2

Large scale studies on ice shelves

Pritchard et al., 2012Zwally et al., 2005 Shepherd et al., 2010

9 years100 km3 months

DurationSpat. Res.Time Res.

14 yearsOne value per ice shelf35 days (!)

5 years30 km1-2 years

ICESat 2003-2008ERS-1/2 1992-2001 ERS-2/Envisat 1994-2008

This study: ERS-1/ERS-2/Envisat 1992-2012

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3

The need for multi-mission RA

Long vs short records in detecting climate trends

Our goal → to capture the variability in space and time on the ice shelf spatial scales: 20+ years / 20-30 km

Fricker and Padman, 2012

How long? Decadal records (20+ years)

Interannual and decadal variability unexplored

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4

Penetration depth (backscatter)

Penetration depth:

! Water ! "(mm)! Wet snow ! O(cm)! Dry snow ! O(m)

! And varies with time

Radar penetrates into firn layer

A

B

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5

Constructing time series of dh

Similar (but not the same) method asDavis & Segura (2001), Zwally et al. (2005), Khvorostovsky (2011).

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6

Averaging in time and space

less crossovers per bin → larger error bars

improved signal-to-noise ratio and no gaps

1 vs 3-month averages

20-30 km bins

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Crossing all possible time combinations

Now we have one time series per reference time: t1, t2, t3, ...

These are elevation changes with respect to different epochs

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8

One grid per time combination

t1t2

t3

~ 1500 gridsCrossovers

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Multi-referenced time seriesAt every individual grid-cell we have now several time series

2) Then we frequency-weighted average the aligned time series

1) To align we use average of the offset for overlap period only

outliers

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10

Cross-calibration of average TSCross-calibration is done using overlap periods between missions

dh = elevation change dAGC = backscatter power change

ERS-1 ERS-2 Envisat

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11

Backscatter correction (approach 1)

By correlating absolute values ! dAGC x dh

Wingham et al., 1998; Davis & Ferguson, 2004; Zwally et al., 2005

Backscatter correction (approach 1)

By correlating absolute values ! dAGC x dh

Backscatter correction (approach 2)

By correlating differences ! diff(dAGC) x diff(dh)

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Backscatter correction (approach 3)

By time variable correlation ! R(t) and S(t)

15

Backscatter correction (approach 3)

By time variable correlation ! R(t) and S(t)

Sensitivity

Correlation

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Correlation and Sensitivity maps

(1)

(2)

FRIS

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Correlation and Sensitivity maps

(1)

(2)

ROSS

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Now some results

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High spatial and temporal variability

20-year trend in elevation change(original grid)

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High spatial and temporal variability

Obs: 2001 was chosen to avoid a big calving event

20-year trend in elevation change(original grid)

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Obs: 2001 was chosen to avoid a big calving event

20-year trend in elevation change(original grid)

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High interannual variability

Coherent changes?Tracking coherent events around the Antarctic margin

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How well do we know what RA is measuring?

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25

Envisat vs ICESat

We followthe ICESatcampaigns

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Envisat vs ICESat

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Conclusions

! Multi-mission RA can be used to construct continuous long records with their variability content

! There is a lot of variability both in time and space

! Variability is the key to understand forcings and climate-induced changes (ocean and atmospheric circulation)

! Relative error (precision) vs absolute error (penetration)

! Different b/s approaches yield different results?

! How can we validate b/s correction when there are so little ground truthing data and in practice:

28

We thanks

! NASA NESSF Fellowship

! Jay Zwally & Jairo Santana (NASA/GSFC)

! Curt Davis (UM) & Duncan Wingham (UCL)

! NASA grants NNX06AD40G and NNX10AG19G

! ESA for ERS-1, ERS-2 and Envisat altimeters!

! San Diego Super Computer Center

! Geir Moholdt

! Python and Open Source

fpaolo@ucsd.edu

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Antarctic ice shelf mask

A reliable and complete ice shelf mask is a problem

So we (Geir Moholdt) created our own using all data available: MOA (Scambos et al. 2007), ASAID (Bindschadler et al. 2011), InSAR (Rignot et al. 2011), ICESat (Fricker/Brunt et al. 2006-10)

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Backup slide

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Challenges of multi-mission integration

! Differences between missions:

- RA systems, orbit configurations, time spans...

! Radar interaction with time variable surface properties

! Spatial and temporal dependent corrections:

- Ocean tides (for high lat)

- Atm pressure (IBE)

- Surface density (firn densification)

- Penetration depth (backscatter)

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How to reduce the noise?

! Due to hydrostatic equilibrium the altimeter only see 10% of the grounded ice signal (in elevation change)

! So to increase signal-to-noise ratio → requires lots of averaging both in time and space

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33

The uncertainty

! How well do we know the error?

! What do error bars in the time series actually represent?

! What about the uncertainty in penetration depth?

O(m/cm)

After all the averaging a mean error is: ± 5-20 cm over 20-30 km