Spatial distribution of accumulation in the Adélie Land - Comparison of the Antarctic GLACIOCLIM-SAMBA observation data with remote sensing techniques and high-resolution climate models

Estimating the Surface Mass Balance

of the Antarctic coastal area

for climate models validation

1 – Coastal area SMB & sea level rise

2 – SMB observation for climate model validation

3 – Contribution of remote sensing data

4 – Conclusion & Outlook

Cécile AGOSTA

 SMB ~ net accumulation of snow

 GCMs  precipitation increase (21st c.)

↔

 Coast : major precipitation area & major changes

1 – Coastal area SMB & sea level rise

mm w.e. yr-1 200 150 100 50 20 0 -50

2 – SMB observation for climate model

validation

2

0

0

k

m

2 – SMB observation for climate model validation

http://www-lgge.obs.ujf-grenoble.fr/ServiceObs Dome C 4200 4000 3800 3600 3400 3200 3000 2800 2400 2200 2000 1800 1600 1400 1200 1000 800 600 400 200 0 -10 -60

Stake line (from 2004)

 Annual measurements

 emergence + density

 91 stakes over 150 km

Prud’homme Cape

 Glacioclim-SAMBA

 French Observatory of the Antarctic SMB

2 – SMB observation for climate model validation

 Glacioclim-SAMBA stake line

S M B ( m m w .e . y r -1 )

 Glacioclim-SAMBA stake line (average over 10 km) S M B ( m m w .e . y r -1 )

km along the stake line

 Measurements from IPEV

 From 1971 to 1991

 First 15 km of the stake line

2 – SMB observation for climate model validation

Comparison with older repports

Stationarity of spatial distribution

km along transect S M B ( m m w .e . y r -1 )

Similar temporal variability No significant trend

 Measurements from IPEV

 From 1971 to 1991

 First 15 km of the stake line

Years S M B ( m m w .e . y r -1 )

2 – SMB observation for climate model validation

MAR

Regional Atm. Model

40 km

2 – SMB observation for climate model validation

First assessement of climate models

 SMB distribution in coastal area

 Very different meso-scale distribution

LMDZ4 Atm. GCM 60 km Courbes 900 800 700 600 550 500 450 400 350 300 250 200 150 100 70 50 30 20 10 0 mm w.e. yr-1

1981-2000

km along stake line

S M B ( m m w .e . y r -1 )

2 – SMB observation for climate model validation

First assessement of climate models

 Long term mean

Years S M B ( m m w .e . y r -1 )

3 – Contribution of remote sensing

data

Data assimilation

3 – Contribution of remote sensing data

Reference climatology : Arthern et al., 2006

1200 1000 700 600 550 500 450 400 350 300 250 200 150 100 70 50 30 20 10 0 -100 mm w.e. yr-1 Parameters κ,θ,n n T P P e SMB ) (  ₀    Infrared T Microwaves P-P₀ Background model Field mesurements 1950 – 1990

Strong control of the Background model on

Arthern’s final map (in Adelie land)

Background model Arthern’s final map

900 800 700 600 550 500 450 400 350 300 250 200 150 100 70 50 30 20 10 0 mm w.e. yr-1

 _{Suspicious lack of variation in coastal area}

 _{Ability of background model to capture variations ?}

3 – Contribution of remote sensing data

900 800 700 600 550 500 450 400 350 300 250 200 150 100 70 50 30 20 10 0 mm w.e. yr-1

Arthern’s climatology km along stake line

S M B ( m m w .e . y r -1 )

 Spatial variability seems too weak in coastal area :

 Microwave footprint 60 km ?

 higher SMB should be modeled

 Melting ?

 low melting above 20 km from the coast

3 – Contribution of remote sensing data

 Reproducing mesoscale variations requires misleading

parameters

3 – Contribution of remote sensing data

Background model with adjusted parameters 900 800 700 600 550 500 450 400 350 300 250 200 150 100 70 50 30 20 10 0 mm w.e. yr-1

km along stake line

S M B ( m m w .e . y r -1 )

 Spatial variability seems too weak in coastal area :

 Microwave footprint 60 km ?

 higher SMB should be modeled

 Melting ?

 low melting above 20 km from the coast

 Orographic precipitation ?

 complementary parametrisation in the background model ?

3 – Contribution of remote sensing data

 Stake line

 Stable accumulation pattern

 Similar IPEV (20 yr) and Glacioclim (5 yr) average

and variability

 First evaluation of 2 models in coastal area

 LMDZ4 seems OK

 MAR seems too dry / low variability

 Lateral variability is required

 Remote sensing in coastal area :

 Spatial variability seems too weak

 Complementary parameterisation is proposed

(slope)

 Assessing additionnal information on lateral SMB

distribution

 Ground Penetrating Radar + Ice cores

www-lgge.ujf-grenoble.fr/ServiceObs/

SiteWebAntarc/background.html

References :

Arthern et al., 2006

Magand et al., 2008

Acknowledgement :

ice2sea, IPEV, Charmant