Interannual and decadal variations of Antarctic ice shelves using multi-mission satellite radar altimetry, and links with oceanic and atmospheric forcings
DSD-INT 2017 XBeach application of coral reef-lined coasts - Van DongerenDeltares
Presentation by Ap van Dongeren (Deltares) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
DSD-INT 2017 XBeach application of coral reef-lined coasts - Van DongerenDeltares
Presentation by Ap van Dongeren (Deltares) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
DSD-INT 2017 Research and decision support applications of XBeach at the USGS...Deltares
Presentation by Joe Long (U.S. Geological Survey) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Wednesday, 1 November 2017, Delft.
DSD-INT 2017 Drivers of estuarine sand and mud dynamics, the example of the W...Deltares
Presentation by Anna Zorndt, Federal Waterways Engineering and Research Institute (BAW), Germany, at the Delft3D - User Days (Day 2: Sediment transport and morphology), during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
New evidence for surface water ice in small-scale cold traps and in three lar...Sérgio Sacani
The Mercury Laser Altimeter (MLA) measured surface reflectance, rs, at 1064 nm. On Mercury, most water-ice deposits have anomalously low rs values indicative of an insulating layer beneath which ice is buried. Previous detections of surface water ice (without an insulating layer) were limited to seven possible craters. Here we map rs in three additional permanently shadowed craters that host radar-bright deposits. Each crater has a mean rs value > 0.3, suggesting that water ice is exposed at the surface without an overlying insulating layer. We also identify small-scale cold traps (< 5 km in diameter) where rs > 0.3 and permanent shadows have biannual maximum surface temperatures < 100 K. We suggest that a substantial amount of Mercury’s water ice is not confined to large craters, but exists within micro-cold traps, within rough patches and inter-crater terrain.
DSD-INT 2017 Understanding the Response to Extreme Events in a Deltaic Curvil...Deltares
Presentation by Marc Sanuy (Universitat Politècnica de Catalunya) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Wednesday, 1 November 2017, Delft.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
20th Conference on Middle Atmosphere at the 99th Annual Meeting of the American Meteorological Society (abstract: https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352664)
DSD-INT 2017 Research and decision support applications of XBeach at the USGS...Deltares
Presentation by Joe Long (U.S. Geological Survey) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Wednesday, 1 November 2017, Delft.
DSD-INT 2017 Drivers of estuarine sand and mud dynamics, the example of the W...Deltares
Presentation by Anna Zorndt, Federal Waterways Engineering and Research Institute (BAW), Germany, at the Delft3D - User Days (Day 2: Sediment transport and morphology), during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
New evidence for surface water ice in small-scale cold traps and in three lar...Sérgio Sacani
The Mercury Laser Altimeter (MLA) measured surface reflectance, rs, at 1064 nm. On Mercury, most water-ice deposits have anomalously low rs values indicative of an insulating layer beneath which ice is buried. Previous detections of surface water ice (without an insulating layer) were limited to seven possible craters. Here we map rs in three additional permanently shadowed craters that host radar-bright deposits. Each crater has a mean rs value > 0.3, suggesting that water ice is exposed at the surface without an overlying insulating layer. We also identify small-scale cold traps (< 5 km in diameter) where rs > 0.3 and permanent shadows have biannual maximum surface temperatures < 100 K. We suggest that a substantial amount of Mercury’s water ice is not confined to large craters, but exists within micro-cold traps, within rough patches and inter-crater terrain.
DSD-INT 2017 Understanding the Response to Extreme Events in a Deltaic Curvil...Deltares
Presentation by Marc Sanuy (Universitat Politècnica de Catalunya) at the XBeach X (10th Year Anniversary) Conference, during Delft Software Days - Edition 2017. Wednesday, 1 November 2017, Delft.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
20th Conference on Middle Atmosphere at the 99th Annual Meeting of the American Meteorological Society (abstract: https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352664)
In the first part of the talk, we will present a sensitivity analysis of a novel sea ice model. neXtSIM is a continuous Lagrangian numerical model that uses an elastobrittle rheology to simulate the ice response to external forces. The response of the model is evaluated in terms of simulated ice drift distances from its initial position and from the mean position of the ensemble. The simulated ice drift is decomposed into advective and diffusive parts that are characterized separately both spatially and temporally and compared to what is obtained with a free-drift model, i.e. when the ice rheology does not play any role. Overall the large-scale response of neXtSIM is correlated to the ice thickness and the wind velocity fields while the free-drift model response is mostly correlated to the wind velocity pattern only. The seasonal variability of the model sensitivity shows the role of the ice compactness and rheology at both local and Arctic scales. Indeed, the ice drift simulated by neXtSIM in summer is close to the free-drift model, while the more compact and solid ice pack is showing a significantly different mechanical and drift behavior in winter. In contrast of the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy’s trajectories. We found that neXtSIM performs better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search-and-rescue operations. Adaptive meshes, as the one used in neXtSIM, are used to model a wide variety of physical phenomena. Some of these models, in particular those of sea ice movement, use a remeshing process to remove and insert mesh points at various points in their evolution. This represents a challenge in developing compatible data assimilation schemes, as the dimension of the state space we wish to estimate can change over time when these remeshings occur.
In the second part of the talk, we highlight the challenges that such a modeling framework represents for data assimilation setup. We then describe a remeshing scheme for an adaptive mesh in one dimension. The development of advanced data assimilation methods that are appropriate for such a moving and remeshed grid is presented. Finally we discuss the extension of these techniques to two-dimensional models, like neXtSIM.
On 17/10/2013 TU Delft Climate Institute organised the symposium The Greenland and Antarctic ice sheets: present, future, and unknowns. This is one of the four presentations given there.
http://www.tudelft.nl/nl/actueel/agenda/event/detail/symposium-tu-delft-climate-institute-17th-october-2013/
Sea ice models governed by physical equations have been used to simulate the state of the ice including features such as ice thickness, concentration, and motion. Recent satellite observations with high spatio-temporal resolution have also provided unique opportunities to examine ice motion and deformation. These multiple disparate data sources prompted the research questions in our working group: How do we evaluate the skill of models in simulating ice features? How
do we identify numerical model parameter space that produces realistic state of the ice? I will discuss an ongoing project that explores some potential methods for validating sea ice models.
First Observation of the Earth’s Permanent FreeOscillation s on Ocean Bottom ...Sérgio Sacani
The Earth’s hum is the permanent free oscillations of the Earth recorded in the absence ofearthquakes, at periods above 30 s. We present the first observations of its fundamental spheroidaleigenmodes on broadband ocean bottom seismometers (OBSs) in the Indian Ocean. At the ocean bottom,the effects of ocean infragravity waves (compliance) and seafloor currents (tilt) overshadow the hum. In ourexperiment, data are also affected by electronic glitches. We remove these signals from the seismic traceby subtracting average glitch signals; performing a linear regression; and using frequency-dependentresponse functions between pressure, horizontal, and vertical seismic components. This reduces the longperiod noise on the OBS to the level of a good land station. Finally, by windowing the autocorrelation toinclude only the direct arrival, the first and second orbits around the Earth, and by calculating its Fouriertransform, we clearly observe the eigenmodes at the ocean bottom.
1. Interannual and decadal variations of
Antarctic ice shelves using multi-mission
satellite radar altimetry, and links with
oceanic and atmospheric forcings
Fernando S. Paolo
PhD Qualifying, May 20, 2013
Scripps Institution of Oceanography
University of California, San Diego
3. Why do we care?
Ice-sheet mass loss Sea-level rise
4. Why do we care?
Ice-sheet mass loss Sea-level rise
Shepherd et al., 2012
Antarctica
Greenland
Glaciers
Ice volume
3 mm/yr (~1.8 from Cryosphere)
5. Why Antarctica?
The marine
Ice-sheet instability
Bed above sea level
Vaughan and Arthern, 2007
Increased discharged with grounding-line
retreat → unstable condition!
Fig. M. Helper
Data BEDMAP
6. Why ice shelves?
ice shelves are the
“interface” between
the ice sheet and
the ocean
Fig. Ice-shelf coverage
by satellite altimetry
missions
7. Ice-shelf buttressing
Compressive stress is a result of ice-shelf buttressing
Hughes, 2011
OCEAN GROUNDED ICE
Ice rise
Confining embayment
Ice rumple
Calving
front
13. Previous studies
ERS-1/2 1992-01 ERS-2/Envisat 1994-08 ICESat 2003-08
Zwally et al., 2005 Shepherd et al., 2010 Pritchard et al., 2012
9 years
50 km
Duration
Spt. Res.
14 years
One trend per ice shelf
5 years
30 km
To detect climate signals we need long and continuous records!
14. 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
15. 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)
18. The challenge of multi-mission
integration
Differences between missions:
– RA systems, orbit configurations, time spans...
Radar interaction with variable surf. properties:
ρs ( x , t )
ke( x , t)
– Surface density,
– Penetration depth,
Spatial and temporal dependent corrections:
– Ocean tide + load (for high lat)
– Atm pressure (IBE)
– Regional sea-level rise
19. 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
20. Averaging in time
Monthly
averages
Seasonal
averages
Time steps → 3-month blocks of data
21. Averaging in space
3 x
One month of data
~750 bins with 15 to 200 observations (for FRIS)
22. Averaging time series
82 time series per bin (x 2)
61,500 time series for FRIS (x 2)
Matrix before
Matrix after
28. Different corrections, different
results?
Different fluctuation and trend
Constant correlation
Variable correlation
Amplitude ts
Differenced ts
How significant are these differences?
30. 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
31. Do they measure the same thing?
First time this
comparison is
done in this way
32. Do they measure the same thing?
Envisat ICESat
We need an explanation for such differences!
First time this
comparison is
done in this way
33. Two ways of estimating elevation
changes
∂ h
∂ t Dh
1) Eulerian (fixed):
2) Lagrangian (moving):
Dt =∂ h
∂ t + u⋅∇ h
(t1) A (t2) A' B
A'-A = Euler B-A = Lagrange
37. What is signal and what is noise?
ICESat data are very noisy! How much can we trust?
Cross-over analysis Along-track analysis
Pritchard et al.,
2012
Two different techniques, same pattern → features are in the data!
43. Correlations, correlations...
Fig. J. Allen, NASA
Data NSIDC
What is the relation
to sea-ice variability?
– Sea ice protects ice
shelves by cooling
air temperatures and
dampening waves.
– Also affects mode 1
of basal melt.
Is there any relation to climate
Indices (ENSO, SAM, ZW3)?
– EOF analysis on h(x,t)
45. 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.
Editor's Notes
an ice stream entering a confined and pinned ice shelf. Shelf flow is from the ice-stream ungrounding line (heavy dashed line) to the ice-shelf calving front (hatchured line), with flow shearing along the sides of a confining embayment (half arrows alongside thick solid lines), around ice rises (half arrows alongside thin solid lines), and over ice rumples (full arrows across thin dashed lines)
Along an ice-sheet periphery, the ocean surface waters tend to be relatively fresh and cold (Fig. 2, C and D), typically at or near the surface freezing point. The properties of such waters typically are of polar origin and have only modest impact on melting beneath ice shelves. Below these surface waters, at depths typically ranging from 100 to 1000 m, there often resides a relatively warm and salty layer of water originating from the subtropical or subpolar regions (Fig. 2, C and D).
These warm waters have a large impact where they contact glacial ice, causing melting rates of orders of tens or more meters per year
(right) Vertical temperature and salinity sections (a) from the CTDs shown in the Fig. 1 inset and extended beneath the PIG and (b) along the PIG calving front, looking toward the ice shelf. Both panels show temperature in colour relative to the in situ freezing point, salinity by black contours and the surface-referenced 27.75 isopycnal and potential temperature maximum by thick and thin white lines. Open circles in b show ice draft above the ridge crest (black dots) beneath the PIG, from airborne radar and Autosub measurements11
Along an ice-sheet periphery, the ocean surface waters tend to be relatively fresh and cold (Fig. 2, C and D), typically at or near the surface freezing point. The properties of such waters typically are of polar origin and have only modest impact on melting beneath ice shelves. Below these surface waters, at depths typically ranging from 100 to 1000 m, there often resides a relatively warm and salty layer of water originating from the subtropical or subpolar regions (Fig. 2, C and D).
These warm waters have a large impact where they contact glacial ice, causing melting rates of orders of tens or more meters per year
(right) Vertical temperature and salinity sections (a) from the CTDs shown in the Fig. 1 inset and extended beneath the PIG and (b) along the PIG calving front, looking toward the ice shelf. Both panels show temperature in colour relative to the in situ freezing point, salinity by black contours and the surface-referenced 27.75 isopycnal and potential temperature maximum by thick and thin white lines. Open circles in b show ice draft above the ridge crest (black dots) beneath the PIG, from airborne radar and Autosub measurements11
Arrows highlight areas of slow-flowing, grounded ice
Accelerated ice discharge from the Antarctic Peninsula following the collapse of Larsen B ice shelf
Ice velocity, V, in Jan. 1996 (black square), Oct.
2000 (red square), Dec. 2002 (blue triangle), Oct. 2003
(green triangle), Dec. 2003 (yellow triangle) vs distance, D, from the grounding line along profiles in Figure 2. Surface elevation (meters) from CECS/NASA in (b –c) and InSAR in (a) are thick black lines. Bed elevation (meters) from CECS/NASA are thick black lines in (b). In (a –c), bed elevation deduced from ice shelf elevation assuming ice to be in hydrostatic equilibrium are dotted black lines
Three inter-related steps independently publishable
Say something about IMBIE comparisons!!!!!!!!!!!!!!!!!!!!!
Peterman Glacier: 80% of the thickness is removed by basal (5% by surf.) melting when it reached the ice front.
Peterman Glacier: 80% of the thickness is removed by basal (5% by surf.) melting when it reached the ice front.
Explain how. Frontal and full-ice-shelf time series.