Pre-PARCA Greenland Surface Mass Balance (SMB) Meeting
Monday, January 26
8:45 AM – 5:30 PM
GSFC Building 33, Room H114
08:45 Welcome
Tom Wagner, Charles Webb, NASA Headquarters
Thanked participants for attending. The idea behind this meeting and the theme are as
follows: Are we making the right measurements in the right places in the right way?
The community came up with this idea as many are focused on SMB and measuring on
the Greenland traverse. Probably spending nearly @2M a year on this alone in Greenland.
So, we want to maximize the return on this.
State of the Program:
NASA’s Earth Ventures program is somewhat new and is doing focused cost-constrained
measures. It is a combination of satellite and aircraft missions. OMG is one of the
missions and it is looking at changes in the ocean and changes in the ice sheet.
ICE-SAT 2 nearly complete and IceBridge is in the budget until 2019. It will nearly be a
decadal mission.
NISAR and GRACE-FO are on track; SMAP will launch soon – a large footprint radar with
complete maps of the Arctic every few days.
Budget-wise for NASA in the year(s) ahead looks pretty good.
The cryo budget is pretty strong--grown by about 300% in last six years.
The Decadal Survey, which recommends what ESD should do, will begin soon. It should
look different this time around and will identify what are the big Earth Science questions
and how should we address them. SLR was used as the key example they will use as
guiding emphasis for the report. In early February, we will hold webinars on new
technology that could be useful to the survey process.
Even with all of this, there is still some money around.... So, if you have ideas, keep that
in mind.
Goals of the Meeting:
Are we making the right measurements in the right places in the right way?
 Point of this meeting is for us to talk and figure out where the synergies are and
how we are making best use of what is out there and what we need to do next to
ensure meeting the right scientific needs.
 We will take notes and follow up (with $s)
Introductions made by participants in the room
Additional goal:
There will be a 30 minute discussion during tomorrow’s PARCA meeting to provide our
feedback from the presentations and afternoon discussions. We will want to report our
recommendations. During the discussions today, we will come up with a summary that
can be presented plus a set of questions that can be considered by tomorrow’s meeting
participants. This will enable concerns and needs to be presented among people who
don’t typically connect.
09:00 Overview of GCNet and new measurements needed for SMB in Greenland
Koni Steffen, CIRES/University of Colorado at Boulder, Swiss Federal institute of
Technology
20 years of PARCA meetings (1995 – 2014).
Logo representing elements used (remote, airborne, modeling, and in situ) have not
changed. Haven’t resolved all elements of the model within the circle.
Model of Mass Balance slide with processes in middle and mass gain and mass loss
schematic hasn’t changed for 20 years even though some elements are better known.
SMB=P- R- E
Precipitation is about 500 to 640 gigatons (Gta)
Runoff is about 250 to 350 Gta
Ice Discharge is about 320 to 420 Gta
Negative balance is about 50 to 200 Gta
First ten years of activities focused on ice cores because we needed to know how much
precipitation we had; aircraft tracks as well. There was an emphasis on shallow ice cores.
First meetings focused on coordinating efforts!
After five years, published a special issue in JGR – 29 papers, to summarize results.
Through 2005, have nearly 140 papers. Discuss whether we are ready to make a new
special issue in a peer review journal. This could culminate in a science meeting next
year.
GIS Cumulative Mass Balance: Saw major change during creation of PARCA going
forward. Currently, 7 mm sea level contribution from Greenland.
Greenland Climate network (GC-Net): 1995 started to put up a network to understand the
climate. Reduced number of stations. We wanted to understand how much was melting
and why.
32 parameters can be downloaded on a daily basis (measure hourly). Tries to keep the
network the same over the last 20 years. Basic parameters are the same.
Map of sites covers 2000km N-S. There is a dense network in the middle. 2000m
elevation (not as many in the low altitudes?)
220 data users per year. Cires.colorado.edu/steffen/gc-net. Users are diverse. Most are
ice modelers, but not all. Receives many student requests.
Data displayed within 15 minutes after it is collected. Data transmission is free (no charge
to NASA).
Swiss Camp Climatology Station: Image shows that in two seasons, lost 3 m of ice.
Mean Annual Temp is about 1.3 c per decade (with strong variability) between 1990 to
2014.
If look at seasonal air temperature variability, summer has continued to increase; winter
and fall are closer to each other.
We are finding advection of warm air over the ice.
Surface Balance Mass – It was stagnant with no ice loss, but lost 12 m of ice since we put
up the station. Essentially, observed ice loss beginning in 2002. The camp has collapsed
twice since then. 2012 was a big melt year.
Net radiation (how much energy received and lost) – 1993 to 2014 – reflection high due
to snow, but albedo low in the summer. There is surplus energy either warming the air
(which can’t do on a cold surface) or you melt, which is occurring. If it is 1 surface
albedo, all is reflected. Below 1 is standing water in ice. This feedback creates more
melting.
In 2012, there was melt all the way to the summit. Surface Baseline Radiation Network
measured and explained that if have 0 degree at summit, the sun comes through semitransparent cloud. Long-wave is reflected back and only with that got temperature to 0.
If you have no clouds or completely overcast, you wouldn’t have reached melting
temperatures. These “melting” clouds are becoming more frequent in our warming
weather.
Passive microwave can track the melt extent – graph of 1979 to 2013 showing
culmination in 2012 of melt. Have continuous increase and over 34 years have a 2%
increase/year.
Migration of Equilibrium Line Altitude 1996 - 2012: Latest ELA was 1700 m, this means
ice sheet impacted by additional melt. Can calculate how quickly it will move to the
summit under current conditions. Was calculated to be 2028, but 2012 was an extreme
year.
High resolution satellite imagery can map where the ice margin has retreated. Can even
look at the consistency of the lakes and the surface height change.
Surface height changes are measured in 18 different locations in Greenland.
There is an intercomparison project that WMO is undertaking, yet all measurements are
different. These snow drift measurements are being made in the Alps.
New solutions undertaking:

In 2015, will install two new radars one at 1600 MHz and another at 600 to 1600
MHz. Can see the internal layers. Hopes to show validation from three seasons at
next PARCA meeting to demonstrate what the radar can measure at several sites.

Scatterometers providing change in percolation. Percolation has increased over
time.

Surface features: water is disappearing in the summer. On the way down, the
water changes the physical behavior of ice by warming and cracking. Warming in
the center occurs when double the melting. We need to go through the ice sheet
and through the water conduits and put sensors down there.
Conclusion: what is missing and what we need more of:






Temperature is well assessed and we use the data for the modeling and
Snow extent we know through passive microwave
We don’t know the AMOUNT of melt. Energy balance models are not as
accurate for this.
Snow height change is access to access, but don’t know accumulation. We
need a better understand of this as this is the big number in the equation.
Snow compression and percolation is important and we need more sensors.
Snow radar can measure percolation.
Surface radiation balance could give insight in SMB.
Discussion
Question: With SMB question, where are we trying to get to? Get an understanding of
small things for better reanalysis products or a network for a state of the ice sheet?
Response: End game is combination of the two. GRACE can deliver something with higher
resolution. Then, we can put that into our equation where the uncertainties are. Each
component has a large uncertainty right now. Sea level curve placed in the IPCC report
has a large uncertainty of +/- 15%. No direct answer yet.
Comment: GRACE won’t provide the process even if it provides the answer. GRACE data
won’t solve everything.
Discussion on Comment: We see a new process we didn’t understand before every few
years and this helps us realize what new measurements we need. We probably won’t ever
know all the processes because with increasing melt, new processes are occurring.
Accumulation is needed, but runoff seems more uncertain. Precipitation is well modeled
but runoff isn’t because we don’t know retention. Largest uncertainties are important.
Comment made that some modelers might disagree with how well precipitation is known.
09:30 GEUS perspective, interests and suggestions
Andreas Ahlstrøm, Geological Survey of Denmark and Greenland (GEUS)
What trying to do:
Quantify current mass balance
Explain how and where mass is lost and gained
Understand why:
Build predictive models based on this understanding
Tell people what we do and why we do it.
List of Danish activities:
 Ice coring
 AWS network
 Velocity
 Elevation/thickness (using NASA airborne and satellite data)
 Total mass
 Regional climate modeling HIRHAM5
 Meltwater retention
 Albedo
 IS Model development and use
 Fjord bathymetry
 Recent ice sheet history
 Database
 Outreach
Program for Monitoring of the GIS (PROMICE) -- focused on mass loss with 23
stations:
 provides pressure sensor ablation observations
 doing validation v melt water discharge in the catchments outside the ice sheets
 mass loss from iceberg calving (flux) – done in August so can combine NASA
flights with their flights
 InSAR ice velocity mapping
 Dynamic mass loss exercise combining products
 GPS tracking of outlet glaciers
 Mapping ice extent (nearly completed and highly accurate)
 Historical surface mass balance records with earliest measurements in 1930s.
Question: In situ measurements: Does it include aerial photography?
Answer: No, this is only surface balance measurements. Compiling data archive.
Question: Is this compiled in gridded boxes?
Answer: No, just being compiled now. It is not integrated into... x. Many
measurements haven’t been used yet.
 RCM: Greenland at 5km resolution. HIRHAM5.
Question: Will names change?
 Exercise comparing different models and comparison is out and published
 Polar Portal collaboration: Trying to put all information (often real-time) placed
online for all to see. www.polarportal.org. SMB is calculated daily by Met Office
and placed on this site. It resembles NSIDC site in some ways. Greenland and
Arctic sea ice are two main components. Some of it is based on NASA satellite
data.
Field campaigns
 AWS
Question: Are there stations on same transect as the Dutch?
Answer: Yes, it compliments. Sometimes doubled, but it strengthen the key
transect since it is an important location. This is supplementary to PROMICE
network.
 Meltwater in the Kangarlussuaq region chart: Has large uncertainty. What is
going on is not well known and it is changing fast. Must better understand this.
 Firn cores information coming out of this work
 Thermistor strings
 Using radar to better understand the layers
 Swiss instrument, the snow pack analyzer. Four bought and placed on ice
sheet. Risky. If they work, the antennas give ratio between dry snow, water in
the air. Have ongoing densification and retention process over time if it works.
It doesn’t work yet. Idea with this is to have it in ablation zone too, if possible.
That will tell you what is happening with snow in terms of accumulation. This
will capture processes in this year’s snow.
Response: There is a new device now it was noted.
 Q-Transect in the south: Running measurements since 2001; have weather
data stations; snow pack analyzer station there too.
 UAV and ground studies of bare ice reflectivity in the k transect. Getting albedo
measurements to compare to MODIS product. Using MODIS data for change
between 2000 and 2012 time period. Mapping the ELA too.
New/Additional
 RETAIN: predicting nonlinear change in the permeability of Greenland firn is a
new project.
 August 2015 flight campaign hopefully with a hyerspectral imager in addition
to laser altimeter. This flight can take more data, possibly.
 GRISO Network: Looking at ice-ocean interaction. Want to expand more
internationally. What to get GrIOOS system working. Target is about 10
glaciers. Ocean is having a huge impact on the ice sheet and must better
understand that. Will take advantage of existing networks. Want the critical
oceanographic atmospheric and glaciological variables at long-term in situ time
series. Long-term! Compiling existing bathymetry in collaboration with others.
 Military data? Did he say?
Suggestions: PARCA Revisited
 All are interested in mass balance but no one is measuring it. Need whole
flux gate to get the full picture and this is trying to get at small scale in situ
mass balance to get full mass balance
 1960 to 1990 reference period is not balanced in the interior.
 Need to get beyond ice sheet-scale comparison between sat products,
desirable to make comparisons with in situ mass balance characterized
similar scales
 Build on GCNet sites to get interior mass
 We need mass balance measurements of the ice to compare with the
satellites.
Priorities:
Boundary conditions for RCMS and ISMs
 Fixed: bedrock and bathymetry
 Moving: elevation, velocity, surface climate (precipitation, radiation, turbidity
fluxes), albedo, meltwater penetration, ocean forcing
Improved model physics through observations:
 Targeted in situ campaigns for process understanding
 In situ-validated satellite products
Discussion: Held for now
Measurement Links to Climate Models
This session will focus on links of measurements to climate models with three
presentations. Guiding questions should be kept in mind.
Guiding questions:
•
•
•
•
What
What
What
What
kinds of measurements do the models need?
are the limitations in existing measurements?
are the improvements most needed?
does the community expect from these models?
10:00 RACMO
Jan Willem van de Berg (via Skype), IMAU/Utrecht University
Brooke Medley, NASA Goddard Space Flight Center
Will discuss briefly what measurements for us will be taken and what can be contributed
to the community. Work with the RACMO model. Focus on Mass Balance and Firn Process
because if want to understand SMB, have to understand what is happening in the snow
and in the boundary.
First, the High SMB Observation graph, percent plot of P-E Greenland. Image of
observations we have. Not much on precipitation, few with precipitation over 1m. Then,
we can see what models are predicting (MAR and RACMO). RACMO has high
accommodation sites – it is not that that statement is not true, just not observed. Sites
with High PE are undersampled. This is the gigaton uncertainty we of spoke before
Modeled SMB for last nine years at 2.2 km and see ablation zones....
Wish: Need surface height mass balance elevations
We also need ablation measurements. We want to know the energy balance. If you know
the processes, you know whether you are right or wrong and can adjust where wrong. If
we understand the processes, we can get it right. We can reproduce what we see and
predict what might happen to know if future change different than what we have seen to
date. Understand processes in the field to improve the model. In lower ablation zones,
area is not homogenous, but the model thinks it is. Need albedo measurements and
satellite observations to address this.
What also want is: we processed mean surface mass balance, but it varies from year to
year with changes in ablation and accumulation. Change in ice doesn’t move as fast... We
have to model something right Short-term mass balance changes. So need observations
to compare with our models. If we could have something for Greenland similar to what
we have for Antarctic to validate our models that would be helpful.
Question: Precipitation behaves differently in terms of component of SMB, so you will
discuss runoff later? You discussed ablation and you want interior something. What is the
important driver of interannual variability of Greenland of SMB? Precipitation is more
important than in Antarctic.
Question: which component is most important to SMB in Greenland?
Answer: Using next slide to answer:
SMB Prior 1950:
RACMO reanalysis of SMB, Total Precipitation, and Runoff. Which component more
important to variability? Precipitation is a bit more important than runoff. For some years,
the runoff is very variable. You need both to see it. Easier for regional climate model to
get variability in melt, but if miss something in interannual variability and variability of
precipitation, it will be harder to capture than ablation. Graph is lower than the numbers
before, the simulation is very dry and see change in precipitation in 1940. Trying to figure
out if it is an example of a warmer climate. It might be that it could just be another
artifact. Not sure if it is realistic. Maybe we can never get SMB prior to 1950 because
there are too few observations of weather to constrain the model. Want to answer this
question, but it is a limitation of the model.
Subsurface processes:
Firn compaction: most models use empirical formulation, but this isn’t ideal because you
put in a rate before the simulation start. If you want to see how firn changes over longer
periods, it isn’t that good. Need a process that gets at firn layer and grains – heat and
moisture diffusion can lose grain’s roundness. Helpful if more understanding of what is
happening in the firn and how it gets denser. Need observations to constrain that in our
model.
How is meltwater moving around in the snow? Hard to measure this. It isn’t homogenous.
Not representative in one area for global scale. Observations of what meltwater is doing
in the snow will help improve the models. Want to know what is leaving the ice sheet.
Transition zone has much re-freezing, but other areas don’t have this.
Summary:
Can model SMB, but
 SMB/SEB climate observations needed for evaluation/enhanced confidence
 SMB prior 1950 will remain uncertain
 Subsurface processes are tricky
Discussion:
Question: Accumulation increase on SE coast up to 9m. K had a station there and saw
accumulation reduction to 1 to 2 meters due to wind strength. Be careful if go there.
Answer: That is the complication. Those are the areas hard to observe, but that is why
need to go there. We are underestimating snow drift, but whether model does it
accurately is question.
Question: Your model doesn’t add the katabatic wind inside?
Answer: Yes, it does plus also snow drift.
Question: Physics for the wind? That requires detailed resolution, hence a limitation.
Answer: The more heat hits the surface, the more difficult it is to get it right.
Question: You said that ablation is not a big issue, but if you look at ARACMO, a larger
source comes from run off. So, there are different processes and you can model total
runoff. From what I understand, what is largest source of uncertainty when you model
this? Albedo, energy balance, retention? Because which measurement is most useful and
which is largest source of the error in the runoff?
Answer: Observation of ablation, then you check the whole process. Most ablation occurs
on edges where minimal snow accumulation in the winter occurs, so refreezing isn’t
important. Refreezing is important in the intermediate area. Highest amount of runoff
comes from the edges so refreezing is less important. This is a combination of albedo and
surface energy balance. The latter is important because if you don’t get amount of melt
energy right, then you get to the wrong numbers. Need good observations of albedo and
the check that with RACMO. If include ice albedo from MODIS... cut off
Participant added that the problem could be solved with runoff record of 40 years from
catchment in West Greenland. This cold test the model.
Additional response: Maybe it could in the southeast. Combine with catchment balance.
10:15 MAR
Marco Tedesco, National Science Foundation, City University of New York
Report card of MAR:
Fully coupled atmospheric/ice model
15 to 5 km work
Daily resolution
Surface model: SISVAT
Snow model: CROCUS
MAR History
Running since 2009
Can look at variable and plot of interest.
Open source
Variables of interest v variables produced slide
Second slide of outputs v measurements variables
** Some can model relatively well, but others can’t really do it all or validate.
What is needed?
SMB:
Along eastern coast where max of precept occurs
Along west coast to complement K-transect
Spatially distributed measurements
Density Profiles:
Along percolation zone to characterize the depth of permeable layers (helps convert xx?
into mass)
Albedo:
Melt onset, snow/firn
Bare ice (for parameterization)
Impurities evolution (from scaveaging) this isn’t in the model now, but can have a large
impact on melt.
Runoff and liquid water content:
Systematic measurements of runoff and LWC at selected locations along west Greenland
(we have no direct measurements now). We are coupling MAR scheme with hydrological
scheme to understand assumptions hydrologists are making.
Limitations in Measurements:
 Existing in situ measurements are spatially too sparse – point measurements vs
area estimates (scaling issues)
 There are few in situ measurements of accumulation
 Little or no in situ data on density
 In-situ albedo measurements on at a few selected locations
 No continuous or seasonal runoff measurements on the ice
 No measurements of percolation and refreezing

Remote sensing estimates do not provide LWC
Improvements needed:
Increase measurements in some specific regions to improve our knowledge of
spatial/temporal variability
In terms of simulation we need:
Hydrology, runoff and albedo in the ablation areas
Accumulation and precipiation along the coast
Densification
In terms of observations:
We need spatially and temp distributed in situ measurements of SMB, density,
accumulation, and surface albedo for evaluation and model improvements.
It would be great to have a system or vision to start understanding:
MAR v RACMO difference is that it is open source code. RACMO is proprietary. Research
questions are driven by availability of data. We wanted to drive our research questions
and then run the model. We can modify MAR and that is an advantage for the
community. Might create a summer school to teach MAR.
What does the community expect from the models?
 Spatio-temporal variability of quantities that are not or cannot be measured over
the entire Greenland on a daily or monthly timescale
 Complementary information to observations for studying processes
 Large-scale SMB estimates for differentiation between ice dynamics vs surface
losses
 SMB and SLR Projections (forcings of models are part of uncertainty)
 System-level understanding (Melting is underestimated due to atmospheric forces
is an issue)
 Easy access to data and analysis tools (3.2 MAR data is on the web plus will have
another satellite or two data available on the Amazon Cloud)
Discussion:
Question: How much does it take to build xx? to run MAR on the web?
Answer: One full time person can do it in two weeks.
Question: Hurdle is to install the code and get familiar.
Answer: If you know the right person, they just translate dataset into a database and use
Python.
Question: To test something at 2am is the issue.
Question: We need to do better job from forcings to snow metamorphism. If trying to get
at IS balance and SLR, what is more important?
Answer: For direct SMB measurements, accumulation SE; albedo for xx?; for projections,
atmospheric forcing and albedo is key. Can get IceBridge data, but can’t get mass. So,
have to convert everything into mass from density models, but need to know the
uncertainty. Is it from depth? Need a better albedo scheme. Density measurements for
improving now that translate elevation to model and accumulation to understand model
discrepancies. We need to be careful when we run things for the future. If we have
different atmosphere forcing, that can affect the model. We lack manpower to do this
right now.
Question: large uncertainty for runoff. We don’t know how to model processes like
meltwater retention. Uncertainty could be energy balance, water retention, and the
albedo. So, largest uncertainty is the albedo and the atmosphere?
Answer: Yes, because bare ice zone moving up elevations and albedo feedback comes
more important. For ablation zone, bare ice melts faster than in the past. For southeast
portion, for total mass balance, if you have accumulation in SE, it balances loss in west.
But you still have lots of loss. For direct runoff for new state of melting in past 10 years,
bare ice and ice albedo are crucial. If you want to look at future and densification and
elevation changes from Icebridge, density is the variability.
10:30 GEOS-5
Sophie Nowicki, NASA Goddard Space Flight Center
Mass balance produced by GEOS5 at 7km images. With GCMs, capability of fine-scale
resolution of ices heets shows details
GEOS5 Climate Model Description provided: Includes ocean, sea ice component, ocean
biogeochemistry component, atmosphere component, land ice model, and radiation. To
understand SMB, have to include these various elements. GCM can do this.
GEOS5 can be used in various ways: MERRA is an example; simulations (atmosphere
and coupled atmosphere and ocean simulations). Used to tell ARISE aircraft where to go
and contributes to the Sea Ice Outlook.
GEOS5 includes: condensation, meltwater percolation, refreezing, albedo based on snow
density. It has a new snow model.
It computes on an ISM mesh. GCMs typically have fixed heights and then downscale to
ice sheet models causing errors. With GEOS5, avoid this with elevation tiles.
How does it compare to regional climate models? Compare to MAR and RACMO. GEOS5
picks up additional features. Also compare it to a climate model (the IPCC model
simulations) and it gets to more features that other models don’t get.
What kind of measurements are we using? In situ observations: Temperature v depth
to make sure catching the right seasonal variability; and remote sensing
observations: how many melt days are there? Need to compare different models as
each has strengths. Remote sensing can’t tell us how much runoff there is, so looking at
that through reanalyses.
Looking ahead:
Improvements
 Firm model
 Better snow model based on grain size, radiative properties of snow pack and
blowing snow
 Treatment of albedo: considering MODIS-based bare ice albedo and working on
aerosol deposition
 Water routing at surface and meltpond
Measurement needed for this?
 Data for what is noted above
 More in situ energy and mass balance measurements
 Atmospheric properties such as clouds, winds over ice sheets
 Ocean observations such as salinity, ocean color, and sea ice
Keep this in mind during discussions (these are being debated currently in the work):
How best compare models and observations at different spatial and temporal resolution?
How much of an error is acceptable? When validating the model, within a footprint of
50km or 100km a typical GCM scale, what are the errors that come into this?
10:45 COFFEE
☕
Current In-situ Measurements
Guiding questions:
•
•
•
•
•
•
•
•
What is being measured?
What are the driving scientific questions?
Where are the sites located?
Where is the data?
Who are the users?
How long is the record and how long should it be sustained?
Limits to Greenland-wide extrapolation?
Key results
11:00 Hydrology network
Larry Smith, University of California Los Angeles
River discharge, especially from Greenland rivers and on top of the ice sheet, may be one
of most underutilized and important measurement to collect. This is for outflow to the
ocean and to understand the complex processes that are occurring. This is what is done
on land. River gauges help infer a better understand what is happening.
Measuring small catchments along the edge and a larger river. Collecting in situ discharge
rates to produce a lengthening time series of data. Also do short campaigns on top of the
ice measuring discharges and the hydrolics of melt channels.
To address subglacial storage, these measurements are helpful. Outflow at edge versus
runoff have discrepancies. Outflow (runoff is a meteorological calculation on top) is a
critical issue.
Asiaq has gauges measuring outflow. UCL has gauges too.
For supraglacial measurements, there are few.
Stream river systems are important and huge volumes of water are passing through
them. They extend inland. Need to talk about how water and heat into ice sheet could be
incorporated into ice dynamics model.
Asiaq records are relatively long, although some are spotty.
Key result: studies suggest the core assumption that SMB runoff = true outflow
to ocean is not always valid
Idea to expand this:
One-off sampling over large areas (not permament). Drag an ADCP by a helicopter and
go after veracity of the graph of how early in June, outflows are too. Fly up western side
of ice sheet in June and then again late summer. Suggests water retention in one of most
melt prone part of ice sheet in extreme year of 2012. It should be well-drained. If there is
retention, is that happening elsewhere in ice sheet? Hit to hit the 15 largest rivers
draining into the ocean.
Discussion
Question: Is there any possibility of uncertainty in basin delineation is causing those
graphs shown?
Answer: Yes, watersheds are frought with uncertainty.
Question: How do you determine those?
Answer: Use two different methods, different resolutions, and different DEMs. One
positive thing about this delineation exercise is that most error breaks down where
minimal melt is taking place. More convergence of the approaches occurs where melt is
taking place.
Question: Are ice drainage basins different than the other basins?
Answer: Yes
11:15 Snow scales
Ian Howat, Ohio State University
Trying to get ice sheet mass balance from altimetry. Firn column contracts and gets piled
up with more snow and we don’t know the density. Need other terms of accumulation and
put together with altimetry to get an ice column density. Approach here is what can be
put in situ to correct the altimetry measurements.
Use a potentiometer anchored in firn, use a sonic radar, and SWE sensor. Then, can get
some of the terms to close the budget.
Developed the CRAGS System – tower to place
Question: Do you add temperature to test other sensors? Yes, plus a barometer.
We are trying to focus on simplest thing here to solve the equation.
Had to solve issue for snow pillow: snow scale that is 3m by 3m
Comment: Snow radar is only $12,000 made in Italy
Question: Does it come apart?
Answer: Yes, sections snap apart. Digging them out later is the hard part.
Set up a prototype set last summer. Trying to separate out internal vs surface dynamics.
Data is still transmitting. Gives compaction data. 80% to 90% of compaction occurs in
upper 1 meter.
There is a need for a melt time series to understand firn processes. Model’s timing of melt
is very different between MAR and RACMO.
Snow scales: Expensive and hard to get out. Cosmic rays is an alternative. Can measure
the account of them and more water stacked on top, the more attenuated. Doing a
demonstration in 2015. It is a tube.
Future Plans
Other two Crags will deployed. Where should they go? Would like input on this. One near
GCNet site in NW and another one in SE because dynamic thinning is strong there.
Discussion:
Question: How long will snow scale last?
Answer: Rated up to 2m of snow equivalent. In SE, a winter probably.
Question: How long do the cosmic ray tubes last?
Answer: That will be a test. Salesmen says as long as you want, but accuracy decreases
over time.
Question: How heavy is it for one tower?
Answer: Small lithium battery. Snowscale itself is over 100kg and most of the weight.
Don’t have a full measurement of weight. GPS going out with it is way more weight than
the actual tower.
11:30 Firn compaction
Mike MacFerrin, CIRES/University of Colorado at Boulder
Overview of what doing on firn compaction.
Enhanced melt and refreezing is a crucial uncertainty element.
University of Washington ran a Firn Model Inter-Comparison Experiment. Results show
models vary greatly. Depth-Integrated Porosity varies by 7 to 8 meters in steady-state
and then reactions to climate change vary by up to 3 meters in either direction.
They have FirnCover Stations, each with 4 to 7 boreholes at depths of 1 to 20+meters.
Putting them close to GCNet and PROMICE stations. There are 3 stations now and putting
in 5 more in 2015. Set up to get climate coverage, not necessarily spatial coverage to
force the models.
CFM integrates nine published models into a single code framework
Features: portable, trackable, modular, extensible, and free
Results:
Seeing huge dependence of compaction on firn temperature. Lag between when melt
occurs and compaction. July is the slowest compaction month. Have to account for melt
of previous years to get at compaction. Melt and refreezing is heterogeneously spaced.
Another complicating factor is melt, refreezing, and runoff.
Perched lens issues – need measurements to be comparable.
Further Work
More directly study/constrain spatial variability in firn compaction
Monitor winter meltwater movement with year-round GPR installations
Koni added that we are using the sensitive driller that oil companies use and calling it
Micropen. It measures every millimeter of density. Measurement takes 15 minutes.
11:45 SUrface Mass balance and snow on sea ice working groUP (SUMup)
Lora Koenig, NSIDC/University of Colorado at Boulder
Accomplishments:
Working group to bring people together in remote sensing, modeling, and field work.
Compiled a community dataset with one million data points.
Came up with 8 questions and what each group needed.
Data: Have four ice cores under OIB lines in Arctic, but no data provided under
Greenland. Need to discuss this.
3 datasets: accumulation, snow density, and snow depth over sea ice
Recommendations:
Quantify supraglacial water volume and englacial water retention
Additional Greenland ice cores spanning past two decades
Improve melt ponds over land and sea ice
Future:
Goal is to make field measurements as consistent and easy to use as modeled data.
Dataset needs to be interagency so it needs to be simple to submit the data
Needs to hold original and aggregated datasets
This is data and science mixed together. Need to set what the standard format would be.
Need densities can’t just use depth.
Discussion:
Question/Comment: Discussed similar issues in Switzerland. You can get a DOI for your
dataset and then PIs can get credit for it.
Question/Comment: NSF has activities focused on this topic, not just polar. EarthCube
and other programs have that infrastructure building blocks. Christine Leonard at Lamont
looks at this to. One way to move along the work is to look into the suggestions of the
other geosciences groups.
12:00 LUNCH

Remote Sensing of Surface Mass Balance
Guiding questions:
•
•
•
•
•
What products are available?
What are the limitations?
What in situ measurements are needed?
Is there hope for an improved product?
Key results
12:30 Greenland surface hydrology
Derrick Lampkin, University of Maryland
Water can proliferate through various mechanisms (lake drainage, partioning of water
through channelized flows). Largely not impactful on primary outflow glacier dynamic
because don’t get super glacier channels that form. But one part of Jakobshavn, you see
pondage and drainage and that is along sheer margins. This has been area of intersection
between outlet marine terminating glaciers and super something.
Saturated crevasses at margins of Jakobshavn is focus of work. Looking at the impact of
squeezing water into a very critical place.
Seeing rapid speed up and thinning. Two key issues are: Terminus instability and Melt
water infiltration.
Spatial temporal variability and extent of these crevasses. Mapped them into regions.
During the progression of melt season, potential importance is that it is ponding in melt
season in critical part of ice sheet. Think ponding areas could play a role in influencing the
observed speed up is able to promulgate upstream. Plus an ancillary effect that
weakening of sheer margins can play.
Transects across the water areas: water saturated areas are each different and locally
derived. Like children.
Strong basal topographic control in water saturated regions. Facilitates capture of
presumed surface runoff. Will try to constrain degree of filling of saturated areas driven
by in situ melt by solar radiation or water that is being brought in by channelized flow.
Likely hard to get water from outside margins, I think that is what he said....
Peak extent reached in early July followed by sharp decreases. Characteristic variability
in how these things behave. This was done in 2007.
Looked at the relationships between filling rates and changes in elevation across sheer
margins. Filling rate lower and lower elevation assumed. Much stronger local control on
how meltwater is routed or produced. If in situ melt is responsible for filling rate, aspects
ratio drive some of the noncharacteristic behaviors. If meltwater input from outside sheer
margins, cold be impact of regional variable of water routing. This must still be
determined.
Looked at drainage rates: ice sheet dynamic changes are one of the drivers. This is the
case for many saturated systems, but doesn’t explain all variability of drainage rate
magnitudes.
How much contained in the saturated region, how it drains, and how much it drains must
be understand to understand the impact. Use a radiation continuation model to get at
depth estimate, but isn’t as precise and can only go so deep. Deployed another method
too, providing total volume contained in largest saturated features. Found drainage is
similar to large amounts being squeezed into sheer margins.
How does it matter/does it matter?
 Squeeze water into sheer margin, reduce capacity for sheer margin to take up
stress, you increase ice melt. Degree we can assess this is part of an upcoming
analysis.
 Sheer margins are primary point where marginal ice being driven into ice stream.
Sheer margins are controlling mass flux. Lubricating the sheer margin could
increase mass flux.
 The impact that water can have being squeezed into sheer margins can weaken
the sheer margins in two ways: enhance basal sliding; and storage of water in ice
column could refreeze and change ice viscosity.
 All of this not well established yet.
Question: Why do we struggle to separate the two processes when they are the
same? We are always looking at the bedrock versus 3-D volume process. They are the
same.
Answer: Maybe it is harder. We lack the data structure to resolve that at the temporal
and spatial scales they are occurring. We don’t have the models to do this. ISSM is a
platform that could help resolve some of these process. Understanding vertical water
routing is a key issue for this and needs to be understand. How routed, how much
goes to bedrock and how much stays must be understood.
Extra marginal impact could occur in this way:
If you weaken the sheer margins, you can increase mass flux across sheer margins,
propagates upstream. Spatial extent and upstream impacts amplified. If look at lines
where transferring ice, can effectively draw ice from further away by lubricating the
bed at key locations. This dilates the catchment area where Jakobshavn can suck ice
into the main trough. This could be squeezing meltwater into sheer areas.
Tried to explore this in one manner, but couldn’t get it published. Looked at different
scenarios of water pulses. Model results indicate that more water stick in and the
larger you do so, nudge mass flux rate into a new steady state that has an increase of
volume discharge at sheer margins (did not capture this information correctly from
the speaker). This might not be true for all areas, however.
Now, trying to understand what happens inside the trough by squeezing meltwater
into sheer margins. Do you enhance basal sliding or weaken the ice? Results
explored temporal variability and the lateral drag at key transects across water
saturated areas. Found that where not water saturated crevasses, you get stress
loading or increase in lateral drag. Where have transects that cut across where water
identified, something opposite from previous sentence. Probably stress loading sheer
margins. Much spatial variability that sheer margins can take up that stress.
Looked at viscosity vs basal friction. Do see interesting spatial variability but need to
tease out more regarding contributions.
Conclusions:
 Rate of increase – whatever happening in meteorological context, we are
getting more meltwater production.
 Want to look at relationship between near terminus dynamics and upstream
drainage. How much speed up and thinning can be explained by one process or
the other? Probably inter-related with feedbacks.
 Basal drainage network and response to melt water input from shear margins.
12:45 IceBridge snow radar and applications
John Paden, University of Kansas
Lora Koenig, NSIDC/University of Colorado at Boulder
Ultra-Wideband radar is what looking at here.
Being used on aircraft, but had to solve chirp problems.
Use a chirp generator
Radar instrumentation is: MCoRDS/I; Ku-Band Altimeter; Snow Radar; and Accumulation
Accumulation instrument can show information in the layers
Snow radar shows deeply into the ice sheet and matches well with actual accumulation
Future Work:
Will try to combine two systems into one. One will operate with one chirp, doubling
resolution. Also, increasing transmitters and receivers.
Want to enhance bandwidth for MCoRDS/I from 150 to 600 MHz. Provides better
resolution.
Want to develop a Temperate Ice Sounder instrument and a Sea Ice Sounder to look at
sea ice resolution directly.
Conclusions:
Advances in RF and digital technology are helping us to develop low-power and high
sensitivity ultra wideband radars
Question: With the replacement of accumulation radar with ultra wideband, how much
thought put into being able to make measurements comparable between the two
instruments?
Answer: This is something to think about. Replace is too strong. Table show the antennas
developed and haven’t flown the accumulation. Not taking anything away. If can upgrade
P3 antennas, we’d want to fly both systems so we can see how to interpret both
frequency ranges.
Lora Koenig
Water retention and accumulation rates monitoring.
Water retention: near surface radars show annual snow layer and how lakes are not
refreezing. Able to see water retained in buried lakes, not a significant amount. Mapping
buried lakes.
Don’t know anything about volume in water using radar. Not a problem with radar, but
need to know water volume measurements in the ice sheet along with density
measurements.
In 2015, going to drill four cores and take seismics, electromagnetic measurements,
radar data, and one other type of data (could not catch this). Radar data shows changes
in the aquifer. So the difference between two dates in time can be shown.
Traced all radar layers and can make maps of snow accumulation rates. If have ice cores
that give densities, you can make these maps accurate. When compare to MAR, snow
accumulation is low compared to MAR because of densities of MAR.
Guiding Questions:
 Quality of radar sufficient
 Can improve spatial sampling in areas with water retention
 Need improved/increased near-surface (top 20 m) densities modeled/measured.
Discussion:
Question: Is it possible you see a watery ice layer instead of a lake as both will absorb
the radar?
Answer: Yes, we don’t know about the depth. We can’t tell. Radar data do good job, but
need to drill holes in systematic water to tell us about water volumes.
Question: Where do you need the cores from?
Answer: Mainly under the airborne lines and in transects in each sector of the ice sheet.
01:00 Satellite measurements
Marco Tedesco, National Science Foundation, City University of New York
Tom Mote, University of Georgia
Complicated: Modeling component, satellite element
Products available (remote sensing)
Ablation products
 Melt extent and duration
o Active and Passive MW
o Thermal IR
o Albedo
Accumulation products
 Snow depth
o Active MW
o
o
o
o

Mass
o
SAR
Laser altimeter
WV2 DEMs
Airborne altimeters
GRACE
Limitations
 Coarse spatial resolution at the margins
 Clear sky conditions for thermal
 Products don’t tell you how much water is refreezing or running away
 Limited to melt area/duration
THIS ONE IS KEY
Advantages
 Long-term series
 High temporal resolution
 Spatial coverage
Remote sensing of Albedo Limitations
 Limited to cloud-free conditions
 Geometry at high latitudes
 Does not provide a direct measurement of SMB
 Sensor degradation
 Physical processes impacting albedo difficult to address
Question: PRDF you can use to correct for the last bullet?
Answer: Yes, but it doesn’t use BRDF correction. Less issues when full BRDF is used at a
different time period.
Remote sensing of accumulation Limitations
Limited to intra-seasonal variability
Large uncertainty
Limited to dry zone
*From spaceborne point of view, accumulation is a challenging issue
Remote sensing of cloud radiative forcing Limitations
How to assess RS tools for SMB: metrics could include maturity of algorithms,
understanding of error sources, uncertainty quantification, assessment and validation
What in situ measurements needed?
 Accumulation rates at large spatial scale
 LWC profiles, refreezing and runoff at several locations and along the evolution of
melting season
 Multi-temporal measurements of in situ albedo


Spatially distributed measurements of density vertical profiles and snow
microstructure in snow and firn
User-centric approach for improving access, discoverability, and re-use of data
Hopes for improved product?
Combining satellite, model, and in situ measurements
Constrain error estimates
Data assimilation of satellite radiances/products
Discussion:
Question: When we have error estimates and stack them between different
reconstructions, we have a big issue. Either error is not assessed or transect is wrong.
Answer: Other scheme is modular and you can switch models to see the effect.
Question: What is data assimilation? When stay on SBM models. RACMO uses xx?? as a
boundary condition. RACMO doesn’t do further assimilations against in situ. Why not have
a forward re-assimilated model as it would be much more useful? Why are we not going
into data assimilation of in situ data since we have so much of it.
Answer: Yes, that is what I’m referring to. Force depth, have atmospheric model, coupled
with snow model. Then, taking the boundaries and letting surface and atmosphere
evolve. If you take GCNet stations and other observations, you can improve your
estimates by assimilating data to the model while it is run.
Questions: For ice sheet that worked. Doesn’t work for forward models. Why is it that we
don’t have round of data assimilation that GCM does with regional atmospheric models.
There forward modeling capability not good enough.
Participant: That is how regional climate models were developed. Assimilation isn’t a
straight forward process. Interim reanalysis don’t reinterpret surface temperature data.
Participant: observation violates the model; this is an issue with the model. The issue is
complex
Speaker: if a passive MW tells you there is a melt extent, you can add that when running
MAR. Add in some additional data. Point is that RACMO guys use something based on
MODIS. Assimilation will help you keep physics of model safe and yet you have useful
information. You will have uncertainties separate. How do we use what is best about
remote sensing doing and use complimentary data that MODIS can tell us. The
improvement you get is big with small steps.
Participant: WRF model has a xx?? mode.
Speaker: Took five years to run WRF
Question: Issue with ISM is that we don’t have the state of the lower atmosphere
perfectly known. We don’t know what is throwing it off. No knowledge of state of the
surface. Trying to reconstruct that. This is a priority. State of SME or state of firn level of
20 to 30 years is critical. Don’t see any effort here or approach here to back out what is
needed to get a perfect state of the atmosphere.
Use of Measurements
Guiding questions:
•
•
•
•
What products are being used?
What are the limitations?
What could be improved?
Key results
01:15 Ice-sheet modeling
Eric Larour, Jet Propulsion Lab
Surface elevation dataset: This is surface altimetry and must be translated. Need for full
density profile to transfer altimetry into ice equivalent. Wants top to where ice is fully
compacted. Improved definitions of compaction areas under surface. Where are firn,
snow, and ice boundary are issues.
Forcings of interest are basal stress and SMB. Getting to dynamics with altimetry is
difficult. SME and altimetry are so related that the basal frequency can be masked.


Sensitivity of misfit to observations with respect to SMB
Spatio-temporal derivative, indicates where and when SMB and models agree and
disagree.
Needs
 Accurate knowledge of uncertainty is only way to differentiate between dynamics
and firn-compaction or snow accumulation.

Need for improved spatio-temporal errors to avoid bad inversions (no melt-rate
peaks in winter)

Need to correct for spatio-temporal correlations in SMB (such knowledge for basal
friction is not currently attainable).

Need covariances in time and space plus something else?? to inform the process.

SMB correction to best-fit altimetry data: Want deltas to fall in the errors that are
specified. Misfit addressed in-land, but not at coastal areas. Need resolutions at
20 to 30 km level. Fact improving internally is not much. Problem is upstream of
the conversion between upstream something and ice something altimetry.
Question: What is the spatial resolution of the model being shown on 2006 SMB
corrections to best-fit 2006 DEM.
Answer: Gave best guess.

There is an automatic bias of dumping more snow to automatically match DEM.
We need to do a better job of just pouring in more snow to match the DEM. Could
constrain in such a way to avoid lumping spin-up errors into basal friction
inversions.
Conclusions




Need for uncertainty/error assessment sin SMB time series that are realistic
Horizontal resolution required to constrain projections at the 20 to 40 km
range
Full spatio-temporal density profiles
Reconstructions of past SMB time series to correctly capture ice volume at
present time without biasing basal friction and/or bedrock inversions
Discussions:
Need density profile for borehole – that is about 50 m deep. Yes, need more than that.
How else unless assimilate that in altimetry. Don’t have power to do xx? otherwise.
Participant said it is expensive to get that. Firn layer – isn’t always defined the same in
depth. We get confused by that.
Question: You were saying bedmap and geothermal flux. IceBridge has improved that.
Are you saying SMB is important?
Answer: We knew bedrock was important but didn’t understand SMB was so important
too. SMB is becoming just as important. Didn’t realize before.
01:30 GRACE
Isabella Velicogna, University of California Irvine
Combining SMB with GRACE and other datasets.
Products used: SMB – Precipitation – Runoff – Firn correction
Key results: Combine GRACE with SMB for understanding processes driving observed
changes in mass balance. GRACE can be used to evaluate SMB products. Can use GRACE
to reduce GIA uncertainty. Inter-comparison with GRACE/Mass budget/altimetry – can
help evaluate the firn correction from RACMO.
Improvements/Limitations:
Daily data would be helpful
Spatial resolution – ultimately need 150m resolution in certain places
Runoff – large uncertainties caused by water retention/albedo/energy balance?
Need measurements in region of high accumulation
Need to prioritize by focusing on area/processes that have larger impact
Impact of not modeling katabatic winds: to model them, need resolution of less than 1km
Need model with source code available
Showed charts using GRACE data
70% of total mass loss from SE and NW
88% of total acceleration in loss from the SW and the NW
SMB account for 68% of total mass loss and 79% of the acceleration in loss
GRACE can be used to evaluate SMB products by looking at trends or looking at absolute
values.
GRACE can be used to reduce GIA uncertainties – comparison with three models with
major discrepancy in one area, NE.
Used GRACE data to evaluate RACMO firn correction (Antarctic example)
Question/Comment: We need home for SMB of ice sheets. ECVs have homes and we
should discuss as a group what is the home? Looking at this presentation, we need to see
if there is a home for the ice sheets.
01:45 Combined altimetry data sets
Bea Csatho, University at Buffalo
Demonstrate use of altimetry of study ice sheet and snow processes.
Compare altimetry derived surface elevation changes with those attributed to SMB
anomalies and firn compaction.
Can see complex process, but once integrate it, can get a better answer.
Will focus on the surface processes from the recent published paper.
Took data from three sources: airborne topo mapper; LVIS; and ICESat.
Taking all later altimetry data within a surface area, typically 1 km2.
Shows that approach works. Shows elevation change through graphs on: Measured ice
thickness change corrected to GIA and firn compaction. Ice thickness due to SMB
anomalies from RACMO2/GR and estimated firn densities. Ice thickness changes due to
ice dynamics.
Maps of thickness change for total, SMB, and dynamic, and ice flow velocity.
Good agreement between observed total and estimated surface process-related annual
dhdt in regions of low ice velocity. Shows Total dhdt, SMB+firn compaction and dynamic
dhdt. Large, short-term dynamic signal in N and SW below the ELA – probably SMB
related.
Rapid reversal of extensive (coast to ice divide) dynamic thinning in SE – thickening or
large errors in dhdt, SMB or firn density? Having a transect in SE Greenland would be
helpful to answering this question.
Annual elevation changes of outlet glaciers: Observed annual thickness change has larger
amplification of SMB... Believe speed of thinning and slowing of thickening is what is
happening. Either SMB errors or dynamic thickening?
Looked at Antarctic too. Observed dhdt, firn densification and ice dynamics charts.
Conclusions
Synergism between remote sensing, in situ measurements, SMB and others all together
needed.
Limitations
Spatial and temporal sampling of altimetry, uncertainties in products
What is needed
Better knowledge of firn density, error estimates of SMB anomalies, firn densification
Filling the temporal and spatial gaps in altimetry observations by DEMs, radar altimetry,
GPS
02:00 COFFEE
☕
02:30 Wrap-up discussions:
Breakouts if needed
Discussion leaders in parentheses
•
•
•
•
•
•
What are the current and emerging research questions that require SMB information?
(Velicogna)
What is our ultimate goal to producing an SMB product: a reanalysis method, a
satellite-based product? (Tedesco)
What do we need to do to better quantify SMB with in-situ? (Steffen)
What processes do we need constrain: water storage/flow in ice, at bed? (Smith)
What should the future plan be for networks: hydrologic and/or ice/snow focused?
(Ahlstrøm)
Do we need a data center, set of data standards? (Koenig)
EOS Article on Greenland networks? Other journals?
Prepare summary to be presented at PARCA
Discussion:
 Tom W said that Duncan W previously said all records we had were useless for
Cal/Val for Cryosat2, but how can that be? After listening today, re-thinking this.
We have these cores, but we don’t know the accumulation. We are fundamentally
stuck at getting at SMB, interpreting dhdt. We obviously need a concerted effort at
a certain number of locations where vary precisely measuring accumulation and
what happens to that accumulation product because we are spending millions on
products affected by that measurement.
 Super sites – use a few points for calibration. Ice sheet isn’t that complicated A
few diff climate regions. If you measure accumulation at a few sites, call them
super sites and bring in snow densification measurements, sublimation? Maybe 5
is enough.


















Suggest at a transect.
East, Southern, and West would be helpful.
Do we need to measure this at some sites around Greenland?
o For model validation, ok at summit and a transect and look at GCNet sites.
For surface mass balance, those are the only two sites
For the sites at GCNet, we have a long term plan, but not for continuous coring in
an organized fashion.
Ice cores – those people got too greedy and would have been better to continue
the cores at a certain minimum because that is the data we are missing.
When taking ice cores, unless we know accumulation for each layer – you can get
accumulation and density by extrapolation. Have to do this at areas where core is
preserved. But with snow metamorphosis and hydrology xx? Need long-term
hydrology measurements at the super site with the long term coring.
Can still collect at the very top of the ice sheet, but others need compaction.
Share this need for accumulation – during EU ice to sea project, we did shallow ice
coring to extend previous ice cores. That was interesting and it showed not a
presentation?? Trend that was expected.
What about a volcanic ice signal? Could see amount of mass among the 91-92
signal? Would the ash be that mobile? Thought ash particles might be immobile.
Looking at PARCA shallow cores, they have ice percolation there. It is possible to
obtain accumulation rates above 2000m.
We have seen similar to that in cores drilled. Even where have thin percolation, if
you have centimeter lenses, you can pick up the isotope record. There is a limit on
that when you get significant percolation into previous annual layers. There are
still valid sites.
Do we know all the measurements that are all there? I didn’t know there were
transects in the southwest. Let’s put together what is there and then figure out
what is missing. Laura added that Greenland data isn’t coming in, but is it because
they are too busy? We need field measurements need to be as organized as
modeled data.
Ice to Seas project mentioned.
What isn’t clear is whether there is data being collected in a way that can answer
the worse case scenario – site where lots of snow accumulation exists, but lots of
melt in summer. Is anyone collecting data you need to compare with RACMO and
MARS?
Maybe for one or two seasons, but not for 10 years. You need monitoring to give
you that dataset year after year.
Do we even have it at one place? Not really. Have summits. In small areas, Mike’s
work and aquifer work with NSIDC has density measurements. Then need to ask
how the water is percolating and we don’t know much about that.
What are the larger uncertainties? Meltwater retention? What do we want to know
more about? Marco mentioned albedo and energy balance.
Marco said that I agree that there are at two stages of melting and accumulation.
Albedo and accumulation measurements are crucial, but depends on where you
want to go. Higher evaluations changing top levels of snow. Can measure albedo
over ice. If want to do improved projections, need to know in higher elevations
when melting starts and over bare ice. Is it happening in just percolation zones?
Ice albedo is crucial to runoff. The southesast area is crucial to SMB. Percolation is



















crucial to projections. Having megasites is great, but need some spatial variability
in the data we get. If add some spatial variability, you can do better. Have drones,
etc. Have a minimum of spatial look for long term. That is fundamental.
Renee said: Design what measurements are needed to fill in these knowledge
gaps – that is what is being asked. What needs to be done in the field? About to
launch ICESat2 and money already being spent, so want to be able to use the
resources wisely.
If we did a supersite with spatial variability, need a magnitude of more
instruments to do this. Becomes more complicated. But if you can get that, you
can identify the error bounds that are needed.
Is that true? Yes, it is variable, but need to constrain how big the spatial error is.
Arctic observatory – place to measure accumulation. What else do we need? Could
have case studies to understand how percolation occurs. Build up a network for
extrapolation.
Can you take it all the way through? From those records get accumulation, then
do a better job of atmospheric reanalysis? How get to dhdt and SMB?
Annual variability still very large and change signal over decades, we can never
detect change in accumulation in one place. Variability from one year to another is
bigger than signal over time. Can’t necessarily resolve the change. We need to
know the change in precipitation.
That is what they were saying – we know we do a good job of xx?, but we don’t
have yxx? so we don’t know how good we are. Cover the spaces where changes
taking place.
One point can tell you how well you do at one place and then do a transect.
But if you want to do everything or just start, there is a different strategy. Cost of
having a transect with essential variables may be more beneficial.
We need to look at the area where see huge changes going on in general and that
is the lower accumulation zone/the ablation zone.
One of things wondering was how to make link between observation and the
model. IceBridge did a detailed grid and that was very helpful. Have one area very
well sampled in detail and then can get at accumulation, spatial variability can be
tested. Spatial variability is important to the modeling. If I go into climate models,
I need to know the same answer at a different scale than another xxx?.
Question for RACMO and Marco, is there an effort to constrain the accumulation
models using old ice core record? That is low hanging fruit right there.
Marco said that his student going to NASA-GISS and will look at ice cores.
Certain big data can’t be released, said Laura. Certain data identified exists, just
needs to be released.
RACMO does compare something with cores, it was noted.
Two problems: SME models and firn models. Are we lumping those two together?
SMB models have densification in them. We are not assimilating a lot of data as
we should. Compare spatio-temporal variability. Should have these models on
target.
As a reminder, had super sites in early PARCA and something?? within 25km.
Is our problem accumulation or loss?
2000 to 2012 SMB components for MAR and various other models or re-analyses.
Arctic system reanalysis is an outlier. Other four are within 5%. Runoff is within

















30% of the standard deviation. It is the runoff. P-E is very close. Marco said the
accumulation depends on one thing and runoff depends on something else.
These are combined because of densities. Radars tell us about layer variability
over space, we need to understand what the densities are and in the percolation
zones, how is it densifying. And, how is the water leaving. Combine density and
accumulation radar and xx?.
But we don’t know how to measure that from what I have heard.
Mike said that IceBridge radar could be variable to spatial variability. In 2013, we
haven’t fully evaluated the value. You can get a constraint on how well your core
data matches with IceBridge data.
Neuman added that Summit is close as we have to that kind of site. Densification
has only been measured like two times so it doesn’t have everything. Changing
from dry zone to other systems are the hard ones to measure and most useful.
A transect is very important because there are small scale processes taking place
and RACMO resolution is too large.
Larry said interested in hearing reaction to how significant outflow measurements
are to what we are discussing. If have an ablation zone catchments with well
constrained outflows, how useful would that be to our interpretations to
densification?
If xx?, they would be very important.
Isabella said we need to go to areas changing fast. Need coastal area information.
If characterize those with transects, you get spatial variability. We will have to
figure out what spots specifically.
Wants to hear from people running firn models on what processes are not getting
done correctly. Firn model works in dry snow zone. But in ablation zone, it doesn’t
work as well?
Especially as transitions from percolation to saturation. Mike’s work has looked at
the transition, but eventually we’ll have enough data to parameterize certain
zones of the ice sheet.
Contradicting Isabella, speaker made an incorrect(?) statement that can calculate
melt based on temperature, but you need to know pressure on entire ice sheet. I
can predict melt. People ask why are models giving different melt rates then?
Laura says on density, got a couple different density models that were so far off
and not getting the percolation at all. They are not getting the density well in
regions with percolation, which is growing to be a larger part of the ice sheet. We
do it well in the dry.
Marco said models are using density of major driver for albedo calculation. Albedo
runs runoff and it effects density. Lost rest of what was said...?
For Marco and RACMO, what happens in the ablation zone, when the snow falls,
there is something?? in the crevasses, ice is already there. RACMO
underestimates melt in certain regions and need to get a hold on that.
Working with hydropower feasibility studies and we corrected the xx? in the
steeper ablation zones.... Disagreement with the comment about the models
regarding what is working and not working.
Global model will go to high resolution soon. In terms of spatial resolution,
everything will converge.
Lower ablation zone surface mass balance is crucial. We can’t figure out how much
is staying or how much is outflow.













RACMO and densification models – in dry snows in Antarctica, the densification
isn’t perfectly captured because don’t have right SMB. We need good initial SMBs.
In order to determine firn compaction, need to know how much snow falls plus
how to compact the snow.
Up until this point, RACMO has held assimilation at arms length. Is MAR
assimilating? No. Neither are doing as well as they could do for present conditions.
Assimilating weather data, ice core, Koni’s data, etc. Between Koni and Ian, we
are in a position at specific places for what surface and precipitation are doing.
What can we infer to get those answers in other places?
When we compare models globally, they work. But comparing with same boundary
conditions. It doesn’t tell me much. Need them to agree on ic esheet itself. In
answer to data assimilation efforts, it is a very messy process.
Spatially distributed data, I’m curious about CloudSat data?
Harry said that key motivations for why here, what can we do about processes in
mass balance and better interpretation of altimetry. Accumulation and melt we
more or less have it made. But retention is the key. If we want to get a better
answer to that question, what is the strategy to move forward, we need smallscale studies in clusters if not transects.
Ted agrees. Focused transects. It isn’t straight accumulation of snow fall. It is
more in this vast zone where all this complexity happens. Maybe we can conclude
that a focused study with a lot of instrumentation across a transect could be
useful. We don’t have an EEM to show a true runoff model. A transect with a lot of
instrumentation could support altimetry analysis.
Need east and west coast transects for water retention. Aquifer regions and
regions where it freezes. Need two transects.
Kangarlussuaq transect and west coast is difficult for us because there is an
outcropping of an aerosol xx?. That is an obstacle. It isn’t that accumulation is a
done deal. In terms of relative importance, we don’t know whether our run off is
good or bad.
Need to better define the goals. If it is to better understand how ICESat gets used
versus SLR in next few years. The RCMs are great, but whatever solution they give
you is xx?. Whenever you are doing multiple sites, transects, etc., you make
consistent measurements. Test the RCM, the GCM, etc. How can you test that you
are blowing snow if you don’t have a single point?
Blow zone was overestimated first and when compared to GRACE, you could see it
was overestimated.
In southeast, we have a transect and happy with it, but all the snow is blowing off.
RACMO is predicting that that is happening.
Santiago says in terms of remote sensing and upcoming ICESat mission, ideal
case is able to observe changes in volume, most difficult part to interpret is the
changes in accumulation and densification, including percolation processes.
Tom said what can we do with remote sensing in the near term? IceBridge will fly
in next four years, what can we get out of it?
o Do melt season flights.
o What would flight plan look like? Repeat transects of same place? Yes. You
would have a mix. Every climate zone you could at peak melt season and
repeat that. June and August.
Laura said that mid-summer or late season flights would need a different
set of instrument that could give us depth measurements over melt and
streams.
o Ben said a subset of flights might already be done to serve this. We could
make them part of the repeat list. There might not be huge spatial
distribution. In east, can see surface mass balance signal, but different in
west. We could do it once or twice and see how badly we are doing. If we
compare to the models, we wouldn’t need to do it again. What do we do to
interpret ICESat measurements so need to start making flights to interpret
that.
o Given challenges with SMB, what should we do with IceBridge right now to
help with IceBridge? Do monthly repeats now to see changes over
percolation zones form May to June to July to August. How much of that is
mass loss or retained within changing the volume?
o Important that we keep repeat profiles in place. Don’t sacrifice old profiles.
Bea added that all different radar altimeters -- doing radar with IceBridge is
important.
o

Next Step:
What do you want to do? Reconvene when?
Get data together and brainstorm over email?
Will be helpful to review presentations posted tomorrow.
One way to move forward is if we appoint some people to discuss where to go from here
and post it online and then come back to a next meeting.
Koni wants to identify 1100 m to xx m as area of interest. Concentrate there.
Have another face-to-face meeting or meet three hours tomorrow?
It was decided to meet Tuesday morning and use time in Tuesday’s session to finalize
next steps.
Participants:
•
•
•
•
•
•
•
•
•
•
•
•
•
Waleed Abdalati, CIRES/University of Colorado at Boulder
Andreas Ahlstrøm, Geological Survey of Denmark and Greenland (GEUS)
David Bromwich, Ohio State University
Zoe Courville, CRREL/US Army Corps of Engineers
Bea Csatho, University at Buffalo
Richard Cullather, NASA Goddard Space Flight Center
Indrani Das, Lamont-Doherty Observatory, Columbia University
Santiago de la Pena, Ohio State University
Ian Howat, Ohio State University
Lora Koenig, NSIDC/University of Colorado at Boulder
Derrick Lampkin, University of Maryland
Eric Larour, Jet Propulsion Laboratory
Mike MacFerrin, University of Colorado at Boulder
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Brooke Medley, NASA Goddard Space Flight Center
Tom Mote, University of Georgia
Sophie Nowicki, NASA Goddard Space Flight Center
John Paden, University of Kansas
Hari Rajaram, University of Colorado at Boulder
Eric Rignot, University of California Irvine, Jet Propulsion Laboratory
Ted Scambos, NSIDC/University of Colorado at Boulder
Frederick Simons, Princeton University
Larry Smith, University of California Los Angeles
Koni Steffen, CIRES/University of Colorado at Boulder, Swiss Federal institute of
Technology
Marco Tedesco, National Science Foundation, City University of New York
Jan Willem van de Berg, Utrecht University
Isabella Velicogna, University of California Irvine, Jet Propulsion Laboratory
Tom Wagner, NASA Headquarters
Charles Webb, NASA Headquarters
Caglar Yardim, Ohio State University