for Izobamba station from 1891 on, this newinformation was again related to the balance in theablation zone. The data from 1891 were determinedand revealed a trend over time.

Model 3 explains 47% of the balance variance, isstatistically significant within a confidence interval of95%, and used 132 data (length of record of the massbalance of the ablation zone). However, in futurestudies, it will be possible to build a stronger modelusing either empirical or physical models that includeprecipitation, which would make it possible toobserve the past behaviour of the mass balance ofthe ablation zone. Simulations of general precipita-tion and regional atmospheric circulation models cur-rently available for the Andes are not yet sufficientlyreliable (Garreaud et al. 2009) to enable observedprecipitation to be replaced by precipitation gener-ated from general atmospheric circulation models inthe statistical models presented here.

The equation for Model 3 is:

Balance ¼ 2664� 244 � TM003

r2 ¼ 0:47(5)

It was built to eliminate the redundant variables inModel 2 (we retained temperature at Izobamba sta-tion, TM003, in °C), and provide a simpler modelwith just one variable. Temperature was recon-structed for the 1891–1962 period using informationfrom Quito station (TM054), based on equation (5).

Figure 6 shows how Model 3 fits the observedbalance for the 10-year study period. Figure 7 showsthe negative value of the observed and calculated

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Fig. 5 Observed and calculated values of the balance at the ablation zone using model 2 for the 1964–2006 period.Comparison of melt modelling and variation in the Earth’s surface temperature in the Southern Hemisphere, which gives anannual value of r2 = 0.67. The grey dashed line is the calculated mass balance in the ablation zone using equation (3) withan r2 = 0.50; the thin solid line is the measured mass balance in the ablation zone of Glacier 15α; and the thick solid lineshows the variation in the Earth’s surface temperature in the Southern Hemisphere. The negative value of ablation zonemass balance was plotted to enable comparison of trends with the increase in temperature during 1964–2006.

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balance using Model 3 for 1891 to 2006, to enablecomparison with the temperature of the Earth’s sur-face in the Southern Hemisphere.

Model 3 shows good agreement between thevariation in the calculated balance for the ablationzone from 1891 to 2006 and the variation in thetemperature of the Earth’s surface in the SouthernHemisphere in the same period (annual r2 between

these two variables = 0.71). It was important to takethe trend of the variation in the Earth’s surface tem-perature into account in the model so that it could becompared with temperature variations in the past.The model shows that temperature variations couldbe higher at the summit of high mountains than atlower elevations, as observed by Bradley et al.(2006) in global circulation models.

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Fig. 6 Comparison between the observed and calculated balance in the ablation zone based on the results of Model 3 forthe 1996–2006 period. The solid black line is the measured balance in the ablation zone and the grey dashed line is thebalance calculated using equation (5), r2 = 0.47. The negative value of ablation zone mass balance was plotted to enablecomparison of this figure with others.

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Fig. 7 Observed and calculated values of the balance at the ablation zone using Model 3 for the 1891–2006 period.Comparison of modelled melt and variation in the Earth’s surface temperature in the Southern Hemisphere (IPCC) has anannual value of r2 = 0.71. The grey dashed line is the calculated mass balance of the ablation zone using equation (5) withr2 = 0.47; the thin solid line is the measured mass balance of the ablation zone of Glacier 15 and the thick solid line is thevariation in the Earth’s surface temperature in the Southern Hemisphere.

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Model 4: Calibrating the regional and globalmodel

This model was developed by Villacís (2008) as acontinuation of Model 3. It uses the temperature (in°C) recorded at Izobamba station, but also the windvelocity index (in m/s) of the mean of the previous12 months at 500 hPa (Index_u500hPa), whichis significantly correlated with the mass balance(Table 4). This index can be interpreted as an indi-cator of atmospheric circulation perturbations in theEcuadorian Amazon region caused by ENSO events.The wind could be considered as a flow index(Marshall et al. 2007) and it could replace the pre-cipitation index developed by Cadier et al. (2007) inreconstructing the mass scheme. The proposed equa-tion is as follows:

Balance ¼ 1891þ 62 � Index u500hPa

� 212 � TM003

r2 ¼ 0:53

(6)

Model 4 allows the mass balance in the ablationzone to be calculated after 1949. It explains 53% of thevariance of the mass balance in the glacier ablationzone, has a good fit for the period from 1996 to 2006,is significant within a confidence interval of 95%(Fig. 8) and the sign of each variable is coherent withthe physical processes. Figure 9 compares the calcu-lated and observed balance from 1962 to 2006, plotted

as negatives, and the variation in the Earth’s surfacetemperature in the Southern Hemisphere in the sameperiod, showing the trends; the annual r2 between thecalculated balance and temperature is 0.78.

Model 4 is coherent with observations of thebalance at the ablation zone at a regional scale, i.e.with more accumulation than ablation occurring from1965 to 1975 (Fig. 10) in response to higher precipita-tion and lower temperatures than in the 1980s and1990s. Climatic conditions caused either a glacierretreat (1980–1990) or a glacier advance (1965–1975with a higher precipitation and lower temperature) inthe case of Antisana glacier and other Andean tropicalglaciers (Kaser et al. 2003, Rabatel et al. 2013).Similiar glacier advance behaviour was observed inNew Zealand in the same period (1965–1975) in simi-lar climatic conditions (Fitzharris et al. 2007).

In Ecuador, inter-annual variation in precipita-tion is significantly influenced by ENSO from Juneto November (Vuille et al. 2000, Villacís et al. 2003).In contrast, between 1963 and 1975 (first period) andbetween 1975 and 1990 (second period), a largenumber of positive anomalies were observed duringthe first period and of negative anomalies during thesecond period (Villacís 2008). On Antisana volcano,precipitation between June and November counts for44% of total annual precipitation. Consequently, thecontrasting behaviour of the mass balance observedduring the periods 1963–1975 and 1975–1990 couldbe explained by variations in ENSO and PacificDecadal Oscillation during these periods (Espinoza

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Fig. 8 Comparison between the observed and calculated balance in the ablation zone based on the results of Model 4 forthe period 1996–2006. The thick black line is the measured balance in the ablation zone and the thin grey dashed line is thebalance calculated using equation (6), r2 = 0.53.

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et al. 2009). The first period was characterized bycolder temperatures, faster wind speed and positiveprecipitation anomalies, while the opposite condi-tions were observed in the second period. Model 4is able to reproduce the 1975 inflection observed byKaser et al. (2003) in Peru, whereas Model 3 is not.Villacís (2008) showed four periods with differentresponses by Glacier 15α (1963–1975, 1975–1990,1990–2001 and 2001–2006), as shown in Fig. 10.The inflection points were chosen to compare themwith the aerophotogrammetry data available for those

dates and with the results obtained by Kaser et al.(2003) in Peru and by Rabatel (2005) and Ramírezet al. (2001) in Bolivia. This information enabledvalidation of Model 4.

The same inflections of the mass balance wereobserved at a regional scale in Ecuador, Peru andBolivia around 1970 (Ramírez et al. 2001, Kaseret al. 2003, Rabatel 2005, Rabatel et al. 2013).Model 4 appears to be robust because the role oftemperature is complemented by that of wind velo-city. This index represents slight atmospheric

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Fig. 9 Observed and calculated balance in the ablation zone using Model 4 for the 1962–2006 period. Comparison ofmodelled melt and variation in the Earth’s surface temperature in the Southern Hemisphere (IPCC), which has an annualvalue of r2 = 0.78. The grey dashed line is the mass balance calculated in the ablation zone using equation (6) withr2 = 0.53; the thin solid line is the measured mass balance in the ablation zone of Glacier 15 and the thick solid line is thevariation in the Earth’s surface temperature in the Southern Hemisphere.

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variations (variation in the east–west circulation) thatinfluence precipitation (Villacís 2008).

CONCLUSIONS

With regard to data analysis and gap filling at thelocal scale, i.e. for the stations located west andnorthwest of the Antisana volcano, new insightswere obtained regarding the climate around the icecap. Two main climatic zones were identified. Thefirst zone is affected by eastern winds and higherprecipitation. The second zone, located to the west,has less precipitation because it has an orographicboundary that protects it from the influence of thesewinds.

Local precipitation indexes were used because asignificant relation was found between these indexesand the balance of the ablation zone of the glacier.Interestingly, the fit was much better using the indexof the variables located in the Páramo: Mica 05,Humboldt 06 with a 9-month time lag (P_Param9,which is included in the local model).

At regional scale, a good relationship was foundboth for temperature and for precipitation betweenthe mass balance and the stations located in the Sierraregion and the Amazonian stations. However, a sig-nificant correlation was found between glacier melt-ing and rainfall at Pilahuin station (M376) andtemperature at Izobamba station (M003), which areincluded in Model 2 in this study.

Model 3 was developed using the temperaturerecorded at Izobamba station (TM003). A historicaltime series of the behaviour of the ablation zonebalance from 1891 to 2006 was generated. It wasvery important to build a model that enabled pasttrends of the mass balance of the ablation zone ofAntisana volcano to be traced, because these changesmirror a major trend showing a general increase intemperature in the parts of the country covered byglaciers. The calculations prove that these glaciershave been retreating for the past 150 years, but thatthe rate of retreat has accelerated in the past 30 years,while global temperatures are increasing but theincrease has not yet reached 1°C. Scenarios basedon an increase of 2–6°C by the end of this centurypredict a more serious outcome.

Model 4, which also comprised temperaturesrecorded at Izobamba station, included an index ofwind speed to represent the flow of moisture reachingAntisana volcano. This model allowed reconstructionof the mass balance in the ablation zone of the glacier

in the past and simulated what was observed at aregional scale in Ecuador, Peru and Bolivia i.e. aperiod of stagnation of glaciers between 1956 and1975 and a period of rapid retreat after 1976.

For the generation of new models, an indexcould be derived with the precipitation recorded atPilahuin station (M376). An average over the 9months before the month in which the analysis isperformed can be calculated (as was done here atthe local scale), to try to eliminate the temperatureat 700 hPa from the model. Alternatively, an aver-age calculated using the stations located near sta-tion M376 could be used, as seen in the PCA, andan index of the previous 9 months could beobtained.

These results are encouraging for the study ofthe future changes in water resources from glacierareas based on climate variations. Temperature andwind are the most reliable meteorological data sup-plied by general circulation atmospheric models usedby researchers for forecasting future climates.However, these empirical relationships of a statisticalnature need to be confirmed in the future.

The models presented here produced new resultsthat are valid for areas where few data are availableon glacierized surfaces. These models can be used fornew research on ice caps, such as the new studies onthe annual morphology of glaciers on Cotopaxi vol-cano (Cáceres et al. 2004) and Iliniza Sur (Febres2007) located in the Eastern Cordillera and in theWestern Cordillera, respectively. The empirical mod-els presented here could be validated at theselocations.

Acknowledgements The authors wish to thank theNational Institute of Meteorology and Hydrology,INAMHI, which provided a large proportion of theinformation used in this study. We also thank theInstitut de Recherche pour le Développement, IRD,who allowed us access to the hydrometeorologicalinformation from stations belonging to theGREATICE project in the Antisana. The authors aregrateful to the two anonymous reviewers for theirconstructive remarks.

Funding The Institut de Recherche pour leDéveloppement, IRD, provided financial assistancethat enabled this study to be completed and researchrelated to glacier melting to continue. Marcos Villacíswishes to thank IRD-DSF for his PhD scholarshipand also because this publication was made possible

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through support provided by IRD-DSF; EPN forteam training in the use of JEAI IMAGE; andSENESCYT for grant PIC-08-506.

REFERENCES

Bates, B.C., et al., 2008. Climate change and water. Technical Paperof the Intergovernmental Panel on Climate Change. Geneva:IPCC, Secretariat, 210 pp.

Bradley, R., et al., 2006. Threats to water supplies in the tropicalAndes. Climate Change Sciences, 312 (5781), 1755–1756.

Brecher, H. and Thompson, L., 1993. Measurement of the retreat ofQori Kalis glacier in the tropical Andes of Peru by terrestrialphotogrammetry. PE & RS, 59 (6), 1017–1022.

Cáceres, B., et al., 2004. Determinación del Volumen del Casquetede Hielo del Volcán Cotopaxi. Quito: INAMHI, IRD, IG-EPN,INGEOMINAS, 54 pp.

Cáceres, B., et al., 2006. Climate variability and change – hydro-logical impacts. In: S. Demuth, et al., eds. Climate variabilityand change hydrological impacts. Habana: IAHS Publication,308, 479–482.

Cadier, E., et al., 2007. Variations of a low latitude Andean glacieraccording to global and local climate variations: first results.In: P. Ginot and J.E. Sicart eds. Glacier mass balance changesand meltwater discharge. Foz do Iguaçu: IAHS Publication,318, 66–74.

Callendar, G.S., 1938. The artificial production of carbon dioxideand its influence on temperature. Quarterly Journal of theRoyal Meteorological Society, 64 (275), 223–240.doi:10.1002/qj.49706427503.

Dobhal, D.P., Gergan, J.T., and Thayyen, R.J., 2008. Mass balancestudies of Dokriani Glacier from 1992 to 2000, GarhwalHimalaya. India. Bulletin of Glaciological Research, 25, 9–17.

Espinoza, J.C., et al., 2009. Spatio-temporal rainfall variability in theAmazon Basin countries (Brazil, Peru, Bolivia, Colombia, andEcuador). International Journal of Climatology, 29 (11), 1574–1594. doi:10.1002/joc.1791.

Favier, V., Wagnon, P., and Ribstein, P., 2004b. Glaciers of the outerand inner tropics: a different behaviour but a common responseto climatic forcing. Geophysical Research Letters, 31 (16), 5.doi:10.1029/2004GL020654.

Favier, V., et al., 2004a. One-year measurements of surface heatbudget on the ablation zone of Antizana Glacier 15,Ecuadorian Andes. Journal of Geophysical Research:Atmospheres, 109 (D18105), 15.

Favier, V., et al., 2008. Evidence of groundwater flow on Antizanaice covered volcano, Ecuador. Hydrological Sciences Journal,53 (1), 278–291. doi:10.1623/hysj.53.1.278.

Febres, A., 2007. Análisis tempo-espacial de la cobertura nivo-glaciar del nevado Iliniza Sur. Ingeniería Civil Thesis. Quito:EPN, 257 pp.

Fitzharris, B., Clare, G., and Renwick, J., 2007. Teleconnectionsbetween Andean and New Zealand glaciers. Global andPlanetary Change, 59 (1–4), 159–174. doi:10.1016/j.gloplacha.2006.11.022.

Francou, B., et al., 1995. Balances de Glaciares y Clima en Bolivia yPerú: Impacto de los Eventos ENSO. Bulletin De L’institutFrançais D’etudes Andines, 24 (3), 661–670.

Francou, B., et al., 2000. Glacier evolution in the tropical Andesduring the last decades of the 20th century: Chacaltaya,Bolivia, and Antizana, Ecuador. Ambio: A Journal of theHuman Environment, 29 (7), 416–422.

Francou, B., et al., 2003. Tropical climate change recorded by aglacier in the central Andes during the last decades of thetwentieth century: Chacaltaya, Bolivia, 16°S. Journal of

Geophysical Research: Atmospheres, 108 (D5), 16.doi:10.1029/2002JD002959.

Francou, B., et al., 2004. New evidence for an ENSO impact on low-latitude glaciers: Antizana 15, Andes of Ecuador, 0°28′S.Journal of Geophysical Research: Atmospheres, 109 (D18),17. doi:10.1029/2003JD004484.

Gallaire, R., Taupin, J.-D., and Coudrain, A., 2010. Evolutionrécente des paramètres cryo-climatiques et des teneurs isotopi-ques des précipitations dans les Andes Boliviennes: La Paz etGlacier du Zongo. Hydrological Sciences Journal, 55 (4),467–483. doi:10.1080/02626661003747366.

Garreaud, R., et al., 2009. Present-day South American climate.Palaeogeography, Palaeoclimatology, Palaeoecology, 281(3–4), 180–195. doi:10.1016/j.palaeo.2007.10.032.

Hastenrath, S., 1981. The glaciation of the Ecuadorian Andes. 1st ed.Rotterdam: Taylor & Francis, 159 pp.

Hastenrath, S., 2001. Variations of East African climate during thepast two centuries. Climatic Change, 50 (1–2), 209–217.doi:10.1023/A:1010678111442.

Huss, M., et al., 2010. Glacier mass balance in the south–easternSwiss Alps since 1900 and perspectives for the future.Erdkunde, 64 (2), 119–140. doi:10.3112/erdkunde.2010.02.02.

IPCC (Intergovernmental Panel on Climate Change), 2007.Climate change 2007: synthesis report. R.K. Pachauri andA. Reisinger eds. Contribution of Working Groups I, II andIII to the Fourth Assessment Report of the IntergovernmentalPanel on Climate Change [Core Writing Team]. Geneva:IPCC, 104 pp.

Jomelli, V., et al., 2009. Fluctuations of glaciers in the tropical Andesover the last millennium and palaeoclimatic implications: areview. Palaeogeography, Palaeoclimatology, Palaeoecology,281 (3–4), 269–282. doi:10.1016/j.palaeo.2008.10.033.

Jones, P. and Salmon, M., 2008, Temperature [online]. ClimaticResearch Unit. Available from: http://www.cru.uea.ac.uk/cru/data/temperature/ [Accessed January 2011].

Kaser, G., 1999. A review of the modern fluctuations of tropicalglaciers. Global and Planetary Change, 22 (1–4), 93–103.doi:10.1016/S0921-8181(99)00028-4.

Kaser, G. and Georges, C., 1997. Changes of the equilibrium-linealtitude in the tropical Cordillera Blanca, Peru, 1930–1950, andtheir spatial variations. In: M. Whillans, ed. Annals of glaciol-ogy. Fjaerland: International Gaciological Society, 24,344–349.

Kaser, G., et al., 2003. The impact of glaciers on the runoff and thereconstruction of mass balance history from hydrological datain the tropical Cordillera Blanca, Perú. Journal of Hydrology,282 (1–4), 130–144. doi:10.1016/S0022-1694(03)00259-2.

Lhuissier, P., 2005. Mise en évidence de modèles de ComportementPluviométriques sur le Volcan Antizana. Distribución de laPluviometría en la parte occidental del volcán Antisana aescala mensual, estacional e interanual. Informe provisional.Quito: IRD, INAMHI, EMAAP-Q, 23 pp.

Marshall, S., et al., 2007. Near-surface-temperature lapse rates onthe Prince of Wales Icefield, Ellesmere Island, Canada: impli-cations for regional downscaling of temperature.International Journal of Climatology, 27 (3), 385–398.doi:10.1002/joc.1396.

NWS, 2010, El niño southern oscillation (ENSO) [online]. NationalWeather Service. Available from: http://www.srh.weather.gov/srh/jetstream/tropics/enso.htm [Accessed 2010].

Pouyaud, B., et al., 2005. Avenir des ressources en eau glaciaire de lacordillère. Hydrological Sciences Journal, 50 (6), 999–1022.doi:10.1623/hysj.2005.50.6.999.

Rabatel, A., 2005. Chronologie et interpretation paléoclimatiquesdes fluctuations des glaciers dans les Andes de Bolivie (16ºS)depuis de maximum du petit âge glaciaire (17eme siècle).Thesis (PhD). Université Joseph Fourier, 208 pp.

1534 Carla Manciati et al.

Dow

nloa

ded

by [

DU

T L

ibra

ry]

at 0

6:27

06

Oct

ober

201

4

Rabatel, A., et al., 2013. Current state of glaciers in the tropicalAndes: a multi-century perspective on glacier evolution andclimate change. The Cryosphere, 7, 81–102. doi:10.5194/tc-7-81-2013.

Ramírez, E., et al., 2001. Small glaciers disappearing in the TropicalAndes: a case study in Bolivia: Glaciar Chacaltaya (16°S).Journal of Glaciology, 47 (157), 187–194. doi:10.3189/172756501781832214.

Schmittner, A., et al., 2008. Future changes in climate, ocean circu-lation, ecosystems, and biogeochemical cycling simulated for abusiness-as-usual CO2 emission scenario until year 4000 AD.Global Biogeochemical Cycles, 22 (GB1013), 21 pp.doi:10.1029/2007GB002953.

Sicart, J.E., Hock, R., and Six, D., 2008. Glacier melt, air tempera-ture, and energy balance in different climates: the BolivianTropics, the French Alps, and northern Sweden. Journal ofGeophysical Research: Atmospheres, 113 (D24), 11.doi:10.1029/2008JD010406.

Sicart, J.E., et al., 2003. Etude des précipitations et de la fonte sur unglacier tropical: le glacier du zongo, bolivie, 16°S.Hydrological Sciences Journal, 48 (5), 799–808. doi:10.1623/hysj.48.5.799.51453.

Sicart, J.E., et al., 2007. Glacier mass balance of tropical ZongoGlacier, Bolivia, comparing hydrological and glaciologicalmethods. Global and Planetary Change, 59 (1–4), 27–36.doi:10.1016/j.gloplacha.2006.11.024.

Solomon, S., et al., 2009. Irreversible climate change due to carbondioxide emissions. Proceedings of the National Academy ofSciences of the United States of America, 106 (6), 1704–1709.doi:10.1073/pnas.0812721106.

Soruco, A., 2008. Etude du Retrait des Glaciers depuis Cinquanteans dans les Bassins Hydrologiques alimentant en eau la villede La Paz – Bolivie (16°S). Thesis (PhD). Université JosephFourier (Grenoble 1), 210 pp.

Villacís, M., 2008. Ressources en eau glaciaire dans les Andesd’Equateur en relation avec les variations du climat: Le casdu volcan Antisana. Thesis (PhD). Université de Montpellier IISciences et Technique du Languedoc, 250 pp.

Villacís, M., et al., 2003. Variabilité climatique dans la sierraéquatorienne en relation avec le phénomène ENSO. In: E.Servat, et al. eds. Hydrology of the Mediterranean and semiaridregions. Montpellier: IAHS Publication, 278, 202–208.

Villacís, M., et al., 2010, Relaciones hidrológicas entre el glaciar ylos páramos en los andes tropicales del ecuador: su papel enla disponibilidad de recursos hídricos, IV Simposio

Internacional sobre Cambios Globales: impactos y perspecti-vas. Proceedings of an international symposium held at LaPaz, September. Centro de Investigaciones de CambiosClimáticos, and the Academia Nacional de Ciencias deBolivia, 10 pp.

Vimeux, F., et al., 2009. Climate variability during the last1000 years inferred from Andean Ice Cores: a review ofmethodology and recent results. Palaeogeography,Palaeoclimatology, Palaeoecology, 281 (3–4), 229–241.doi:10.1016/j.palaeo.2008.03.054.

Vuille, M. and Bradley, R.S., 2000. Mean annual temperature trendsand their vertical structure in the Tropical Andes. GeophysicalResearch Letters, 27 (23), 3885–3888. doi:10.1029/2000GL011871.

Vuille, M., Bradley, R.S., and Keimig, F., 2000. Climate variabilityin the Andes of Ecuador and its relation to tropical Pacific andAtlantic sea surface temperature anomalies. Journal of Climate,13 (14), 2520–2535. doi:10.1175/1520-0442(2000)013<2520:CVITAO>2.0.CO;2.

Vuille, M., Kaser, G., and Juen, I., 2008b. Glacier mass balancevariability in the Cordillera Blanca, Peru and its relationshipwith climate and the large-scale circulation. Global andPlanetary Change, 62 (1–2), 14–28. doi:10.1016/j.gloplacha.2007.11.003.

Vuille, M., et al., 2003. 20th century climate change in the TropicalAndes: observations and model results. Climatic Change, 59(1–2), 75–99. doi:10.1023/A:1024406427519.

Vuille, M., et al., 2008a. Climate change and tropical Andean gla-ciers: past, present and future. Earth-Science Reviews,89 (3–4), 79–96. doi:10.1016/j.earscirev.2008.04.002.

Wagnon, P., et al., 1999a. Annual cycle of energy balance of Zongoglacier, Cordillera real, Bolivia. Journal of GeophysicalResearch: Atmospheres, 104 (D4), 3907–3923. doi:10.1029/1998JD200011.

Wagnon, P., et al., 1999b. Energy balance and runoff seasonality of aBolivian glacier. Global and Planetary Change, 22 (1–4),49–58. doi:10.1016/S0921-8181(99)00025-9.

Wagnon, P., et al., 2007. Four years of mass balance onChhota Shigri glacier, Himachal Pradesh, India, a newbenchmark glacier in the western Himalaya. Journalof Glaciology, 53 (183), 603–611. doi:10.3189/002214307784409306.

WMO, 2006. Guide to meteorological instruments and methods ofobservation, 8. 7th ed. Geneva: World MeteorologicalOrganization, 681 pp.

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