interannual and decadal variability of antarctic ice shelf elevations from multi-mission satellite...
TRANSCRIPT
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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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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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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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Constructing time series of dh
Similar (but not the same) method asDavis & Segura (2001), Zwally et al. (2005), Khvorostovsky (2011).
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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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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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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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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)
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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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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:
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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
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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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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