1 briggs altimetry mass balance - esa...
TRANSCRIPT
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Ice sheet mass balance from satellite altimetry
Kate Briggs (Mal McMillan)
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Outline
• Background
• Recap ‐ 25 year altimetry record
• Recap ‐Measuring surface elevation with altimetry
• Measuring surface elevation change
• Determining ice sheet mass changes
• Sources of uncertainty
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Outline
• Background
• Recap‐ 25 year altimetry record
• Recap ‐Measuring surface elevation with altimetry
• Measuring surface elevation change
• Determining ice sheet mass changes
• Sources of uncertainty
• Current challenges
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Ice sheet contribution to Sea Level Rise
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Ice sheet contribution to Sea Level Rise
Current monitoring Future Projections
• Current mass balance assessment.
• Identifying emerging signals of change.
• Understanding process.
• Model evaluation and boundary conditions.
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Ice sheet contribution to Sea Level Rise
Current monitoring Future Projections
• Current mass balance assessment.
• Identifying emerging signals of change.
• Understanding process.
• Model evaluation and boundary conditions.
Aiming to resolve the dominant variability of the ice sheet system:• Spatial – evolution of individual glacier systems.• Temporal – seasonal fluctuations in mass.
High resolution.
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Ice sheet contribution to Sea Level Rise
Current monitoring Future Projections
• Current mass balance assessment.
• Identifying emerging signals of change.
• Understanding process.
• Model evaluation and boundary conditions.
Altimetry:• Spatial ‐‐ 1 – 10 km.• Temporal ‐‐ monthly.
High resolution.
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A 25 year record of change
* Focus here on radar altimetry but large overlap with laser altimetry *
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Polar orbiting radar altimeter missions
1991 2000
1995 2003
2002 2011
ERS‐1 ERS‐2 ENVISAT CryoSat‐2
2010
Sentinel‐3
2016overlap
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ERS‐1 ERS‐2 ENVISAT CryoSat‐2
Repeat measurement every ~ 35 days
369 day repeat
(30 day subcycle)
Sentinel‐3
27 day repeat
Polar orbiting radar altimeter missions
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ERS‐1 ERS‐2 ENVISAT CryoSat‐2
Repeat measurement every ~ 35 days
369 day repeat
(30 day subcycle)
Sentinel‐3
Regular, near continuous record of surface elevation change
27 day repeat
Polar orbiting radar altimeter missions
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ERS‐1 ERS‐2 ENVISAT CryoSat‐2
Low resolution altimetry High resolution altimetry
Sentinel‐3
Delay‐Doppler / SAR processing
Interferometer
Polar orbiting radar altimeter missions
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Recap ‐Measuring surface elevation
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Measures time taken for electromagnetic pulse to be reflected back from Earth’s surface.
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Measures time taken for electromagnetic pulse to be reflected back from Earth’s surface.
Surface elevation of the ice sheet.
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Measures time taken for electromagnetic pulse to be reflected back from Earth’s surface.
Surface elevation of the ice sheet.
Repeating measurements in time shows whether the surface is rising or falling.
dh
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Measuring surface elevation change
• Cross‐over method
• Repeat track
• Plane fit method
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1. Identify intersections of satellite tracks
2. Form time‐series of elevation (plus uncertainty) at each crossing point
3. Spatially average data to reduce uncorrelated noise, e.g. 10 x 10 km
4. Fit model to each gridded time‐series, e.g. linear + sinusoidal
5. Retrieve elevation rates
6. Account for omission areas
Cross‐over Method
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dH
35 days
t = 0
t = 0Cross‐over Method
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dH
35 days
t = 0
t = 35Cross‐over Method
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dH
35 days
t = 0
t = 70Cross‐over Method
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dH
35 days
t = 0
t = 105Cross‐over Method
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dH
35 days
t = 0
t = 140Cross‐over Method
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dH
35 days
t = 0
t = 175Cross‐over Method
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35 days
t = 0
t = 0Uncertainty from same cycle dh
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Time (years)
)2sin( dtcbta
Relative he
ight (m
)Fit model to gridded time series and retrieve elevation rate
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1. Split satellite track into kilometer scale segments
2. Define a model for elevation fluctuations within each segment (account for
topography and dhdt)
3. Least squares fit of model to data within each segment
4. Retrieve model parameters (includes surface elevation, elevation rate) and
associated uncertainty
5. Remove poorly constrained solutions
6. Retrieve elevation time series using model fit
7. Account for omission areas
Repeat Track Method
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1. Split satellite track into kilometer scale segments.
2. Define a model for elevation fluctuations within each segment.
3. Least squares fit of model to data within each segment.
4. Retrieve model parameters and associated uncertainty (includes elevation rate).
5. Remove poorly constrained solutions.
6. Retrieve elevation time series using model fit.
7. Account for omission areas.
Repeat Track Method
Alternative: External DEM
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Repeat track method
Don’t just utilise data at orbit crossing point….
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350 m
…. use all data along the satellite track.
Repeat track method
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t = 35
Accumulate data from repeat tracks
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t = 70Accumulate data from repeat tracks
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t = 105Accumulate data from repeat tracks
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t = 140
Accumulate data from repeat tracks
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t = 140
Accumulate data from repeat tracks
• Cut data up into segments along track.
• Data within each track segment are dispersed in space and time, and so solve simultaneously for both spatial and temporal components.
• Retrieve elevation rate.
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Plane fit method
x y
z
• Modification of repeat track approach.
• Well suited to more homogeneous data sampling (e.g. CryoSat), rather than where data clustered along distinct tracks (e.g. ICESat).
• Group data based on spatial proximity, don’t distinguish between tracks.
x
y
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t1
Influence of surface slope – affects repeat track and model fit
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t1 t2
Influence of surface slope – affects repeat track and model fit
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~ 500 m
dz
Strategy required to separate elevation changes into spatial and temporal components.
t1 t2
Drift between orbits
z = f (x,y,t)
Influence of surface slope – affects repeat track and model fit
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Fit model to data segment and retrieve elevation rate
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Fit model to data segment and retrieve elevation rate
Linear rate of elevation change
anisotropic scattering within snowpack
quadratic model of topography
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mean elevation
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1
-1m
anisotropic scattering within snowpack
• Difference in penetration depth depending on the direction of the satellite
• Caused by persistent wind driven, directional features of the surface and firn
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Linear rate of elevation change
‐2
+2
m/yr
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Derive time‐series by isolating temporal component
dH
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2010
2011
2012
2013
2014
RACMO2.3 simulated first year of melt, 2010‐2014.
•Model fit ‐ Greenland model refinements
Unprecedented melting in 2012 produced large area undergoing
melt for the first time.
Transition from dry to wet snowpack and ice lens formation.
Affected the depth distribution of backscattered energy.
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Surface elevation change recorded by CryoSat‐2 during summer 2012.
m‐2
‐1
+2
+1
0
Nilsson et al. (2015)
2010
2011
2012
2013
2014
RACMO2.3 simulated first year of melt, 2010‐2014.
•Model fit ‐ Greenland model refinements
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Surface elevation change recorded by CryoSat‐2 during summer 2012.
m‐2
‐1
+2
+1
0
Nilsson et al. (2015)
•Model fit ‐ Greenland model refinements
Additional term introduced into the model fit to account for changes in depth distribution of backscattered energy.
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Remove poorly constrained solutions
• During model fit, additional parameters (e.g. surface slope) and goodness‐of‐fit statistics (e.g. RMS of the residuals) are generated.
• These can be used to reject model solutions with large uncertainty or physically unrealistic retrievals.
Failure of CryoSat‐2 interferometric model fits
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Account for omission areas
• coastal regions.• high latitude region.• areas where elevation rate retrieval failed.
1992‐1996 elevation change
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Account for omission areas
1. Statistical approaches:• apply the mean elevation rate of each basin to unobserved regions.• interpolation, e.g. Delauny triangulation, ordinary Kriging.
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Account for omission areas
Use multiple linear regression to model elevation rate as a function of latitude (l),
elevation (z) and velocity change (Δv).
1. Statistical approaches:• apply the mean elevation rate of each basin to unobserved regions.• interpolation, e.g. Delauny triangulation, ordinary Kriging.
2. Model based approaches:• use external data fields, e.g. elevation, velocity, to derive an
empirical model of elevation change.• model used to fill unobserved regions.
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Account for omission areas
1. Statistical approaches:• apply the mean elevation rate of each basin to unobserved regions.• interpolation, e.g. Delauny triangulation, ordinary Kriging.
2. Model based approaches:• use external data fields, e.g. elevation, velocity, to derive an
empirical model of elevation change.
3. Combined approaches:• e.g. Kriging with external drift.• supplements Kriging with a background field, e.g. velocity, to guide
interpolation in data sparse areas.
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Cross‐over solution ‐ Antarctica
1992‐1996 elevation change
1992‐2003 elevation change (Shepherd and Wingham, 2007)
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Pritchard et al. (2009) Flament and Remy (2012)
Envisat radar altimetry, 2002‐2010ICESat laser altimetry, 2003‐2008
Repeat track solutions ‐ Antarctica
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‐2
+2
m/yr
Model fit solution ‐ Antarctica
CryoSat‐2 radar altimetry, 2010‐2013
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Cross‐over solution ‐ Greenland
1992‐2008 elevation change from radar altimetry (Khvorostovsky, 2012)
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Repeat track solution ‐ Greenland
2003‐2008 elevation change from laser altimetry (Sørensen et al., 2012)
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Model fit solution ‐ Greenland
2011‐2014 elevation change from radar altimetry,
+2
‐2m/yr
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Cross‐over advantages• The same location is repeatedly sampled in time, resulting in a smaller
topographic contribution to dH.
• Uncorrelated measurement errors can be computed from height differences from the same cycle.
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Cross‐over advantages• The same location is repeatedly sampled in time, resulting in a smaller
topographic contribution to dH.
• Uncorrelated measurement errors can be computed from height differences from the same cycle.
Repeat track / Model fit advantages
• Order of magnitude increase in the number of measurements available.
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Determining ice sheet mass changes
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bedbed
Determining ice sheet mass changes
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
Ice
Firn
.hobs
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Elevation changes not associated with change in ice mass
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
• firn compaction – change in density but not mass.
• bed elevation:• GIA – glacio‐isostatic adjustment
bedbed
Ice
Firn.hfc
.hbed
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2011‐2014 rate of elevation change, due to anomalies in firn compaction,
from IMAU‐FDM.
Elevation changes not associated with change in ice mass
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
• firn compaction – change in density but not mass.
• bed elevation:• GIA – glacio‐isostatic adjustment
These contributions can be modelled and either:• an explicit correction applied to the observed
elevation rates, or• their magnitude included as a term in the
uncertainty budget.
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Determining ice sheet mass changes
Ice
Firn
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
.hBMB
Basal mass balance – assumed to be negligible (not for ice shelves).
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Determining ice sheet mass changes
Ice
Firn
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
.hSMBLeaves changes in the thickness of the firn and
ice column, driven by mass loss or gain.
FirnThickness of the firn column changes when SMB deviates away from steady state conditions, usually defined as the long term (climatological) mean SMB.i.e. when mass exchange at the upper and lower boundaries of the firn column are not in balance.
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Determining ice sheet mass changes
Ice
Firn
hobs = hSMB + hdyn + hfc + hbed + hbmb
. . . . . .
Leaves changes in the thickness of the firn and ice column, driven by mass loss or gain.
IceThickness of the ice column changes when ice flux into the column is not in balance with ice flux out of the column.i.e. when mass exchange at the lateral and upper boundaries of the ice column are not in balance.For example resulting from changes in ice dynamics.
Q inQ out
V ice
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bedbed
Ice
Firn.hfirn
.hice
Determining ice sheet mass changes
• To convert from elevation change to mass change, need the density of the mass gained or lost.
• Therefore need to partition observed elevation changes according to whether they have occurred within the firn or the ice column.
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bedbed
Ice
Firn.hfirn
.hice
Determining ice sheet mass changes
• To convert from elevation change to mass change, need the density of the mass gained or lost.
• Therefore need to partition observed elevation changes according to whether they have occurred within the firnor the ice column.
• 2 strategies:
1. Use an empirical model to define regions undergoing dynamic and SMB driven changes.
2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
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1. Use an empirical model to define regions undergoing dynamic and SMB driven changes.
• ρsnow > ELA
• ρice < ELA
Binary model for Greenland:
• ρsnow < Tdyn
• ρice > Tdyn
Binary model for Antarctica:
Tdyn is a threshold based on auxiliary data, for example elevation rate and velocity.
ρsnow
ρice
Simple model
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1. Use an empirical model to define regions undergoing dynamic and SMB driven changes.
Simulate spatial and temporal variations in ρsnow and use auxiliary datasets to identify regions of ice dynamic imbalance.
Greenland density model definition.
ablation dynamicablationfirn
Greenland density model.
Refined model
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2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
• Model SMB thickness changes, including deposition, removal and compaction.
• Subtract modelled SMB thickness change from observed change to estimate dynamic thickness change of underlying ice column.
• Convert ice thickness change to mass at density of ice.
• Add modelled SMB mass change from regional climate model to get total mass balance.
bedbed
Ice
Firn.hfirn
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2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
• Model SMB thickness changes, including deposition, removal and compaction.
• Subtract modelled SMB thickness change from observed change to estimate dynamic thickness change of underlying ice column.
• Convert ice thickness change to mass at density of ice.
• Add modelled SMB mass change from regional climate model to get total mass balance.
m/yr
IMAU‐FDM rate of SMB driven thickness change, 2011‐2014.
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2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
• Model SMB thickness changes, including deposition, removal and compaction.
• Subtract modelled SMB thickness change from observed change to estimate dynamic thickness change of underlying ice column.
• Convert ice thickness change to mass at density of ice.
• Add modelled SMB mass change from regional climate model to get total mass balance.
bedbed
Ice
Firn
.hice
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2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
• Model SMB thickness changes, including deposition, removal and compaction.
• Subtract modelled SMB thickness change from observed change to estimate dynamic thickness change of underlying ice column.
• Convert ice thickness change to mass at density of ice.
• Add modelled SMB mass change from regional climate model to get total mass balance.
m/yr
Ice column thickness change, 2011‐2014.
![Page 77: 1 Briggs Altimetry mass balance - ESA SEOMseom.esa.int/.../Day4/1_Briggs_Altimetry_mass_balance.pdf · 2016-11-09 · Determining ice sheet mass changes Ice Firn h obs = h SMB + h](https://reader034.vdocuments.site/reader034/viewer/2022042212/5eb573b4263677792545461e/html5/thumbnails/77.jpg)
2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
• Model SMB thickness changes, including deposition, removal and compaction.
• Subtract modelled SMB thickness change from observed change to estimate dynamic thickness change of underlying ice column.
• Convert ice thickness change to mass at density of ice.
• Add modelled SMB mass change from regional climate model to get total mass balance.
bedbed
Ice .hice x ρice
Firn
+msmb
.
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1. Use an empirical model to define regions undergoing dynamic and SMB driven changes.
Advantages• Avoids observational and model errors being interpreted as ice imbalance
across the ice sheet interior.• For example:
small elevation rate errors x high density x large areacan accumulate into relatively large mass balance signals.
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2. Use a firn model to simulate SMB driven changes and isolate the underlying ice dynamic imbalance.
Advantages• Avoids relying on an a priori model for where ice dynamic imbalance is
occurring.• For example, allows for the possibility that dynamic imbalance is
occurring across the ice sheet interior.
1. Use an empirical model to define regions undergoing dynamic and SMB driven changes.
Advantages• Avoids observational and model errors being interpreted as ice imbalance
across the ice sheet interior.• For example:
small elevation rate errors x high density x large areacan accumulate into relatively large mass balance signals.
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Ice sheet specific mass balance
‐200
200
cm we yr‐1
Antarctic rate of mass change, from CryoSat‐2.
Greenland rate of mass change, from CryoSat‐2.
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Ice sheet specific mass balance ‐ comparisons
Rate of mass change from CryoSat‐2.
Rate of mass change from RACMO2.3 (SMB only).
Rate of mass change from GRACE.
![Page 82: 1 Briggs Altimetry mass balance - ESA SEOMseom.esa.int/.../Day4/1_Briggs_Altimetry_mass_balance.pdf · 2016-11-09 · Determining ice sheet mass changes Ice Firn h obs = h SMB + h](https://reader034.vdocuments.site/reader034/viewer/2022042212/5eb573b4263677792545461e/html5/thumbnails/82.jpg)
Monthly time series of Greenland mass evolution from GRACE (green) and CryoSat‐2 (blue).
Time series of mass evolution
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Principle Sources of Uncertainty*
1. Rates of elevation change
2. Density model used for volume to mass conversion
3. Accounting for omission areas
4. Correction for mass conserving processes
* Lots of approaches exist. This is one example based on a density model approach for the volume to mass conversion in Greenland.
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Principle Sources of Uncertainty
1. Rates of elevation change.
• Instrument effects (e.g. radar speckle).
• Processing effects (e.g. retracking).
• Elevation rate model limitations (i.e. how well does the functional form of the model reflect reality).
• Snowpack effects (e.g. changes in the depth distribution of backscattered energy).
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Principle Sources of Uncertainty
1. Rates of elevation change.
• Instrument effects (e.g. radar speckle).
• Processing effects (e.g. retracking).
• Elevation rate model limitations (i.e. how well does the functional form of the model reflect reality).
• Snowpack effects (e.g. changes in the depth distribution of backscattered energy).
1‐sigma uncertainty on rate of elevation change.
Estimated during model fit.
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Principle Sources of Uncertainty
1. Rates of elevation change.
• Instrument effects (e.g. radar speckle).
• Processing effects (e.g.retracking).
• Elevation rate model limitations (i.e. how well does the functional form of the model reflect reality).
• Snowpack effects (e.g. changes in the depth distribution of backscattered energy).
• Mitigate using a backscatter correction.
• Estimate empirically, e.g. based on Greenland 2012 melt event.
• Area of ongoing research.
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Principle Sources of Uncertainty
1. Rates of elevation change.
2. Density model used for volume to mass conversion.• Estimate uncertainty based on model simulations..
Estimate of density uncertainty based upon the IMAU firn
densification model.
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Principle Sources of Uncertainty
1. Rates of elevation change.
2. Density model used for volume to mass conversion.
3. Accounting for omission areas.• Statistical uncertainty from interpolation scheme or regression model.
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Principle Sources of Uncertainty
1. Rates of elevation change.
2. Density model used for volume to mass conversion.
3. Accounting for omission areas.
4. Correction for mass conserving processes.• Estimate firn compaction correction
uncertainty based upon model simulations.• Estimate magnitude of bed elevation
change from GIA model.
Estimate of firn compaction correction uncertainty based upon the IMAU firn densification model.
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Current Frontiers for Radar Altimetry
1. Improving understanding of radar wave interaction with the snowpack.
2. Inter‐mission cross‐calibration for continuous time series generation.
3. Managing the transition from low to high resolution measurements.
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Δσs0 Δσv0
Change in scattering characteristics during summer 2012 melt event.
1. Improving understanding of radar wave interaction with the snowpack.
• Retrieval of scattering properties from the radar echo.
• Use to investigate snowpack related artifacts.
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Cycle number (relative to 1st Envisat cycle)
ERS‐2 Envisat
Relative he
ight (m
)
2. Inter‐mission cross‐calibration for continuous time series generation.
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Interferometric altimetry elevation rates
Non‐interferometric altimetry elevation rates
3. Managing the transition from low to high resolution measurements.
• Comparisons between interferometric and non‐interferometric high resolution altimetry
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3. Managing the transition from low to high resolution measurements.
Standard CryoSat low resolution data
Emulated Sentinel‐3 high resolution data
Low resolution data emulated from high resolution
Comparison of high and low resolution data over Dome C in East Antarctica, and capability to generate a pseudo low resolution
product to facilitate merging of full altimeter record.
• Comparison between high and low resolution altimetry.
• Assessment of whether the latter can be simulated from the former.
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Summary and Outlook
• 25 year record of ice sheet elevation change from radar altimetry.
• Longest continuous record of all geodetic techniques.
• Unlike GRACE doesn’t measure mass change directly, and so additional processing steps and assumptions are required to convert to mass.
• Able to provide high resolution measurements of mass balance, at the basin scale and with monthly temporal sampling.
• Observations can be combined with regional climate and firn model simulations to investigate processes driving change.
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Summary and Outlook
• Provision of future measurements with the Sentinel‐3 and ICESat‐2 satellites, allowing the continuation of long‐term, systematic monitoring programmes.
1992 20302010 2016
Low resolution altimetry High resolution altimetry
ICESAT‐2
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