phd qualifying presentation
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Interannual and decadal variations of Antarctic ice shelves using multi-mission satellite radar altimetry, and links with oceanic and atmospheric forcingsTRANSCRIPT
Interannual and decadal variations ofAntarctic ice shelves using multi-missionsatellite radar altimetry, and links with oceanic and atmospheric forcings
Fernando S. PaoloPhD Qualifying, May 20, 2013Scripps Institution of OceanographyUniversity of California, San Diego
Presentation outline
1. Background
Importance, Hypothesis, Evidence
2. Thesis
Chapter 1, Chapter 2, Chapter 3
3. Summary
Results and Big Picture
Why do we care?
Ice-sheet mass loss Sea-level rise
Why do we care?
Shepherd et al., 2012
Ice-sheet mass loss Sea-level rise
Antarctica
GreenlandGlaciers
Ice volume3 mm/yr (~1.8 from Cryosphere)
Why Antarctica?
The marine Ice-sheet instability
Bed above sea levelIncreased discharged with grounding-line retreat → unstable condition!
Vaughan and Arthern, 2007
Fig. M. HelperData BEDMAP
Why ice shelves?
Fig. Ice-shelf coverageby satellite altimetrymissions
ice shelves are the “interface” between the ice sheet and the ocean
Ice-shelf buttressing
Compressive stress is a result of ice-shelf buttressing
Hughes, 2011
OCEAN GROUNDED ICE
Ice rise
Confined embayment
Ice rumple
Calvingfront
Ice-shelf-ocean interaction100s km
1-2 km
Three modes of basalt melt (Jacobs et al., 1992)
Fig. M. Craven, AAD
Ice-shelf-ocean heat exchange
Jenkins et al., 2010
Melt rates of 10s m/yr
Ice-shelf-ocean heat exchange
Jacobs et al., 2011Jenkins et al., 2010
Melt rates of 10s m/yr
CDW all the way to the sub-ice-shelf cavity
Ice-shelf and grounded-ice thinning
Pritchard et al., 2012
Evidence on ice-shelf buttressing
Rignot et al., 2004
Previous studies
Zwally et al., 2005 Shepherd et al., 2010 Pritchard et al., 2012
To detect climate signals we need long and continuous records!
ERS-1/2 1992-01 ERS-2/Envisat 1994-08 ICESat 2003-08
9 years50 km
DurationSpt. Res.
14 yearsOne trend per ice shelf
5 years30 km
My contribution
1. Derive reliable time series of elevation
change over the longest possible time period
2. Quantify long-term trends
3. Quantify interannual-to-decadal variability
4. Identify causes of temporal and spatial
variability
Thesis structure
Chapter 1 → The methodology
(Generate the dataset)
Chapter 2 → Radar-Laser comparison
(Validate the dataset)
Chapter 3 → Ice-shelf variability
(Analyze the dataset)
Chapter 1Constructing time series of elevation change
Satellite altimetry missions
Twenty years of continuous data over the ice shelves
The challenge of multi-mission integration
Differences between missions:
– RA systems, orbit configurations, time spans...
Radar interaction with variable surf. properties:
– Surface density,
– Penetration depth,
Spatial and temporal dependent corrections:
– Ocean tide + load (for high lat)
– Atm pressure (IBE)
– Regional sea-level rise
ρs(x , t )
k e( x , t)
The challenge over ice shelves
Due to hydrostatic equilibrium the altimeter only see
10-15% of the grounded ice signal (in elevation
change)
So to increase signal-to-noise ratio requires lots of
averaging both in time and space
Averaging in time
Monthly averages
Seasonalaverages
Time steps → 3-month blocks of data
Averaging in space
3 x
One month of data
~750 bins with 15 to 200 observations (for FRIS)
Averaging time series
82 time series per bin (x 2)
61,500 time series for FRIS (x 2)
Matrix before
Matrix after
Inter-mission cross-calibration
ERS-1 ERS-2 Envisat
What happens when there are no data in the overlapping period?
The backscatter problem
ρs(x , t )
k e(x , t )
Remy et al., 2012
Penetration:
Densification:
Backscatter correction
hc (t)=h(t)−s g (t)−h0
Amplitude series
Differenced series
Done for each grid-cell
Elevation
Backscatter
Time-varying backscatter
hc (t)=h(t )−s(t )g (t )−h0(t )
ERS-1 ERS-2 Envisat
Done for each grid-cell
Different corrections, different results?
Amplitude ts
Differenced ts
Different corrections, different results?
Different fluctuation and trend
Constant correlation
Variable correlation
Amplitude ts
Differenced ts
How significant are these differences?
Chapter 2Envisat (radar) vs ICESat (laser) inter-comparison
Two altimeters, one purpose
Envisat (Radar)
– microwave (λ ~ 2.5 cm)
– wide footprint (3 km)
– all weather
– continuous sampling
– penetrates into snow
ICESat (Laser)
– visible (λ ~ 650 nm)
– narrow footprint (70 m)
– cloud interaction
– campaign mode
– top-of-snow reflection
Do they measure the same thing?
First time this comparison is done in this way
Do they measure the same thing?
Envisat ICESat
We need an explanation for such differences!
First time this comparison is done in this way
Two ways of estimating elevation changes
1) Eulerian (fixed):
2) Lagrangian (moving):
∂h∂ t
D hD t
=∂h∂ t
+ u⋅∇ h
A A' B(t1) (t2)
A'-A = Euler B-A = Lagrange
Footprint differences
ICESat footprint (70 m) is about 0.05% of RA-2 footprint (3 km)
Is radar Eulerian?∂h /∂ t
Is radar Eulerian?∂h /∂ t
What is signal and what is noise?
ICESat data are very noisy! How much can we trust?
Pritchard et al., 2012
Cross-over analysis Along-track analysis
Two different techniques, same pattern → features are in the data!
Chapter 3Variability of Antarctic ice-shelf elevations
Our main goal
– Search for mechanisms that could explain the
observed variability in h (x , t )
Ice-shelf mass balance
∂h∂ t
=∂Δ∂ t
−M∂∂ t
ρw−1
+∫0
Mdm
∂∂ t
ρ f−1
(m)
+ (ρi−1−ρw
−1) ( M s+ M b+ u⋅∇M + M ∇⋅u )
Altimeterobservation
Sea-level variations
Ocean-densitychanges Firn compaction
Ice-ocean density contrast
Surfaceaccumulation rate
Basalaccumulationrate
Advection ofthickness gradientand flow divergence
Shepherd et al., 2003; Padman et al., 2012
Variability within an ice shelf
We are able to resolve the “fine” spatial scales
Spatio-temporal change in ∂h /∂ t
Why aren't thinning/thickeningregions “fixed”?
Correlations, correlations...
Fig. J. Allen, NASAData NSIDC
– Sea ice protects ice shelves by cooling air temperatures and dampening waves.– Also affects mode 1of basal melt.
What is the relationto sea-ice variability?
Is there any relation to climate Indices (ENSO, SAM, ZW3)?
– EOF analysis on h(x,t)
Large-scale coherent events?
AMERYFRIS
ROSS
Decadal oscillationIn phase?
Thesis summary
Generate a 20-year long and high resolution dataset of thickness variation for all Antarctic ice shelves.
Better understand the radar altimeter signal interaction with ice surfaces, and its effect in the final estimates.
Estimate long-term trends and explain the variability in Antarctic ice-shelf thickness for the last two decades.