Initialisation of the SEB/MB model for application at Halji glacier

So far, Halji glacier has not been subject to any glaciological observations. Therefore, information on ELA or snow distribution for initial model assumptions is lacking. Two snow depth measurements in the north western region of the glacier in December 2013 at an altitude of ≈5300 m a.s.l. reveal a snow pack between 40 and 50 cm and lowermost layers with densities in the range of firn (N. Neckel

& B. Schröter, personal communication). Based on this knowledge, we assume an initial snow depth that slightly increases linearly from 0 cm in 5300 m a.s.l. to 12 cm in the uppermost regions.

To account for uncertainties in total precipitation amounts (section 5.2) and to obtain a model uncer-tainty for further MB calculations, we perform three model runs with varying precipitation scaling factors (0.56±0.25). The model run with a scaling factor of 0.56 is called reference run. Furthermore, HAR precipitation amounts are decreased by 25% (scaling factor 0.31) and increased by 25% (scaling factor 0.81) relative to the reference run. The lower factor (0.31) leads to a mean annual precipita-tion total of 335 mm, applying the higher factor (0.81) results in precipitaprecipita-tion sums of 875 mm yr^-1.

5.4 Results and discussion

In this section the results of the 11-year simulation period of Halji glacier are interpreted. The SEB/

MB model explicitly calculates the different SEB and MB components and allows revealing altitudinal, inter- and intra-annual patterns on the glacier (section 5.4.1). In a further step, the typical annual cycle of the modelled transient snow line altitude and the inter-annual pattern of the ELA are linked to the results of the SEB and MB modelling (section 5.4.2).

5.4.1 SEB/MB characteristics 2000-2011

The altitudinal dependency of MB and SEB components averaged over the simulation period 2000-2011 is shown in Fig. 5.4. The given uncertainty ranges result from the model runs with varying preci-pitation scaling factors (see section 5.3). Precipreci-pitation amounts are both decreased and increased by 25% compared to the reference run. The reference run is characterized by a precipitation scaling fac-tor of 0.56. The gradient of calculated MB shows a distinct steepening around 5350 m a.s.l., slightly below the 11-year ELA (Fig. 5.4a). This pattern is similar to the one modelled for Zhadang glacier (see section 2.4.6) and was also observed at Morteratschgletscher by Klok & Oerlemans (2002). The vertical variation of the MB profile is smaller than at Zhadang glacier and varies largely with decrea-sing precipitation amounts. The application of a scaling factor of 0.31 on HAR precipitation results in a strong decrease in MB especially in the lower glacier areas and increases the vertical variation (Fig.

5.4a). Studies of Mölg et al. (2009) at Kilimanjaro found a similar effect. A decrease (increase) in pre-cipitation over all altitudes (Fig. 5.4c) causes more (less) surface melt mainly at the lower elevations (Fig. 5.4a). At Halji glacier, precipitation amounts with the same scaling factor of 0.56 are higher than at Zhadang glacier (see section 1.4.1) resulting in stronger melt in the lower regions and an increased vertical variation of MB at the latter (Fig. 2.23).

The shape of the vertical MB profile is also apparent in the gradients of SWnet (Fig. 5.4d), Qmelt (Fig.

5.4e), surface melt (Fig. 5.4a) and α (Fig. 5.4g). Qmelt has the same shape as surface melt because the two parameters are directly connected through LM (see section 2.3.1). In the reference run (precipita-tion scaling factor 0.56) the vertical varia(precipita-tion of α is only weak, but increases strongly with decreasing precipitation by 25% (Fig. 5.4g). Values for α decrease from 0.78 to 0.56 in the lower glacier regions.

This effect also determines the altitude dependent variation of SWnet, Ts (expressed through LWout), Qsens, Qlat (sublimation), Qmelt (surface melt) and MB. Similar dependencies are observed by Braun &

Hock (2004) on King George Island (Antarctica) and by van Pelt et al. (2012) on Svalbard. Less solid precipitation (Fig. 5.4c) increases surface melt through decreasing α (see also Klok et al. 2005) es-pecially in the lower regions where Tair is higher. This in turn causes SWnet (Fig. 5.4d) and TS to in-crease (Fig. 5.4f). Increasing Ts yields to smaller Qsens directed to the glacier surface and a higher Qlat

from the glacier surface to the atmosphere (Fig. 5.4e). These feedback processes are also observed by Klok & Oerlemans (2002) at Morteratschgletscher. Larger negative Qlat results in increased mass loss through sublimation in the lower glacier regions (Fig. 5.4b). These patterns and feedback mecha-nisms reveal that a precipitation decrease by 25% compared to the reference run has numerous effects on all SEB and MB components. The combination of decreasing accumulation and increasing mass loss in the lower glacier regions both, from increased surface melt, decreased refreezing and in-creased sublimation lead to a high sensitivity of glacier MB to a 25% decrease in precipitation at Halji glacier.

SWin and LWin are the only ground-independent energy sources for the glacier surface (see section 2.3.1) and do not change with varying precipitation amounts (Fig. 5.4d) because energy fluxes from the surrounding terrain are not considered. SWin is nearly constant with altitude between 5350 and 5550 m a.s.l. and from 5650 m a.s.l. upwards due to minimal terrain shading (Fig. 5.1). It decreases slightly in the lowest regions up to 5350 m a.s.l. The strong decrease in SWin between 5550 and 5650 m a.s.l. can be explained by the steepening of the north facing slope south of the glacier (Fig.

5.1). This sharp decrease in the upper regions is also visible in the vertical patterns of Qsens, Qlat, subli-mation, LWout, Qmelt, surface melt and MB. The direct effect of SWin on MB is not that strong as re-vealed by Mölg et al. (2009) for glaciers at Kilimanjaro. A reason for this might be the relatively cons-tant high value of α in the upper regions of Halji glacier. Thus, large amounts of SWin are directly re-flected.

In the reference run refreezing is largest around 5350 m a.s.l. (Fig. 5.4c) where decent amounts of surface melt and a thick snow pack are present. In the reference run, the annual mean proportion of refreezing reached 50% in the upper glacier regions and 13% in the lowermost altitude band. Respec-tive values for a 25% precipitation decrease (increase) are 46% (51%) in the upper regions and 0.2%

(53%) in the lowermost part. A precipitation decrease by 25% from the reference run raises the alti-tude of maximum refreezing to 5550 m a.s.l., whereas a precipitation increase by 25% yields to a lar-ger snow pack in the lower glacier regions and therefore significantly larlar-ger amounts of refreezing.

For all other SEB and MB components, increasing precipitation by 25% has a much smaller effect than a precipitation decrease by 25% compared to the reference run. This is also evident for ELA variations. The ELA in the reference run is estimated between 5400 and 5450 m a.s.l. (Fig. 5.4a). The altitude coincides with the large-scale ELA for the region of the western Himalayas given by Yao et al.

(2012). A decrease (increase) of the precipitation amount by 25% rises (lowers) the ELA by ≈200 m (≈100 m). Concerning the glacier hypsometry (Fig. 1.12) an ELA increase would have important con-sequences for the glacier MB because the largest area proportion is located in these altitude bands.

Annual cycles of glacier-wide mean monthly SEB and MB components at Halji glacier as calculated by the MB model for the reference run for the period 2000-2011 are illustrated in Fig. 5.5. It is obvious that the values calculated within the first months (blue box in Fig. 5.5) suffer from the errors that stem from the spin-up time of the MB model (see section 2.3.1). The first MB year is therefore not considered in the following interpretations. In general, SWin (+240.8 W m^-2) and LWin (+209.3 W m^-2) dominate energy input over the considered period, followed by Qsens (+19.6 W m^-2). Energy sinks at the glacier surface are LWout (-254.4 W m^-2), SWout (-196.8 W m^-2), Qlat (-16.7 W m^-2), Qmelt (-5.1 W m^-2) and QG (-1.4 W m^-2), making SWnet (+48.7 W m^-2, see Table 5.1) the most important energy source, highly depending on α. This was also observed at Zhadang glacier, PIC and Naimona’nyi glacier. The lowest values of SWnet are evident between June and September when large precipitation amounts increase α (Fig. 1.10, 5.5a). LWnet shows a rather regular seasonal cycle playing a smaller role as ener-gy sink in summer than in winter (Fig. 5.5a). This can be explained through the seasonal patterns of LWin, depending on Tair, e and N (see section 2.3.1) (higher in summer), and LWout, depending on Ts

(largest negative values in summer). In winter, LWin is significantly smaller whereas LWout varies only little. Averaged over the whole period QC is very small and naturally tends to zero. In winter, when the surface is colder than the underlying snow layers, QC becomes positive. Slightly negative QC is evident in spring (Fig. 5.5a), when the surface warms but subsurface layers are still cold from the winter season. QPS is always negative with larger values when α is low (SWnet is large). Generally, Qlat

is an energy sink for the glacier surface (Table 5.1). However, on a monthly scale it is evident that slightly positive values can occur in July and August when the region is under the influence of ma-ximum precipitation amounts in the course of the year, highest Tair and RH (Fig. 1.12, 5.2). A similar pattern of positive Qlat during the summer monsoon period is observed at Chhota Shigri glacier in the western Himalayas based on SEB measurements by Azam et al. (2014) and at Naimona’nyi glacier as reported in chapter 4. During most of the year, the dry conditions on the TP lead to negative Qlat and significant sublimation (Fig. 5.5) with largest values in winter when RH is smallest (Fig. 5.2). Also, higher wind speeds drive turbulence during winter (Fig. 1.13). Generally, monthly means of Qsens and Qlat are of opposite sign and mostly cancel each other out. Absolute values of Qsens are slightly larger than Qlat implying a higher moisture content of the atmosphere and less sublimation compared to Zhadang glacier (section 2.4.6). A similar dependency is observed by Nicholson et al. (2013) at Lewis glacier (Mt Kenya). During the first year of simulation, precipitation amounts are comparatively small and cause highest values of SWnet, Qmelt and surface melt in September to December 2001 (Fig. 5.5a, 5.5b). In the following years surface melt occurs dominantly in June-September, simultaneously with the period of lowest SWnet through high α. The climate chart for Halji glacier confirms that both air

temperature and precipitation maxima coincide between June and September (Fig. 1.12). The res-pective SEB values for the model runs with varying precipitation scaling factors are given in Table 5.1.

For glacier-wide monthly SEB components of the MB model runs with ±25% of precipitation see Fig.

A.5a and Fig. A.6a in the appendix.

Fig. 5.4: Modelled vertical profiles of (left) the specific mass balance and its components and (right) mean SEB components on Halji glacier averaged over the simulation period October 2000 – September 2011.

Data are averaged for 50-m altitude bands. The uncertainty ranges are calculated assuming different precipitation amounts (see section 5.3). Note the different x-axis scales of the components (negative means mass or energy loss at the glacier surface, see section 2.3.1 and Table. 2.2 for abbreviations).

The glacier-wide MB estimate of the reference run for the period 2001-2011 (the first year is not con-sidered) is +206 kg m^-2 (+20.6 kg m^-2 yr^-1) (Table 5.2). In general, sublimation (-1998 kg m^-2/ -199.8 kg m^-2 yr^-1) is the second largest factor of glacier-wide mass loss after surface melt (-4831 kg m^-2/-483.1 kg m^-2 yr^-1) but clearly dominates ablation in winter, when air temperatures are below 0°C and surface melt is absent (Fig. 1.12, 5.5b). Subsurface melt (-36 kg m^-2/-3.6 kg m^-2 yr^-1) plays a minor role. Solid precipitation (+4957 kg m^-2/+495.7 kg m^-2 yr^-1) and refreezing (+2714 kg m^-2/ +271.4 kg m^-2 yr^-1) contribute to the mass gain of the glacier. Maximum snowfall amounts occur in summer (Fig. 1.12, 5.5b, see section 1.4.1). Refreezing at Halji glacier happens mostly in summer, when melt water is produced at the surface and percolates through the cold winter and spring snow layers. Air temperatures at Halji glacier are considerably below 0°C until June (Fig. 1.12). Therefore, it can be assumed that subsurface temperatures are well below the melting point throughout the

summer and allow the refreezing of considerable amounts of melt water. The reference run implies that 56% of the surface and subsurface melt water refreezes within the snow pack between 2001 and 2011. These results will be discussed in section 5.5. The respective MB values for the model runs with varying precipitation scaling factors are given in Table 5.2. For glacier-wide monthly MB compo-nents of the MB model runs with ±25% of precipitation see Fig. A.5b and Fig. A.6b in the appendix.

Fig. 5.5: Glacier-wide monthly (a) SEB components (see section 2.3.1 for abbreviations) and (b) MB compo-nents from October 2000 to September 2011 at Halji glacier (reference run). Note that the first months (blue box) should not be considered.

Table 5.1 lists the absolute and relative contributions of energy flux components to the total energy turnover for the three model runs with varying precipitation scaling factors (0.56/0.31/0.81). For the considered period 2001-2011, SWnet accounted for 37% (43%/36%), followed by LWnet (34%/30%/

34%), Qsens (15%/12%/15%), Qlat (11%) and QG (3%/4%/4%). The relative contribution of SWnet and LWnet, as well as of Qsens and Qlat to the total energy flux is nearly balanced for the reference run. A similar effect was observed by Braun & Hock (2004) on King George Island (Antarctica). The influence of QG is only minor. Therefore, simulated Qmelt is generally low at Halji glacier over the period 2001-2011 with a minimum in 2003/04 (Table 5.1). Low Qmelt results in low surface melt compared to Zha-dang glacier and PIC with an overall positive MB (Table 5.2). As simulated surface melt rates at Halji glacier are generally low, variations in mass loss through sublimation and in mass gain through solid precipitation and refreezing play an important role for total annual MB (Table 5.2). Over the total simulation period, effective melt (surface melt + subsurface melt – refreezing) accounts for 52%

(82%/34%) and sublimation for 48% (18%/66%) of the total mass loss (Table 5.2).

Table 5.1: Mean absolute values of energy flux components as modelled for October 2000 – September 2011 and for the respective MB years with proportional contribution to total energy flux at Halji glacier. The first value is for a precipitation scaling factor of 0.56, the numbers in brackets are for the model runs for a scaling factor for precipitation of 0.31 and 0.81. Note that values of the MB year 2000/01 are not considered in the calculation of the total average.

Table 5.2: Calculated glacier-wide mass balance components for the total period October 2000 – September 2011 and for the respective MB years at Halji glacier. The first value is for a precipitation scaling factor of 0.56, the numbers in brackets are for the model runs with lower (higher) precipitation scaling fac-tors (0.31 / 0.81). Note that values of the MB year 2000/01 are not considered in the calculation of the total values.

Solid precipitation Surface melt Refrozen water Subsurf. melt Sublimation Mass balance*

tot. [kg m^-2] 4957 (2744 / 7170) -4831 (-10875 / -4175) 2714 (775 / 3181) -36 (-41 / -36) -1998 (-2211/-1970) 206 (-9830 / 3491)

*Mass balance = solid precipitation + surface melt + refrozen water + subsurface melt + sublimation

The importance of the various SEB and MB components to surface melt and MB differs largely within the three model runs with varying precipitation scaling factors. Firstly, with decreasing precipitation compared to the reference run (scaling factor 0.31) the relative contribution of SWnet to the total energy flux increases through decreasing α. The differences in SWnet between the two model runs (0.56/0.31) are largest in 2001/02, 2003/04, 2006/07 and 2008/09-2010/11. Annual and total aver-ages of LWnet, Qsens, Qlat and QG do not experience large variations through a 25% precipitation de-crease. However, they follow the pattern of SWnet with larger variations in the respective years (Table 5.1) because the decrease in α directly affects Ts and therefore all SEB components (see sections 1.3.4.1 and 2.3.1). Especially the negative increase in QG is relatively large meaning that more energy

from the surface is transferred into the snow or ice and warms the subsurface. Altogether, this re-sults in a strong negative increase of Qmelt and surface melt and an above average decrease of MB in the respective years compared to the 10-year mean (Table 5.1, 5.2, Fig. 5.6). In the model run with decreased precipitation relative to the reference run (scaling factor 0.31) MB is closer linked to sur-face melt, because the contribution of effective melt to total mass loss increased to 82% (Table. 5.2).

The six years in which the MB is most sensitive to a 25% precipitation decrease are characterized by higher Qmelt already in the reference run and/or low annual precipitation (Table 5.1, 5.2). Thus, the tipping point to further increased mass loss is closer than in the other MB years. A precipitation in-crease relative to the reference run (scaling factor 0.81) causes overall positive MB at Halji glacier (Fig. 5.6) but the differences in the SEB and MB components to the reference run are less distinct (Table 5.1, 5.2). A possible reason might be that the reference run already simulates an overall po-sitive MB and relatively high α in the lower glacier regions (Fig. 5.4g) through periodical strong snow fall events. Thus, any further precipitation increase can not have large impacts on these variables.

Simulated annual mean MB at Halji glacier can hardly be evaluated because no in-situ measurements are available so far and the glacier area is too small for intensive remote sensing analyses (see sec-tion 5.5). Kropacek et al. (2013) report a periodic GLOF event since 2006 (see secsec-tion 5.1). The water is released from a supra-glacial lake on Halji glacier. To reveal possible consistencies between glacial runoff generated by the MB model and the occurrence of ‘GLOF years’ and ‘non-GLOF years’, the annual amount of water supply from the glacier is calculated (Fig. 5.6). Runoff (in kg m^-2) is deter-mined from surface and subsurface melt, refreezing and liquid precipitation for the total glacier area.

The effect of routing, the time delay of the melt water movement vertically through the snowpack and horizontally along the glacier surface (Singh & Singh 2001), is not considered. The actual drainage basin of the supra-glacial lake might be smaller than the total glacier area. This can be neglected because it does not affect the quantitative comparison. Between 2001 and 2011 four ‘non-GLOF years’ were observed (Fig. 5.6). In 2002/03 and 2004/05 simulated runoff is small when including the ranges of uncertainties. In these years no GLOF occurred. This might be an indication that the amount of runoff is possibly linked to a GLOF event. However, simulated runoff in the years 2005/06, 2007/08, 2009/10 and 2010/11 is similar but 2009/10 is the only ‘non-GLOF year’ of those.

In 2001/02 no GLOF event is evident despite high runoff (Fig. 5.6). Thus, the link between simulated runoff and GLOF events at Halji glacier is limited. Kropacek et al. (2013) assume external factors, e.g.

sediment damming of the lake, to override the meteorological forcing. Furthermore, the lake geometry and the temporal melt pattern may influence the lake outburst.

Mean modelled MB between 2001 and 2011 for Halji glacier with varying precipitation scaling factors (0.56/0.31/0.81) is +21 kg m^-2 yr^-1 (-983 kg m^-2 yr^-1/+349 kg m^-2 yr^-1). Considering the published results of less detailed studies for glaciers in the Himalayas (e.g. Gardelle et al. 2013a,b, Kääb et al. 2012, Bolch et al. 2011), the obtained MB for the reference run appears to be too positive. The mean MB for the Pamir/Karakorum/Himalaya region was estimated to be -140±80 kg m^-2 yr^-1 for 1999-2011 (Gardelle et al. 2013) and -210±50 kg m^-2 yr^-1 for the Hindukush/Karakorum/Himalaya region for 2003-2008 (Kääb et al. 2012). For western Nepal the authors give MB values of -320±130 kg m^-2 yr^-1 (Gardelle et al. 2013) and -340±50 kg m^-2 yr^-1 (Kääb et al. 2012). Neckel et al. (2014) estimate a mass loss of -370±250 kg m^-2 yr^-1 between 2003 and 2009 for the Gangdise Shan including the Halji region.

The few existing MB measurements over several years reveal the most negative mass balances in the Himalayas, ranging from -1100 kg m^-2 yr^-1 to -760 kg m^-2 yr^-1 (average -930 kg m^-2 yr^-1) between 2002 and 2010 (Yao et al. 2012).

Thus, the MB model result for a precipitation scaling factor of 0.31 (-983 kg m^-2 yr^-1) is within the range of the results of Yao et al. (2012). However, the detection of a cold bias in HAR air temperature at Naimona’nyi glacier (see section 4) implies that a similar effect probably has to be considered also

at Halji glacier. Both glaciers are only ≈26 km apart from each other (see section 1.4.1). An increase in Tair will in turn demand a precipitation increase to obtain similar MB results. Thus, further uncertainty analyses with various combinations of precipitation scaling factors and air temperature offsets are required (see section 5.5).

Fig. 5.6: Glacier-wide annual MB and runoff for MB years (October – September) 2000-2011 estimated by the HAR forced MB model for Halji glacier. The uncertainty ranges are calculated assuming different preci-pitation amounts (see section 5.3, Table 5.2). The black dots mark the ‘non GLOF years’ (Kropacek et al. (2013)). Note that the MB in 2000/01 (blue box) should not be considered.

5.4.2 Snow line and ELA characteristics 2000-2011

To complement the studies on the inter- and intra-annual snow line and ELA characteristics at Zha-dang glacier (see section 2.4.4) and PIC (see section 3.4.2), mean monthly variations of the glacier-wide mean altitude of the transient snow line between 2001 and 2011 are calculated from the MB

To complement the studies on the inter- and intra-annual snow line and ELA characteristics at Zha-dang glacier (see section 2.4.4) and PIC (see section 3.4.2), mean monthly variations of the glacier-wide mean altitude of the transient snow line between 2001 and 2011 are calculated from the MB