Pankaj kumar

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Past evolution of Himalayan glaciers: a regional climate model study Pankaj Kumar 1 , Sven Kotlarski 2 , Christopher Mosely 3 , Kevin Sieck 3 , Holger Frey 4 , Markus Stoffel 5 , Daniela Jacob 1 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

Transcript of Pankaj kumar

Page 1: 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

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Outline

Motivation

Experiment design

Observational Challenges

Results

Summary Bolch et al., 2012

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• 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

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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

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Steps of Climate modeling chain

Emission Scenarios (IPCC)

Regional climate change signals

Regional climate change simulations

(RCMs)

Global climate change simulations (GCMs)

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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

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Experiment Setup

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

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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.

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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

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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.

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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.

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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)

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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.

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• 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

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Tile Approch

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Model grid-box cross-section

large-scale ice flow neglected!

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Yao et al. 2012, Nature : ELA

Yao et al 2012

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Regional Climate Models (needed!)

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Orography

More realistic

monsoon

precipitation

climatology

in RCM

:1970-1999

RCM ~25Km Obs ~ 55Km GCM ~200Km

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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.

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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

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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

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Precipitation Seasonal Bias

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Temperature Seasonal Bias

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GPCC