Hydrological Research Letters 7(1), 6–11 (2013)
Published online in J-STAGE (www.jstage.jst.go.jp/browse/HRL). doi: 10.3178/HRL.7.6

Projection of glacier mass changes under a high-emission climate scenario
using the global glacier model HYOGA2
Yukiko Hirabayashi1, Yong Zang1, Satoshi Watanabe1, Sujan Koirala1 and Shinjiro Kanae2
1Institute of Engineering Innovation, School of Engineering, the University of Tokyo, Japan
Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology, Japan

2

Abstract:

Meltwater from mountain glaciers and ice caps (MGI)
is an important water resource in arid and semi-arid river
basins mainly fed by mountainous water storage such as
snowpack and ice (IPCC, 2007; Immerzeel et al., 2010;
Kaser et al., 2010). Although measured and model-based
estimations of the meltwater contribution to water resources
vary widely among regions, MGI makes a significant
contribution to streamflow on continental to global scales.
This highlights the potential vulnerability of water resources
under ongoing climate warming and subsequent shrinkage
of MGI in some regions (Immerzeel et al., 2010; Kaser
et al., 2010). Observations show that the present rise in
runoff is a response to glacier melting in some glacier-fed
basins in Alaska, the tropical Andes, and the Alps (IPCC,
2007). As the climate becomes warmer, river discharge will
initially increase due to enhanced melting, until decreases
in glacier volume results in reduced water yields (Braunet al., 2000; Jansson et al., 2003). A numerical future
projection of terrestrial glacier mass change under a warming
climate at selected large Asian rivers suggested that thedisappearance of glaciers would lead to a decrease in riverdischarge. This could cause a loss in food production

affecting as many as 60 million people (Immerzeel et al.,2010).
Early continental- to global-scale models of glacier masschange treated the glacier area distribution as steady (Hocket al., 2009; Kaser et al., 2010). However, modeling theevolution of glacier area and its altitudinal allocation, basedon changes in glacier volume due to changes in atmosphericforcing is indispensable for projecting future glacier masschanges. Because the future change in climate variables
simulated by General Circulation Models (GCMs) varies indifferent regions, especially for precipitation, estimation of
the mass change in individual glaciers with a distributed
glacier model is required to estimate the climate change
impact on water resources in glacier-fed river basins. In
particular, analysis of regime changes (i.e. increased runoff
when glaciers are melting turning to decreased runoff asglaciers begin to disappear) of basin-scale glacier runoff
using a model that can calculate the dynamic evolution of
glacier area is crucial for future water resources assessment.
Using a regionally distributed glacier model, Radić andHock (2011) estimated future sea level rises from theregionally distributed mass change in MGI under climate
projections by several GCMs. They modeled the individual
MGI using seven model parameters obtained from parametertransfer functions calibrated at 36 glaciers located in different
climate and topographic regions. Possibly because of thelack of observed MGI mass changes, however, the modelestimated volume changes were not validated and only future
projections were presented. Marzeion et al. (2012) calculated
the global surface mass balance of glaciers in the past andfuture based on a complete glacier inventory, the Randorf
Glacier Inventory (RGI), and monthly precipitation andtemperature. For integration into internationally significant
reports such as the assessment report of the Intergovernmental Panel on Climate Change (IPCC), it is necessary toassign uncertainty ranges to model-simulated future
projections of glacier volume by carrying out additional
numerical simulations using independent models.
A simple temperature-index-based global glacier model,
HYOGA, was developed to simulate the current and future
mass changes in MGI and was validated globally(Hirabayashi et al., 2010). Compared to available observations, the HYOGA simulation, calculating the masschanges of MGIs aggregated into 0.5° model grid cell at
50 m vertical resolution, reproduced reasonably well thedecadal glacier mass changes and the recent (after the 1990s)
acceleration of glacier mass loss. Hirabayashi et al. (2012)
updated the model to estimate the mass balances ofCorrespondence to: Yukiko Hirabayashi, Institute of Engineering
Innovation, School of Engineering, the University of Tokyo, 2-11-16 Yayoi,
Bunkyo-ku, Tokyo 113-8656, Japan. E-mail: hyukiko@sogo.t.u-tokyo.ac.jp©2013, Japan Society of Hydrology and Water Resources.

Received 31 July, 2012
Accepted 27 February, 2013

We report a time series (1948–2100) of global-scale
meltwater from mountain glaciers and ice caps (MGI)
estimated by the global glacier model HYOGA2. HYOGA2
calculates the temporal fluctuation of the mass balance for
24,234 individual glaciers worldwide. It covers 90% of thetotal glacier area, except for glaciers in Greenland andAntarctica. HYOGA2 also accounts for regionally distributed changes in glacier area and altitude associated withglacier retreat and advance. By computation of individualglacier changes, future dissipation and glacier mass and areachanges can be simulated in the model. The cumulative
volume loss of water between 1948 and 2005 was estimatedto be 25.9 ± 1.4 mm sea level equivalent (SLE). A future
projection under a high-emission scenario demonstrated
significant losses of water from MGI equivalent to 60.3 ±7.9 mm SLE between 1948 and 2060 and 99.0 ± 14.9 mm
SLE between 1948 and 2099.
KEYWORDS glacier; mass change; climate change; sealevel rise

INTRODUCTION—6—GLOBAL GLACIER PROJECTION BY HYOGA2
individual MGI by including a glacier inventory. They also
validated the model performance at glaciers across Europe.
The present study describes an updated version of the model,
HYOGA2, which utilizes an extended global-scale glacierinventory, the RGI. Future changes in glacier volume arethen projected using HYOGA2 forced by the climate
projections of nine independent GCMs.

MODEL
Global glacier model HYOGA2HYOGA2 is an updated version of the global glaciermodel HYOGA (Hirabayashi et al., 2010). The first versionof HYOGA2 was validated over glaciers in Europe(Hirabayashi et al., 2012). The current study extends theresults of Hirabayashi et al. (2012) to a global scale,including the complete glacier inventory of the RGI andusing globally distributed parameters obtained through a
global calibration by a method similar to that suggested byHirabayashi et al. (2010). In this section, the basic structure
and updated components of the HYOGA2 model are briefly
described.
The basic model structure of HYOGA2 is the same asthat of HYOGA. The melt rates on snow and ice surfaces
at each elevation band are calculated using a simple degreeday approach and then aggregated to estimate the massbalance for each MGI. The precipitation and surface
temperature at a daily temporal resolution are necessary
inputs. The subgrid-scale variation in glacier and snow masschanges is considered by dividing a model grid cell into
50 m elevation bands. The surface air temperature is assumed
to decrease at a constant lapse rate of −0.65°C (100 m)−1.
Precipitation is assumed to be constant for all elevation
bands in each grid cell because an approach suitable fordetermining precipitation lapse rates on a global scale islacking. Although precipitation lapse rates may affect themodel-estimated mass balance in a warming climate, we did
not use it to avoid the incorporation of an additional uncertain
parameter. Nonetheless, we note that the incorporation ofadequate precipitation lapse rates is an important subject of
future research. A detail discussion is given in Supplement
Information S1. Further studies are expected to examine theeffect of precipitation lapse rates in a future warming climate
and to set it adequately on a global scale.
The glacier volume was obtained using the equation
A =(V/ca)1/γ
2

(1)
3

where A (km ) and V (km ) are, respectively, the total glacierarea and volume in each glacier, and ca and γ are model
parameters. For all glaciers, the model assumed fixedparameter values: γ = 1.375 from Bahr et al. (1997) and ca= 0.2055 (m3−2γ) from Chen and Ohmura (1990).
The original model (HYOGA) assumed a single ice massin each model grid cell, with the area determined from thecorresponding total glacier area in the inventory. However,the new HYOGA2 independently calculates the massbalances of each MGI using information from the existingglacier inventory. In addition, differently from HYOGA,HYOGA2 updates the maximum glacier length L after eachtime step based on the new total glacier volume. This isdefined following the method of Radić and Hock (2010) as

V = ciLq(2)3−2γ

where ci (= 1.7026 m ) and q (derived from A and L fromglacier inventory) are fixed model parameters.
Because of the nonlinear relationship between A and V
in Equation (1), HYOGA, which considers a single ice mass
with large area, potentially overestimated the total volumeof the glaciers. HYOGA2 overcomes this deficiency byseparately simulating individual glaciers with relatively
smaller areas. As a result, the potential overestimation of
the volume of each small glacier is relatively low. More
importantly, with the inclusion of independent glacier
volume estimations from the completed glacier inventory,
the new HYOGA2 can estimate glacier dissipation underwarming climate scenarios globally.
In HYOGA2, mass changes are calculated for all theglaciers, excluding those in Greenland and Antarctica larger
than 2 km2, to reduce the calculation cost. MGI with areas< 2 km2 (LT2) are then calculated by an upscaling method.
The LT2s, corresponding to about 10% of the total glaciersby area, are important because they are mostly located atlower altitudes, where the glacier mass change is the mostsensitive to climate change (Oerlemans and Fortuin, 1992).
First, the LT2s within a 0.5° grid cell were ordered according
to area. Then, the median LT2 was selected as arepresentative LT2 for the grid. The LT2s smaller than
0.01 km2, which were normally distributed within a few
modeled elevation bands of 50 m resolution, were not taken
into consideration for the selection, since their inclusion
resulted in model instability because of the small numberof elevation bands. The mass change per unit area of arepresentative LT2 was calculated and then multiplied by
the total LT2 area to obtain the total mass change of all
LT2s for each 0.5° grid cell. This process assumed that all
small LT2s located in the same 0.5° grid showed the same
mass changes. Evaluation of the impact of standard LT2
selection on large-scale mass changes over glaciers in Europe
showed that the upscaling method used in this study resulted
in very similar (< 0.01%) total ice mass changes compared
to methods where all small individual glaciers are calculated(Hirabayashi et al., 2012).
There were two calibration parameters in HYOGA2:
degree-day factors (DDFs) for ice and for snow. Other
calibration parameters required in HYOGA were obtained
from the RGI. To obtain globally calibrated DDFs, we first
aggregated the glacier information from the RGI into global
0.5° grid cells. Then, the globally distributed DDFs wereobtained using a method suggested by Hirabayashi et al.
(2010) at each 0.5° grid cell. Detailed descriptions of themodel parameter calibration and initialization are presented
in the Supplement Information S2.

DATAGlacier inventory
The glacier inventory used in this study was version 2.0of the Randolph Glacier Inventory (RGI, http://
www.glims.org/RGI/randolph.html) processed by Marzeion
et al. (2012). The location (longitude and latitude), maximum
and minimum altitude, and glacier area could be obtained
from the inventory. We calculated glaciers with complete—7—

Y. HIRABAYASHI ET AL.
data for location and area. In total, 257,325 glaciers with a
total area of 515,706 km2 were selected from the RGI (Figure1). This represents an improvement compared to the original
HYOGA model, which used a 0.5° gridded global glacierarea dataset created by aggregating the glacier data of theolder version of the World Glacier Inventory (WGI) and the
Global Hydrological Data (GGHYDRO; Cogley, 2003). For
details of the glacier inventories and adjustments necessary
for their application in the HYOGA2 model simulations,see the Supplement Information S3.

Climate input
The retrospective simulation for a 64-year period (1948to 2011 inclusive) of HYOGA2 was forced by an extended
version of the observation-based global 0.5° gridded datasetof daily precipitation and near-surface temperature (H08;
Hirabayashi et al., 2005, 2008a) (details of the extension
are given in Supplement Information S4). The future
simulation (from 2006 to 2100) was forced by nine GCMs

(Supplement Table SI) participating in the fifth phase of the
Coupled Model Intercomparison Project (CMIP5), forcedby the highest emission scenario (representative concentration pathway (RCP) 8.5). The extension of H08 up to 2011
and the bias correction of GCM data for 2006–2100,
following the method of Watanabe et al. (2012), are briefly
introduced in the Supplement Information S4 and S5.

RESULTS
The area-weighted observation-based annual massbalances and old and new model results at regional to global
scale are presented in Figure 2. For the global mean, both
simulations show a global acceleration of negative massbalance after the 1990s, as also reported by Dyurgerov andMeier (2005) (DM05). It should be noted, however, that theregional estimation of DM05 with a limited number of massbalance observations does not necessarily reflect the real

Figure 1. Global distribution of glaciers included in the RGI. The color indicates the number of glaciers within a 0.5° grid cell.

Figure 2. Area-weighted average of annual mass changes of global glaciers. NA: North America, SA: South America, and
EU: Central Europe and Scandinavia. The regions are marked as rectangles in Figure 1. Estimations based on the available
mass balance observations by Dyurgerov and Meier (2005) (DM05) are also shown for reference. The thick lines are the5-year moving averages of annual values presented as thin lines.
—8—

GLOBAL GLACIER PROJECTION BY HYOGA2
mass balance changes over the region. Even with similar
model parameters, a significant difference between the oldand new model may be due to the inclusion of additional
glaciers that were missing in the old model. In North America(NA), many glaciers in arctic Canada were newly includedin HYOGA2. Because of the small number of observations
there, however, the calibration of DDFs was performed byassuming that the glaciers were in balance between 1948
and 1980. This caused high (closer to 0) mass balances in
the model simulations because of the inclusion of moreglacier information in these arctic regions (e.g., the arctic
Canada in NA). In South America (SA), the overall timeseries did not match with the observation-based estimation.
This was mainly due to the limited number of mass balance
observations, in addition to the relatively lower quality of
the climate input data.
The mean and standard deviation of correlation
coefficients between the modeled and observed mass balanceamong available observation stations showed that HYOGA2
with globally calibrated DDFs had a similar performance to
HYOGA (Supplement Figure S1). The root mean-squareerror between the results of the retrospective simulation with
HYOGA2 and the available observed mass balance was
915 mm. This was close to the range of the error estimation
for a global glacier model reported by Marzeion et al. (2012).
Comparison of modeled and observation-based areaweighted regional averages of mass changes by Dyurgerov
Table I. Future projection of the average volume loss of
MGI since 1948 (103 km3) and its sea level equivalent (mm).
The value for 1948–2005 is the result of retrospective
simulations with root mean-square errors of available
observations multiplied by the total glacier area, excluding
Greenland and Antarctica (5.17 × 105 km2). The values infuture periods are means and standard deviations among the
GCMs. The ocean surface area is assumed to be 362 ×
106 km2.
Period since 1948

Volume loss since1948 (103 km3)

Sea level equivalent
(mm)

–2005
–2060
–2099

9.4 ± 0.5
21.8 ± 2.9
35.8 ± 5.4

25.9 ± 1.4
60.3 ± 7.9
99.0 ± 14.9

and Meier (2005) revealed that decadal trends of modeled
mass changes on regional scales were similar between theold and new models, but the long-term mean of annual massbalances was different. This may have been due to theinclusion of new inventory. Nonetheless, the global averagesof the two models were similar (Figure 2).
Even though the results of the retrospective simulations
were similar to those of old models, HYOGA2 isadvantageous for estimating future volume change because
it distinguishes individual glaciers. In particular, HYOGA2
can estimate the retreat and dissipation of glaciers under awarming climate more adequately than the original HYOGA
model. This is important for analyses of regime changes(i.e., rise in runoff turns to fall in runoff as glaciers diminish)of basin-scale glacier runoff for projecting future water
resources.
A future projection of cumulative glacier mass loss andits contribution to future sea level rise based on HYOGA2
is summarized in Table I and Figure 3. A retrospective
HYOGA2 simulation calculated that the global total volume
loss from glaciers for 1948–2005 was 9.4 ± 0.5 × 103 km3,
corresponding to a 25.9 ± 1.4 mm sea level equivalent (SLE).
According to the HYOGA2 simulation forced by the outputsof nine GCMs under the most severe warming scenario(RCP 8.5), the cumulative volume loss from global glaciers
becomes 21.8 ± 2.9 × 103 km3 by 2060 and 35.8 ± 5.4 ×
103 km3 by 2099. Consequently, the total sea level rise since1948 due to meltwater from MGI was estimated to be 60.3
± 7.9 mm by 2060 and 99.0 ± 14.9 mm by the end of the
21st century. These estimations are relatively smaller thanthat of Marzeion et al. (2012), who showed 217 ± 47 mm
up to 2100 with 15 GCMs forced by the RCP8.5 scenario.
The time series of modeled mass balance showed an
obvious acceleration of the negative trend since around 2000.
This trend continued until the end of the simulation (Figure3a), as a result of the assumed warming climate in the future
projection. The cumulative volume loss from glaciers
therefore increased rapidly, especially after the middle ofthe 21st century (Figure 3b).

DISCUSSION
Degree-day factors for ice and snow may show

Figure 3. Same as Figure 2 but for up to 2099 forced with the CMIP5 GCM simulations under the RCP 8.5 climate scenario(a) and cumulative global mass balance of MGI (103 km3) and its sea level equivalent (mm) computed in this study (b).
The ocean surface area is assumed to be 362 × 106 km2. Results based on retrospective simulations for 1948–2011 (black
thick line), individual GCM simulations (colored thin lines) and their ensemble means (gray thick line) are shown.
—9—Y. HIRABAYASHI ET AL.
significant spatial variability (Zhang et al., 2006), greatly
affecting the accuracy of snow- and ice-melt models. As in
the old HYOGA model, DDFs were calibrated until they
reproduced similar values for the grid-cell-specific longterm average of total glacier mass balance observed inneighboring grid cells (Hirabayashi et al., 2010). Because
the DDFs were the only calibration parameters of HYOGA2,
the calibrated DDFs might have compensated for otherpotential sources of errors in the model simulations. For
example, because of the limitations of the global climate
input data, the model simulation might not be accurate at
glaciers located in poorly gauged regions, such as High
Mountain Asia. Glacier characteristics, which were notadequately represented in the model (e.g., slope aspects andeffects of debris cover), were also potential error sourcesthat might have partly been compensated for in the DDF
calibration.
In addition, debris-covered glaciers are common in many
mountain ranges around the world. In these glaciers, thedebris cover in the ablation zones accelerates (if the debris
is thin) or suppresses (if it is thick) the melting of sub-debris
ice relative to that of other snow and ice (Nakawo and Young
1981, Mattson et al. 1993). This then affects the DDFs
(Zhang et al., 2006). However, the effect of the debris coveron DDFs could not be considered in HYOGA2 and otherglobal glacier models (Radić and Hock, 2011; Marzeion
et al., 2012) because of the poor understanding of the largescale spatial distribution of debris thickness and properties.
One potential future method to obtain these debris-related
properties is to use remote sensing data to relate the debris
thickness and thermal resistance of the debris layer (Zhang
et al., 2011).
Improper model initialization is also known to cause
biases in model simulation. Because we assumed that theglacier area was provided in the RGI as an initial area in
1948, the glacier area may often be underestimated for
glaciers where melting was already obvious in the 20th
century, if RGI data were recorded at the end of the 20th
century. With respect to future projection in the 21st century(Figure 3), we assume that this effect is small compared tothe computed future mass loss. The upscaling method for
LT2s, which assumes the same mass changes, may also
cause potential errors.
Finally, in addition to glacier model-related sources oferrors, other potential sources include the model input dataand processing. Future GCM simulations that are used toforce glacier models have significant biases and require
correction. Future studies should therefore also focus on
assigning uncertainty ranges to model simulations becauseof uncertainties associated with the selection of bias
correction methods for GCM outputs.

SUMMARY
A global glacier model, HYOGA2, which includes acompleted individual glacier inventory and uses globally
distributed parameters, was developed and used to estimate
the global glacier mass changes in the past and future. The
performance of the retrospective HYOGA2 simulation was
similar to that of HYOGA, showing reasonably accurate
temporal variations compared to observations at both global

and regional scales. Because of the inclusion of the complete
global-scale glacier inventory, the updated model could
represent large-scale glacier mass losses in the future better
than the old model. Dissipation of small glaciers under awarming climate is one of the key factors to consider in the
projection of future changes in glacier meltwater volume.
By calculating the mass changes in individual glaciers,
HYOGA2 can simulate the timing of MGI dissipation andthe associated shortage in water resources under futureclimate scenarios. Future model simulations using nine
GCMs forced by the highest emission scenario (RCP 8.5)
showed significant decreases in water from MGI, with losses
amounting to 60.3 ± 7.9 mm SLE between 1948 and 2060
and 99.0 ± 14.9 mm SLE between 1948 and 2099.

ACKNOWLEDGMENTS
This research was supported by the Funding Programfor next-generation world-leading researchers, the Japan
Society for the Promotion of Science, and the CREST projectof the Japan Science and Technology Agency.

SUPPLEMENTS
Detail explanations of model sensitivity to precipitation
lapse rate (Supplement Information S1), model calibrationand initialization (Supplement Information S2), glacierinventory (Supplement Information S3), extension ofobservation-based climate data (Supplement Information
S4), bias correction of the GCM data (Supplement Information S5) and model validation (Supplement Information
S6 and Supplement Figure S1) are given in the supplementary materials. Summary of the GCMs used in this study
is given in the Supplement Table SI.

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