Pankaj kumar

Past evolution of Himalayan glaciers:
a regional climate model study
Pankaj Kumar1, Sven Kotlarski2, Christopher Mosely3, Kevin Sieck3, Holger
Frey4, Markus Stoffel5, Daniela Jacob1
1 : Climate Service Center 2.0, Hamburg, Germany
2 : Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland
3 : Max Planck Institute for Meteorology Hamburg, Germany
4 : Department of Geography, Uni. of Zurich- Irchel, Zurich,Switzerland
5 : Institute for Environmental Sciences, University of Geneva, Switzerland

2 Int. Conf. on Climate Change Innovation and Resilience for Sustainable Livelihood, Kathmandu, Nepal 12th-14th Jan. 2015
© Climate Service Center 2.0
Outline
Motivation
Experiment design
Observational Challenges
Results
Summary Bolch et al., 2012

• Over 800 million people depend on glacier melt water runoff throughout the
Hindu-Kush and the Himalayan (HKH) region. The region, also called as “Water
tower of Asia”, is the location of several major rivers basins.
• Glaciers in the central and eastern Himalaya strongly depend on the ISMR,
whereas the WH is more dependent on the winter precipitation.
• Future climate change scenarios suggest that ISMR will be reduced over the
HKH region [Kumar et al., 2013]. Therefore, it is important to assess the glacier
retreat under warming scenario.
• Difficult to assess the overall glacier response based on detailed models of
individual glaciers over HKH.
• RCMs provide an alternative way in which glaciers are interactively coupled to
the atmospheric model component, and their response is therefore fully
consistent with the simulated climatic changes.
• REMOglacier is first applied over the region using reanalysis data to test the model
quality.
Motivation

More sophisticated approach is necessary, as contribution of glacial melt-water is important
Interactive glacier scheme for regional climate modeling
Glacier mass balance and area changes on a sub-grid scale, accounting for direct physical feedback
mechanisms
Motivation-2
Kotlarski et al.
Clim dyn 2008
Motivation
• Applicable for entire mountain ranges and computationally effective, target resolution: RCM grid cell
• Simplified description and minimum of input data

Steps of Climate modeling chain
Emission Scenarios (IPCC)
Regional climate change signals
Regional climate change simulations
(RCMs)
Global climate change simulations (GCMs)

Experiment Setup
RCM REMOglacier
Resolution 0.22°x 0.22°
Domain 60.125-100.125 & 4.125 -40.125
Period 1989-2008, 1989-2005, 2006-2100
Forcing ERAI reanalysis, MPIESM-LR , NorESM [Hist, RCP45/85]
Frey et at. 2013

Experiment Setup
(i) 46 New Variables (ii) On/Of Switch

Observation Challenges
Frey et at. 2013
55% + glacier grid-box ~10%
Limited number of measuring stations over
the glacierized region. No gauge station over
Karakoram.

Seasonal Precipitation
Winter (left) and corresponding summer mean precipitation
[mm/d] for REMOglacier and several observational and reanalysis
datasets, 1989-2007. Gridded data over Karakoram, is quite
unrealistic, apparently due to the limited number of measuring
stations and hence systematic gauge undercatch

Precipitation and Temperature Annual Cycle
1989-2007, temperature annual trend is positive and
significant at 95% confidence level. ERAI –ive trend.
Precipitation Model and MERRA +ive and gridded
station observation negative, ERAI too.
Karakoram-Himalaya:
Observation Parameter Resolution K-H K
APHRODITE Precipitation ~25km 73.7 267.9
Temperature -3.9 -6.9
MERRA Precipitation ~50km 21.9 19.8
Temperature -0.2 -0.9
+ive precip means +ive model bias.
–ive temperature means model is cooler.

Results: Annual Mass Balance
“Karakoram anomaly”
is well reproduced.
(Hewitt, 2005; Gardelle
et al., 2012, Nature
Geo-Sc., 2012; Bolch
et al. 2012; ……)
Simulated mean annual mass balance [m.w.e.] for the period
1989-2008.

The equilibrium line marks the region where glacier mass balance is
zero. It divides accumulation (net snow and ice gain) and ablation (net
snow and ice loss) areas either for a particular year or for a longer period.
It's altitude is referred to as the Equilibrium Line Altitude (ELA).
For the present study ELA is calculated for the Karakoram-Himalaya
region dividing the region into four zone namely Karakoram (K), western
Himalaya (WH), central Himalaya (CH) and eastern Himalaya (EH). The
result of model simulated ELA’s (referring to the mean glacier mass
balance over the period 1989-2008).
Equilibrium Line Altitude (ELA)

ELA
Simulated mean
annual mass balance
[m.w.e./year] against
grid-box orography
(meter), 1989-2008.
The point where mass
balance is zero on the
regression line is
considered as an
estimate of the
regional ELA of the
respective domain.
All these values are close to those reported [Yao et al., 2012 (4800m-5200m); Bolch et al., 2012
(5150m-5600m)), and a slight systematic underestimation is apparent.

• For the first time a complete simulation of glacier climate interaction over South Asia
is done.
• Glacier area in the whole HKH region including Tibet is reported to be ~100,000 km2
(Yao et al. 2012). The glacier inventory prepared for forcing the RCM, estimates an
area of ~98,504 km2 (Frey et al. 2013).
• Over all glacier area change show a decrease, but do show some regions of increase
especially over the Karakoram (Hewitt, 2005; Gardelle et al., 2012; Bolch et al., 2012).
• Model grid is 25km and is very coarse/simplified for the such a complex domain,
where topography changes in km in few horizontal meters.
• Over data sparse and highly complex region, results need to be analyze with
caution!.
• Results indicate that observed glacier changes can be approximately reproduced
within a RCM based on simplified concepts of glacier-climate interaction.
• This, in turn, underlines the general applicability of the model system for scenarios
of 21st century climate and glacier change.
Summary

Tile Approch

Model grid-box cross-section
large-scale ice flow neglected!

Yao et al. 2012, Nature : ELA
Yao et al 2012

Regional Climate Models (needed!)

Orography
More realistic
monsoon
precipitation
climatology
in RCM
:1970-1999
RCM ~25KmObs ~ 55KmGCM ~200Km

Precipitation and Temperature Statistics
Observation Parameter Resolution K-H K WH CH EH
GPCC Precipitation ~50km 29.2 268.0 -2.3 5.6 46.2
APHRODITE
Precipitation
~25km
73.7 267.9 42.4 29.1 105.5
Temperature -3.9 -6.9 -3.9 -4.2 -2.2
CRU
Precipitation
~50km
57.7 151.6 49.6 47.8 57.0
Temperature -3.4 -8.3 -4.0 -1.8 -1.6
UDW
Precipitation
~50km
23.2 307.2 -6.1 8.7 31.6
Temperature -3.4 -6.5 -4.9 -2.4 -1.5
ERAI
Precipitation
~80km
-12.1 8.2 -25.4 -7.3 -11.4
Temperature -0.5 -1.7 -0.2 -0.1 -0.6
MERRA
Precipitation
~50km
21.9 19.8 13.0 0.3 39.7
Temperature -0.2 -0.9 -0.2 -0.6 0.3
Suplementry-Table-1: Details of gridded observation data-sets. Annual mean precipitation (%) and
near surface temperature (°C), REMOglacier difference with respect to observations over Karakoram-
Himalayas and its four sub-regions, 1989-2007. Statistics are computed when all data is brought at
0.25° grid. Positive precipitation means observations lower than model, i.e. a positive model bias.
Negative temperature means model is cooler.

Karakoram Precip and Tmp
Observation data
for Karakoram, is
quite unrealistic and
is apparently due to
the limited number
of measuring
stations and hence
systematic gauge
undercatch

Annual mean precipitation and
temperature bias wrt MERRA reanalysis
against glacierized grid-box fraction for
the four analysis domains. The solid line
shows the annual mean bias at every 5%
interval of mean glacierized grid-box
fraction.
Precipitation and Temperature Statistics

Precipitation Seasonal Bias

Temperature Seasonal Bias

GPCC

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