“Other people had argued that 75 to 80 percent ice volume loss was too aggressive,” said co-author Axel Schweiger, a polar scientist in the UW Applied Physics Laboratory. “What this new paper shows is that our ice loss estimates may have been too conservative, and that the recent decline is possibly more rapid.”
Is it just me, or is this counter to the sea ice comparison chart from the paper, where Cryosat-2 data consistently shows slightly higher sea ice volume than shown by PIOMAS?
Have I got that wrong? Do the two use different domains? (coverage area) Is there a better interpretation of this statement? What have I missed?
So decline over the last 33 years is probably more than the 75% to 80% reduction in minimum volume since 1979 (16.855 down to 3.261 K Km^3).Right Chris, that would be an 80.7% decline Dr. Schweiger refers to. But he goes on to say that the newly published data makes that 80% decline look too conservative. I still don't see that.
On another note, do you happen to know where I might find a link to the paper in question, Arcticio? Is it trapped behind a paywall, or has it managed to sneak out of its cage?
OK, that was a joke ::), but did you guys see this about the restrictions on publishing info related to a joint Canadian-U.S. research project to measure the ice in situ in order to verify CryoSat measurements:
http://www.cbc.ca/news/technology/quirks-quarks-blog/2013/02/loss-of-arctic-ice-loss-of-scientific-integrity.html (http://www.cbc.ca/news/technology/quirks-quarks-blog/2013/02/loss-of-arctic-ice-loss-of-scientific-integrity.html)
"...thanks to new confidentiality rules introduced into the U.S.-Canada project, both the Canadian scientists working at the Department of Fisheries and Oceans and the Americans may not be able to publish or distribute that data without [Canadian] government approval."
Artful Dodger,
I suspect [pgc]'s taking a bit of a risk, let's hope that URL stays below the radar.
Schweiger et al gave +-1,35 Mio km³ for October PIOMAS values in their 2011 paper. This seems to have been too small.
We also use the Pan-Arctic Ice Ocean Modeling and Assimilation
System (PIOMAS, Schweiger et al., 2011) output for
sea ice volume estimates. This Arctic sea ice reanalysis is
obtained by assimilation of sea ice concentration and sea surface
temperature data into an ocean–sea ice model.We use an
adjusted time series of sea ice volume partly accounting for
the possible thickness biases in the reanalysis (A. Schweiger,
personal communication, 2012)
...the arctic sea ice volume is in the kilo km^3 range, not the mega.
[[1]]
x line_piomas
1 2010.539 8.511989
2 2010.622 5.195339
3 2010.708 4.200344
4 2010.790 5.365653
5 2010.881 7.633525
6 2010.962 9.587660
7 2011.041 11.308732
8 2011.124 12.868454
9 2011.211 14.006872
10 2011.292 14.643311
11 2011.380 14.437141
12 2011.460 12.285800
13 2011.541 7.794876
14 2011.621 4.729215
15 2011.711 3.904534
16 2011.792 4.890565
17 2011.881 7.346680
18 2011.960 9.524912
19 2012.041 11.237021
20 2012.122 12.733996
21 2012.210 13.863450
22 2012.292 14.732950
23 2012.381 14.670202
24 2012.457 11.927243
[[2]]
x line_cryosat2
1 2010.790 6.844700
2 2010.881 9.892433
3 2010.960 11.165310
4 2011.038 12.483006
5 2011.122 13.962053
6 2011.210 15.692089
7 2011.288 16.274744
8 2011.787 5.249122
9 2011.879 8.996041
10 2011.957 10.788825
11 2012.040 12.249944
12 2012.119 13.648316
13 2012.208 14.723986
14 2012.288 15.907223
I'd like to add one comparison plot from Tamino, for the sake of completenessYeah, probably not too helpful, since CS-2 has not released any data for Oct/Nov 2012 yet.
In this graph, where is the actual datum point in the triangles: in the center of gravity or in the center of the smallest containing rectangle?Hi dlen,
@Artful Dodger: You are right, for the connection lines, one can take anything, but when You want absolute values to get the ratio, You need to know where Your datum point is.Hi dlen,
This is a misunderstanding - I am o.k. with the data I got.Okay, cool. 8) Let us know if you find anything interesting!
I wonder if there are PIOMAS data in gridded dataset i.e. netCDF files...
Also see Joe Romm's post on this:
http://thinkprogress.org/climate/2013/09/11/2603711/arctic-death-spiral/ (http://thinkprogress.org/climate/2013/09/11/2603711/arctic-death-spiral/)
Did PIOMAS also say volume was record low this spring?
"spring"
year Cryosat volume
2011 16.3
2012 15.9
2013 15.0
| Year | CryoSat2 | PIOMAS on IceSat domain |
| 2011 | 16.3 | 15.36 |
| 2012 | 15.9 | 15.35 |
| 2013 | 15.0 | 15.21 |
I believe SMOS had volume 8% lower than 2012 at the end of the freezing season.
Which lead me to suspect something else was going on. But I didn't go as far as reading off values and spreadsheeting so perhaps you are correct and there is no issue.
The meereisportal.de has a CS2 thickness map for March 2013:
Nothing personal taken. But as for spreadcheating... Hey! That's what I do!
... there's much more small scale variation in the Cryosat image.I suspect these are mostly artifacts of how the sensor scans the planets surface...
Nothing personal taken. But as for spreadcheating... Hey! That's what I do!
The spelling checker did not like spreadsheeting either and suggested "spearheading"
I know that you use spreadsheets and I also know that you are happy with it.
Not for me though.
Can you handle NetCDF and HDF files?
For that 'feed through', a good refreeze will be necessary, Chris. It remains to be seen whether '13-'14 is going to provide that.
Tried 4 tools to read the *.nc files, all give me invalid data for all variables. Asked via the contact form for a reply...
Diablobanquisa,
There's something going on here but I'm not sure what. That comparison shows volume increase between the end of ICESat and start of Cryosat 2. Yet PIOMAS shows a drop in volume between those dates, and PIOMAS compares well with Cryosat 2 (the Laxon paper), and with ICESat Schweiger 2011 "Uncertainty in Modeled Arctic Sea Ice Volume").
Panoply lets me import these *.nc, but resists to plot them. An export of the thickness then starts like this:
196.27345,0.0,1.4E-44,2.8E-45,2.8E-45,1.8416428E34,1.009E-42,2.8E-45,7.366571E34 ... with NaNs spreaded without any system. Makes no sense at all.
Now lets start. EASE is equal area, so not the hassle with grid cell area calculation, hmm sounds too easy.Equal area is nice to do the math, but plotting involves regridding, which consumes a lot of machine cycles and let the virtual machines I use in the cloud die. Don't tell me R does this out of the box.
Now lets start. EASE is equal area, so not the hassle with grid cell area calculation, hmm sounds too easy.Equal area is nice to do the math, but plotting involves regridding, which consumes a lot of machine cycles and let the virtual machines I use in the cloud die. Don't tell me R does this out of the box.
writePNG(sit,"tmp.png")How far are you already? Does AWI meets the CPOM results?
Here is a deep link into the archive showing all Cryosat data on maps: http://mep-datasrv1.awi.de/gallery/index.php?region=n&ice-type=thickness&maps=cryosat&minYear=2011&minMonth=1&maxYear=2013&maxMonth=4 (http://mep-datasrv1.awi.de/gallery/index.php?region=n&ice-type=thickness&maps=cryosat&minYear=2011&minMonth=1&maxYear=2013&maxMonth=4)
Wipneus, yes, the landmask is it what forces me to regrid. I want SMOS, PIOMAS and CS2 all on same map (style). Plotting an aray works similar with Python.
Why is sea ice conc. in your formular? Was thinking it's already included (like thickness for complete grid point). Have to check the docs....
In common with earlier satellite radar altimeters CS-2 sea ice thickness estimates
exclude open water [Laxon et al., 2003]. To compute sea ice volume we therefore
take the product of the area, the thickness excluding open water obtained from CS-2
and the ice concentration obtained from SSM/I
Diablobanquisa,
There's something going on here but I'm not sure what. That comparison shows volume increase between the end of ICESat and start of Cryosat 2. Yet PIOMAS shows a drop in volume between those dates, and PIOMAS compares well with Cryosat 2 (the Laxon paper), and with ICESat Schweiger 2011 "Uncertainty in Modeled Arctic Sea Ice Volume").
PIOMAS vs. ICESAT+CRYOSAT
Fall (they are in quite good agreement):(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fdiablobanquisa.files.wordpress.com%2F2013%2F09%2Fimage0031-e1380128557291.png%3Fw%3D640&hash=226888f6dd49cff0b5d191f18e06059c)
Winter (there are some discrepancies):(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fdiablobanquisa.files.wordpress.com%2F2013%2F09%2Fimage0012-e1380128574103.png%3Fw%3D640&hash=e2293471d71119b9eedc388ac0e505b2)
I've found a recent presentation (http://epic.awi.de/33996/) about the German CryoSat data.
I've found a recent presentation (http://epic.awi.de/33996/) about the German CryoSat data. It gives some interesting insights into error handling, but most importantly it details a bit on the 11-13 winter thickness trend, which is flat in PIOMAS and downwards with CryoSat.The presentation suggests that snow cover on top of the sea ice is still a source of uncertainty for the CryoSat-2 thickness calculations.
03 Oct 2013
CryoSat acquisition stopped
CryoSat SIRAL acquisitions stopped yesterday (2nd October) at 16:23:01 UTC due to an onboard issue which is under investigation.
Science measurements will resume as soon as possible but not earlier than the beginning of next week.
08 Oct 2013
CryoSat acquisition stopped - Investigation ongoing
Activities to recover from the platform problem experienced on Wednesday 02 October at 16:23:01 are ongoing according to plan. The SIRAL instrument still remains switched off.
CryoSat teams are working around the clock to resolve the anomaly and resume science measurements as soon as possible.
There are promising efforts to use passive microwave to estimate snow depth with different approaches, but the current processors uses snow climatology instead of remote-sensing data.
Warren et al., 1999 (http://www.atmos.washington.edu/~sgw/PAPERS/1999_Arctic_snow.pdf) established this climatology with results from drifting station mainly on multi-year sea ice collected over the past decades. But since the Arctic Ocean shows a significant higher fraction of first-year sea ice, we follow the approach proposed by Kurtz et al., 2011 (http://onlinelibrary.wiley.com/doi/10.1029/2011GL049216/abstract) and multiply the climatological snow depth values over first year ice with a factor of 0.5. Note: This approach is identical to Laxon et al., 2013 (http://psc.apl.washington.edu/zhang/Pubs/Laxon_etal2013_icevol_grl50193.pdf). The climatology is given as a fit function for each month and only valid for the central Arctic Ocean. Significant error may occur if this fit is used also for region farther south (e.g. Baffin Bay), however this is included in the data product for the reason of completeness.
At dry and cold conditions, the main reflector of Ku-Band signal of SIRAL should theoretically be the snow-ice interface. However results from validation experiments (Hendricks et al., 2010, Willatt et al., 2010, Willatt et al., 2011) have shown that the retracked elevation is often within the snow layer or sometimes the air-snow interface, e.g. by ground radar observations or comparisons between airborne laser and radar altimeter. Since validation data regarding penetration are sparse at basin scale, we apply a simplified parameterization which is taking into account seasonal changes.Therefore we introduce an additional penetration factor P, which describes the penetration of the radar reflection horizon. For the entire Arctic we assume a maximum penetration of 23 cm from November till April, 11 cm for October and May, and 0 cm from June till September. The penetration factor is set to not exceed local snow depth.
I wonder if it would not have been wiser to build in a laser altimeter instead of this radar, which measures the height of a plane somewhere within the snow layer, as I read somewhere.Lasers work well until they fail, which has been a big problem with IceSat. The big benefit of a radar altimeter is that it does not require cloud-free conditions..
So absolute values from CryoSat2 have to be taken with great care , it seems to me.
Will Laxon, S. et al. update their numbers?CPOM lost both scientists, Laxon and Giles, due to different accidents earlier this year. Apparently AWI took over and released monthly netCDF files, read about here (http://www.meereisportal.de/de/meereisbeobachtung/meereis_beobachtungsergebnisse/beobachtungsergebnisse_aus_satellitenmessungen/cryosat_2_meereisprodukt/).
Any idea on when such information will be released?
Will small gap in data matter?
Satellite records show a decline in Arctic sea ice extent over the past three decades with a record minimum in September 2012, and results from the Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) suggest that this has been accompanied by a reduction in volume. We use three years of measurements recorded by the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, to generate estimates of seasonal variations and inter-annual trends in Arctic sea ice volume between 2010 and 2013. The CS-2 estimates of sea ice thickness agree with in situ estimates derived from upward looking sonar measurements of ice draught and airborne measurements of ice thickness and freeboard to within 0.1 metres. Prior to the record minimum in summer 2012, autumn and winter Arctic sea ice volume had fallen by ~1300 km3 relative to the previous year. Using the full 3-year period of CS-2 observations, we estimate that winter Arctic sea ice volume has decreased by ~700 km3/yr since 2010, approximately twice the average rate since 1980 as predicted by the PIOMAS.
Arctic sea ice area, thickness and volume have decreased in the last decades, but these estimates rely on a number of geophysical parameters which introduce large uncertainties. In our study we quantify the contributions of sea ice density, snow load, and area uncertainties on both sea ice thickness and volume estimates. Sea ice freeboard retrievals from the laser altimeter onboard ICESat are used along with snow depth, sea ice density and area derived from remote sensed measurements, or based on climatological in-situ data.
Our results show that the estimates of sea ice thickness and volume are slightly influenced by uncertainties in sea ice area, while the choice of the ice density value has a major impact. Temporal and spatial patterns of snow depth become particularly important when deriving information about trends and inter-annual variability of sea ice thickness and volume. We find that the order of magnitude of sea ice volume uncertainties is about 10% of the total Arctic sea ice volume and is larger than those reported earlier, while trends within the ICESat period (2003 - 2008) are similar. Using a consistent choice of sea-ice density for the ICESat and CryoSat-2 (2010 - 2011) data we obtain a less dramatic sea ice loss than reported previously.
An improved CryoSat-2 sea ice freeboard and thickness retrieval algorithm through the use of waveform fitting
N. T. Kurtz1, N. Galin2,3, and M. Studinger1
1Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
2Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USA
3NOAA, Silver Spring, MD, USA
Abstract. We develop an empirical model capable of simulating the mean echo power cross product of CryoSat-2 SAR and SARIn mode waveforms over sea ice covered regions. The model simulations are used to show the importance of variations in the radar backscatter coefficient with incidence angle and surface roughness for the retrieval of surface elevation of both sea ice floes and leads. The numerical model is used to fit CryoSat-2 waveforms to enable retrieval of surface elevation through the use of look-up tables and a bounded trust region Newton least squares fitting approach. The use of a model to fit returns from sea ice regions offers advantages over currently used threshold retracking methods which are here shown to be sensitive to the combined effect of bandwidth limited range resolution and surface roughness variations. Laxon et al. (2013) have compared ice thickness results from CryoSat-2 and IceBridge, and found good agreement, however consistent assumptions about the snow depth and density of sea ice were not used in the comparisons. To address this issue, we directly compare ice freeboard and thickness retrievals from the waveform fitting and threshold tracker methods of CryoSat-2 to Operation IceBridge data using a consistent set of parameterizations. For three IceBridge campaign periods from March 2011–2013, mean differences (CryoSat-2 – IceBridge) of 0.144 m and 1.351 m are respectively found between the freeboard and thickness retrievals using a 50% sea ice floe threshold retracker, while mean differences of 0.019 m and 0.182 m are found when using the waveform fitting method. This suggests the waveform fitting technique is capable of better reconciling the sea ice thickness data record from laser and radar altimetry data sets through the usage of consistent physical assumptions.
Citation: Kurtz, N. T., Galin, N., and Studinger, M.: An improved CryoSat-2 sea ice freeboard and thickness retrieval algorithm through the use of waveform fitting, The Cryosphere Discuss., 8, 721-768, doi:10.5194/tcd-8-721-2014, 2014.
Through comparison with Operation IceBridge data for
the 2011–2013 campaigns, this study has shown that fitting of the CryoSat-2 level 1B
waveforms using a physical model can be used to obtain improved results over the em-
pirical lead and threshold tracker (ELTF) methods which are similar to those used by
Laxon et al. (2013). The ELTF method was found to have respective mean freeboard
differences (CryoSat-2 – IceBridge) of 15.4 cm, 15.9 cm, and 11.9 cm and mean sea
ice thickness differences of 144.2 cm, 149.3 cm, and 111.9 cm. The mean freeboard
differences for the waveform fitting method were 2.2 cm, 2.5 cm, and 1.1 cm, and the
25 mean sea ice thickness differences were 20.6 cm, 23.3 cm, and 10.6 cm.
Finally we compare CS-2, gathered between the 10th of March and 09th of April 2011, with ice thickness estimates computed using laser altimeter freeboard measurements from the NASA Operation IceBridge (OIB) mission gathered between the 16th and 28th of March 2011. CryoSat data acquired over the same dates in 2012 are also compared with IceBridge data gathered between the 12th of March and the 2nd of April 2012. We compare this data by gridding both CS-2 and the airborne data onto the same 0.4 latitude x 4 longitude grid as used for the EM comparison. The correlation (figure 3c) is lower (R = 0.608) than either the EM or ULS and the mean difference is -0.048±0.723 m.
Lastly, an
evaluation of the IceBridge snow depth measurements needs to be done to improve
basin-wide snow depth on sea ice estimates. This has been done for a single season
of data (Kurtz and Farrell, 2011) compared to the snow depth climatology of Warren
et al. (1999), and for passive microwave retrievals of snow depth on sea ice for first year
ice (Brucker and Markus, 2013). The focus of a future study will be to utilize existing
observations to improve estimates of snow depth on sea ice to be used in the retrieval
of sea ice thickness from the CryoSat-2 time series.
Overall, this study has further demonstrated the capabilities of CryoSat-2 for the
retrieval of sea ice freeboard and thickness. The advantage of the retrieval processes
used in this study is that they are compatible with the laser altimetry record and show
that the two records can be reconciled to produce a more complete time series of sea
ice volume change. This has distinct advantages for the expected launch of the ICESat-
2 laser altimeter mission in 2017. The lifetime of CryoSat-2 is expected to overlap with
the ICESat-2 mission, as is the new Sentinel-3 radar altimeter mission. The combined
satellite radar and laser altimetry data provided by these missions will thus provide
unmatched information on the state of the Arctic sea ice cover.
The ELTF method was found to have respective mean freeboard
differences (CryoSat-2 – IceBridge) of 15.4 cm, 15.9 cm, and 11.9 cm and mean sea
ice thickness differences of 144.2 cm, 149.3 cm, and 111.9 cm.
Thanks for this, Wipneus.QuoteThe ELTF method was found to have respective mean freeboard
differences (CryoSat-2 – IceBridge) of 15.4 cm, 15.9 cm, and 11.9 cm and mean sea
ice thickness differences of 144.2 cm, 149.3 cm, and 111.9 cm.
What are they saying here exactly? That CryoSat2 data so far has overestimated average ice thickness by over 1 m?
We note that several differ-
ences are present between the freeboard retrieval used by Laxon et al. (2013) and the
ELTF method used here. The primary difference is that Laxon et al. (2013) subtracted
a bias from the sea ice lead elevations by taking the difference between returns from
the ocean when sea ice is not present and returns from leads in the nearby ice pack.
This was done following Giles et al. (2007), but was not done in the ELTF freeboard
retrievals. Additional differences include (but are not limited to) the exact definition of
the first peak, as well as the use of a mean sea surface height data set in place of
the EGM08 geoid. Therefore, the comparisons done in this study are similar, but not
exact reproductions of methodologies. The purpose of the comparison is to highlight
the physical basis between differences in the retracking methods
Thanks for this, Wipneus.QuoteThe ELTF method was found to have respective mean freeboard
differences (CryoSat-2 – IceBridge) of 15.4 cm, 15.9 cm, and 11.9 cm and mean sea
ice thickness differences of 144.2 cm, 149.3 cm, and 111.9 cm.
What are they saying here exactly? That CryoSat2 data so far has overestimated average ice thickness by over 1 m?
As you say, crandles. The wag in me though, judging from the ELTF variability, want's to chime in to suggest that in many cases/areas, the best that could be said of the ELTF data is, "there is ice here" vs "there is thicker ice here". It makes the spot data provided by buoys valuable for adjusting thickness estimates.QuoteWhat are they saying here exactly? That CryoSat2 data so far has overestimated average ice thickness by over 1 m?I think not quite: To me 'difference' is an always positive quantity not a vector which has direction. So if 52% of time CS2 is over 1m thicker than reality and 48% of time it is over 1m thinner than reality then the mean difference is over 1m however in this case the volume only has to be adjusted by 4% of area * over 1m thickness difference. That is a lot less than Neven is suggesting.
I think not quite: To me 'difference' is an always positive quantity not a vector which has direction. So if 52% of time CS2 is over 1m thicker than reality and 48% of time it is over 1m thinner than reality then the mean difference is over 1m however in this case the volume only has to be adjusted by 4% of area * over 1m thickness difference. That is a lot less than Neven is suggesting.
(...) mean freeboard differences (CryoSat-2 – IceBridge) of (...)
no, "difference" is not open to interpretation:Quote(...) mean freeboard differences (CryoSat-2 – IceBridge) of (...)
The focus of this study is to develop a new method for the retrieval of sea ice free
board from CryoSat-2 data. We demonstrate that this method is consistent with inde-
pendent measurements from airborne laser and radar altimetry data sets from NASA’s
Operation IceBridge mission to retrieve sea ice thickness which eliminates the need to
utilize different ice density and snow depth values as an effective bias correction.
These quantities are due to environmental processes and should be applied in a con-
sistent manner in the retrieval of sea ice thickness regardless of which instrument is
used. In the case of sea ice density, previous studies have utilized a wide range of
values, which will result in large differences between data sets if the same physical
assumptions are used. For example, in the study by Kwok et al. (2009) an ice-density
of 925 kgm−3 was used, Kurtz et al. (2011) used a value of 915 kgm−3, while Laxon
et al. (2013) used an estimate of 917 kgm−3 for first year ice and 882 kgm−3 for mul-
tiyear ice. In these studies, the range of sea ice density values for multiyear ice is
particularly large at 43 kgm−3. For a typical multiyear sea ice floe with 60 cm of snow-
ice freeboard and 35 cm of snow depth, the sea ice thickness estimate differs by 1.1 m
within this range of ice densities.
Despite the large-scale mean agreement of the sea
ice thickness data sets described in previous studies, this discrepancy in physical as-
sumptions points to the source of the differences as being due to potential biases in
the freeboard and snow depth data sets used. This large discrepancy underscores the
need to establish a set of consistent physical constants for use in the retrieval of sea
ice thickness from satellite radar and laser altimetry data
Nevertheless, comparison of CS-2 measurements with three independent in situ data sets reveals differences of less than 0.1 m in thickness when averaged on a large scale, or over a full winter growth season.
the mean sea ice thickness differences were 20.6 cm, 23.3 cm, and 10.6 cm.
...
A bias of 1.9 cm was found in the waveform fitting method freeboard retrievals compared to the IceBridge data, this bias is consistent with the estimated range bias due to off-nadir ranging of lead points shown by Armitage and Davidson (2014).
For example, in the study by Kwok et al. (2009) an ice-density
of 925 kgm−3 was used, Kurtz et al. (2011) used a value of 915 kgm−3, while Laxon
et al. (2013) used an estimate of 917 kgm−3 for first year ice and 882 kgm−3 for mul-
tiyear ice. In these studies, the range of sea ice density values for multiyear ice is
particularly large at 43 kgm−3. For a typical multiyear sea ice floe with 60 cm of snow-
ice freeboard and 35 cm of snow depth, the sea ice thickness estimate differs by 1.1 m
within this range of ice densities.
Despite the large-scale mean agreement of the sea
ice thickness data sets described in previous studies, this discrepancy in physical as-
sumptions points to the source of the differences as being due to potential biases in
the freeboard and snow depth data sets used. This large discrepancy underscores the
need to establish a set of consistent physical constants for use in the retrieval of sea
ice thickness from satellite radar and laser altimetry data
If Laxon et al get difference of <0.1m then couldn't an alternative explanation be that as ice gets thinner while snow remains similar (possibly increases slightly) the density that should be used changes over time such that 925kgm-3 is a sensible value for 2009, 915 sensible for 2011, while in 2013 a more sophisticated 917 / 882 split for FYI / MYI is sensible.
I am still left feeling I don't know if the method is an improvement or not.
It seems that Cryosat2 volume is likely to be fairly similar to what has been previously reported (not 1m thinner).
Would a comment on the paper something like the following be sensible?Commenting is open until 21st of March, there is some time.
Further Cryosat-2 data is seen as a vindication of PIOMAS. That may not be as strong as it looked if this paper has it right.
the advantages of getting the freeboard right are huge
If I had know that the Cryosat-2 data includes a bias of about 1.5m, then I would have been less impressed. That is all I wanted to say.QuoteFurther Cryosat-2 data is seen as a vindication of PIOMAS. That may not be as strong as it looked if this paper has it right.
With the errors looking to be of similar magnitude, AFAICS the vindication might be slightly worse or slightly better. If you see reason to think it will be worse rather than better then please explain.
It is what this paper is claiming (with the assumption that IceBridge is correct).Quotethe advantages of getting the freeboard right are huge
Sure, but do we think the freeboard is more correct than Laxon et al?
Our results still reveal a decline in sea ice volume between the ICESat (2003–2008) and CryoSat-2 (2010–2012) periods, but less dramatic than reported in previous studies. However, final quantitative conclusions about a change in sea ice volume are hard to make, considering the large uncertainties and unresolved biases found in our study.
So decline over the last 33 years is probably more than the 75% to 80% reduction in minimum volume since 1979 (16.855 down to 3.261 K Km^3).Right Chris, that would be an 80.7% decline Dr. Schweiger refers to. But he goes on to say that the newly published data makes that 80% decline look too conservative. I still don't see that.
Anyone?
Yes, you go from 1979 maximum to 2012 minimum to come up with the ~80%
A new paper has just been published in The Cryosphere, entitled "Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends"
http://www.the-cryosphere.net/8/705/2014/tc-8-705-2014.html (http://www.the-cryosphere.net/8/705/2014/tc-8-705-2014.html)
16 pages of interesting stuff, including discussion about the uncertainties in ice density and snow depth.
The ultimate conclusion:QuoteOur results still reveal a decline in sea ice volume between the ICESat (2003–2008) and CryoSat-2 (2010–2012) periods, but less dramatic than reported in previous studies. However, final quantitative conclusions about a change in sea ice volume are hard to make, considering the large uncertainties and unresolved biases found in our study.
The mean total sea ice volume is 10120±1280 km3 in October/November and 13250±1860 km3 in February/March for the time period 2005–2007.
This February, Cryosat saw average sea-ice floe thicknesses of just over 1.7m, giving a volume across the Arctic of nearly 24,000 cubic km. Back in the winter of 2013, following strong melting during the previous summer, floe thicknesses averaged 1.5m and the volume fell below 21,000 cu km.http://www.bbc.co.uk/news/science-environment-32348291 (http://www.bbc.co.uk/news/science-environment-32348291)
And to mark the spacecraft's fifth birthday in orbit, the team is switching on a new, near-real-time service to aid science and maritime activities.
Sea Ice thickness products from CryoSat NRT (near real time) released. These include 2, 14, and 28-day Arctic maps and thickness timeseries from Oct-2010 until the latest available Cryosat NRT products.
Monthly thickness and volume maps from the archive (2010-2015) will be released in the next few months.
QuoteAnd to mark the spacecraft's fifth birthday in orbit, the team is switching on a new, near-real-time service to aid science and maritime activities.QuoteSea Ice thickness products from CryoSat NRT (near real time) released. These include 2, 14, and 28-day Arctic maps and thickness timeseries from Oct-2010 until the latest available Cryosat NRT products.
Monthly thickness and volume maps from the archive (2010-2015) will be released in the next few months.
http://www.cpom.ucl.ac.uk/csopr/index.html (http://www.cpom.ucl.ac.uk/csopr/index.html)
http://www.cpom.ucl.ac.uk/csopr/seaice.html (http://www.cpom.ucl.ac.uk/csopr/seaice.html)
(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fwww.cpom.ucl.ac.uk%2Fcsopr%2Fsidata%2Fthk_28.png&hash=f2d0199709d1124a821000ec1f466d41)
Odd how the ice in the Barrents is a metre thick even on the ice edge, you'd expect or at least I expected some kind of tapering off of ice thickness.
Odd how the ice in the Barrents is a metre thick even on the ice edge, you'd expect or at least I expected some kind of tapering off of ice thickness.
Odd how the ice in the Barrents is a metre thick even on the ice edge, you'd expect or at least I expected some kind of tapering off of ice thickness.
I am suspecting that some areas shown as white are too thin to reliably measure as well as possibly also being no measurements or no ice. Even so, there is a lot of green and little blue in Southern Baffin Bay as well.
Sea Ice near real time thickness products are currently available in ASCII and netCDF formats in either a whole Arctic 5km resolution or as individual sectors at 1km resolution.
For 5km resolution data, a circular operator of radius 25km is applied when gridding the data and all points receive equal weight.For the 5km grids, only grid points with sea ice thickness data are included in the file (ie it is a sparse grid).
For the 1km grid all grid points north of 60N are present.For 1km resolution data, a circular operator of radius 5km is applied when gridding the data and all points receive equal weight. For the 1km grid all grid points north of 60N are present.
See also this blog post on Carbon Brief
We have now released our spring and autumn thickness data for the whole CryoSat-2 period on our website:
http://www.cpom.ucl.ac.uk/csopr/seaice.html?thk_period=0&select_thk_vol=select_thk&season=Autumn&ts_area_or_point=all&basin_selected=0&show_cell_thickness=0 (http://www.cpom.ucl.ac.uk/csopr/seaice.html?thk_period=0&select_thk_vol=select_thk&season=Autumn&ts_area_or_point=all&basin_selected=0&show_cell_thickness=0)
This is the data that is presented in the paper, and has now been peer reviewed. Thanks all for your interest.
See my comment in Carbon Brief.
Arctic sea-ice volume during the first two weeks of October was about 6,200 cubic km.
The number comes from Europe's Cryosat mission, which has just restarted its near-real-time data service.
...
Volume of Arctic autumn sea ice: First two weeks of October (average):
2010: 5,900 cubic km;
2011: 4,500 cu km;
2012: 4,600 cu km;
2013: 7,800 cu km;
2014: 6,800 cu km;
2015: 6,200 cu km
The latest observations of sea ice thickness from the CryoSat satellite show that the ice was, on average, 1.8m thick in March [2016], says [Andrew] Shepherd. This is about 10cm thinner than the same time last year, but about 10cm thicker than the record winter low in 2013.
Shepherd and his team will use measurements of thickness together with sea ice extent to estimate the volume of ice left at the end of winter. These results aren’t public yet, but Shepherd tells Carbon Brief it’s not looking hopeful:
“If things continue as in previous years, I suspect this year could see a tie with that record low for volume as well, but it’s too early to say for sure.”
It shows almost no ice thicker than 2 m at that time of year in 2011, then 2013 a big recovery year before falling away again.
We find a positive correlation between buoy snow freeboard and CryoSat-2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat-2 sea ice thickness in autumn 2013.
Presumably they are all 28-day Thickness maps collecting measurements over 24/10 - 20/11?
So this is precise and well-calibrated for year-to-year comparisons?
It shows almost no ice thicker than 2 m at that time of year in 2011, then 2013 a big recovery year before falling away again.
You may wish to read the paper mentioned by Stefan Hendricks above:
http://onlinelibrary.wiley.com/doi/10.1002/2015GL064081/full#grl53020-bib-0003%29 (http://onlinelibrary.wiley.com/doi/10.1002/2015GL064081/full#grl53020-bib-0003%29)QuoteWe find a positive correlation between buoy snow freeboard and CryoSat-2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat-2 sea ice thickness in autumn 2013.
Comparing the Cryosat thickness map to the extent map at minima (circa first week of September), and then taking into account the temperatures that we've seen since then, it's difficult to believe any new ice has grown to 1m thickness this fall/winter. The number of Freezing Degree Days for N80 is still only half of what's needed to grow 1m thick ice and temperatures from N70 to N80 (where most of the new ice resides) have been even warmer.
If temperatures were a uniform -10C it would take 4 months to grow 1m thick ice. For the area between N70 and N80 we've barely seen a month with temperatures below -10C. Thermodynamically it simply isn't possible for much new ice to be greater than 0.6m thick right now.
Could be the darkness or fear of going for a cold swim accidentally, but maybe everyone is scared to walk out on the ice enough to get adequate cores so as to re-calibrate.
I mean, you can understand the surviving MYI being thick, but not new ice.
Maybe everyone is scared to walk out on the ice enough to get adequate cores so as to re-calibrate.
Using Lebedev new ice thickness reduces from 0.84m to 0.52m
Using Berillo new ice thickness reduces from 0.68m to 0.43m
Neither of these takes into account the actual date of first ice formation, this means they are likely biased high.
http://www.esa.int/Our_Activities/Observing_the_Earth/CryoSat/Arctic_freeze_slows_down (http://www.esa.int/Our_Activities/Observing_the_Earth/CryoSat/Arctic_freeze_slows_down)
CryoSat volume update. Looks like tied with previous low for November...
Low, but is it better than expected with the current extent numbers and temperature anomalies?
PIOMAS was tying for the lowest last month, will be interesting to see their numbers in a few days.
(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fwww.esa.int%2Fvar%2Fesa%2Fstorage%2Fimages%2Fesa_multimedia%2Fimages%2F2016%2F11%2F2011_16_november_arctic_sea-ice_thickness%2F16542447-1-eng-GB%2F2011_16_November_Arctic_sea-ice_thickness_node_full_image_2.gif&hash=6008c96935f78809a9f8fdd8713b3cfe)
(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fwww.esa.int%2Fvar%2Fesa%2Fstorage%2Fimages%2Fesa_multimedia%2Fimages%2F2016%2F11%2F2011_16_november_arctic_sea-ice_volume%2F16543625-1-eng-GB%2F2011_16_November_Arctic_sea-ice_volume_node_full_image_2.png&hash=e83022fc5f4bd99427bd0b83a6e15ac1)
Is it even cold enough for cryosat to work right? (Serious question)
This measurement technique works in autumn, winter and spring. In summer, melt ponds prevent us from estimating sea ice thickness
Is it even cold enough for cryosat to work right? (Serious question)QuoteThis measurement technique works in autumn, winter and spring. In summer, melt ponds prevent us from estimating sea ice thickness
The problem seems to be surface liquid water related rather than temperature related? So storms more of a problem than the temp being as warm as -10C ?
Here's a CryoSat animation showing Jan 2016 vs Jan 2017:
(https://lh3.googleusercontent.com/crXXd7ZKOKc1lVt-Ucv6DsIFu4u3NKODqPLA1leysdR-Usz4UHR9qe0Vv2k03bjxzKCsn5dtjgxlGO9KliS9gOZNj8ERMXqRTURtF3KQgnSWPJkSQPanKt42Doflgv97FfaxBR_1r_SbRUhQdOCZhLuoSAgZ5pszsi-wAXhXHMBw4QADVRN_JYbEwq7cfWqXAvcEiatBYO8nWs3mrlWOX_HN1VFhYoBkFHn7NSMFzLU8HJJ0Q8exfX1GjwaoLEGvuWlAnxuF5siv9rXc3sM2QvpS2HyQqRdx1NEZeEkg3Grl6kIJYDfQHgCSJXPzj7uBmn_rSEPC2nyPiwfUaceXE11fZvP1nCHO27CeSd14XweYQHbiv5ywYAhznAOrDBizusBErm8BI3lcnwdXDh0U6k58oKez3Ze3V7RjWdkR_IlO3SqCEK4qqjXCTXjZesDFnNwxO_19oWPiFEpjIJSYLm3j7N8bZHll3bAzQIuovDCQdvQ7908z-llU5ZUIU8a3d5J5JYI236aQP67p2JYhZQ3PNtRYwBvd27bCDCU-CiseB7aLDACGLe5rQ4Tg9fp_bQia6ukMql8ZZNk3qJY__NKOrjIe1zTp4qITlV-48UYxPr0eH4xG=s800-no)
Maybe it's my eyeball, but I would say that CryoSat suggests that the ice is slightly thicker right now than it was last year around this time. Which is weird, given the FDD anomaly, the cloudy weather, the storms, and PIOMAS saying there's a difference of 2374 km3 in volume and 17 cm in average thickness.
Weird...
... what is Cryosat's skill in distinguishing snow?
Here's a comparison of January 2012-2017:There is a problem with the palette used in these maps, in that it is easier to distinguish ice from 2 to 4 m than from 0 to 2 m. Very difficult to compare the main extensions of ice which are under 2m !
(https://lh3.googleusercontent.com/Li6zRONkip0kZJxI1w0Lx14-SSkC4HGpVtavBlwQ7rTT7-QpoeQ_Bt5U0mpIcmyO1RHVDA7A2Fyp1Mllu412DV9qLhtUVJRTYqxpmQn8zGPxERQ2Eq63tCeSXI2UVcEsFWiPQnxR8gOm84PjkI4ivsuflLp4G8sOomq4_F11oVjHSFj8kMpr0upBqbeVsOSA5GY_g7lLxPh_MFond45D4WxW5PLIMoOr2j2YeXjV5WJjwNyE_5_NMR6WKXEz-F4B9TFfL1kbhBgRm5LS9j7rsXlsCsieBFHlKJiReuAJ4vH0GIxeA84O2OY-L-jwWAYlxWfgQDrfccpi2zz_7HdiWBQm9mcmqQMcRRMhimzDtnZXQHSX9zEBkxIvcEDEsInDc8KrnG3digkxTIAuhuLlULkMzDSmeQBtgjdSrxhUVCjW7QBVH6VEse65oWqTHEHKfp8AGxXfSd-h-qbx_S9HkMZ3e3itNsq3MIs5dfqi7wV0O7pwcTS2MqWtisNY2ZnUo7bd2l_FOimK59q1rM6IFpTPYpiO5MGpIm6ZnnhK3F2T8tbQrTV-qHatfYYkaIFfwIpGgQs6LSdht3dIHfOMN3nSI9wLyKAwL1QwmZYtxJIGnqHqgH4k=w1200-h800-no)
CryoSat-2 clearly has Jan 2012 and 2013 thinner than Jan 2017. But PIOMAS has this:
(https://lh3.googleusercontent.com/vaCYDW_AYFn1nxuvWg4RD9Zuv9UrwCTQDupgM4qa9dfcS-3zRXB4SY2GOFxW6n93OUi1-A2Zl3L6OHqC6xT2V2pnaaTWBcfwYE6Mt5ciQTtvlsQEGn_SWi_wc1zix7Y86A6z-dPriht3nVV9tUnrVZBfbxShd2DX2tm13uDPfgoZk61GFpmeu3uXzqdJaEFDpsUmxChNsqHaXOnKvGSwkvBNvghrJ_SxXU7ZGxNloBbjBuvZbehIxgNcS5ftG9NFtLEE9E3m4u2EOh8BiV8TwLU8VbKNQFLTzqGj9PIMuzo4Yp9qjqzv6UpkzlOLZoAHhH-JM4gfOZo2GWdUjoxuj7NZ-bkInacBk38urbowQNQH6MYBJUCF73z_4DxfMIo8YxhKzb2MibOUxq7HInl4URQfYXKP9KI-UFmvOaBwVuB7q59a_EXc4yPl_fm0RuD4H3fe0AZEJS494QQ_T2bLK13bpk5_PPsHv8YkXrxLKBeBjqJOI0xV0His7kXec2qjfZg3n0v6EqBkFPvUG69YgPZLCgHBZ8uzCU_ddTS6z5MGVxlMG_vyeuV1yML5Yd_n3ARCnaeOsgQq9EXz84uGw1NhVSAeJMf60wUAvckRwfOFrfttAb8d=w241-h199-no)
Difference with 2012 and 2013 at the end of January is 2270 and 1571 km3 respectively. Okay, CryoSat is showing an average, so let's the take the average of the differences with 2017 at Jan 1st and 31st: 2118 and 1193 km3 respectively.
There is a problem with the palette used in these maps, in that it is easier to distinguish ice from 2 to 4 m than from 0 to 2 m. Very difficult to compare the main extensions of ice which are under 2m !
Why does 2017 map say Cryosat "NRT"? Is it maybe an enhanced version? That might invalidate comparison (just saying)
Weird indeed.
I know we have discussed about it before but Wlwhat is Cryosat's skill in distinguishing snow?
There is a problem with the palette used in these maps, in that it is easier to distinguish ice from 2 to 4 m than from 0 to 2 m. Very difficult to compare the main extensions of ice which are under 2m !
However, I have what is a rather different quibble. We seem to be saying FDD is low this year which will result in thinner ice. However there is a serious problem of cause and effect. At the other extreme, we could say there is less volume ie thin ice so heat transfers from ocean to atmosphere faster and therefore the atmosphere is warmer. So the low FDD could be a consequence of low volume and as there is fast transfer of heat from ocean to atmosphere, the volume of ice could be catching up to where it normally is. It isn't going to be one extreme or the other but some combination. Is it possible that the low volume and increased storminess could this year have altered the balance between the two causes making it more a case of ocean warming atmosphere. Then would using low FDD as a reason for low volume be giving a false indication of particularly low volume? Perhaps Cryosat2 data shouldn't be dismissed in favour of PIOMAS quite so readily?
I've been thinking about that too - but came to the conclusion that if the ocean is warming the atmosphere more than heretofore, it is symptomatic of something new - and none of the possibilities I can think of seem ultimately conducive to higher than expected volume. viz:
- thinner ice causing more ocean heat to be lost to the atmosphere. No volume increase there.
- less snow cover causing greater heat loss and more freezing? Yes, but it's been stormy, so why would there be less snow? Also, the storms would seem to imply greater mixing with the warmer southern latitudes.
- halocline breakdown. Ouch.
I've been thinking about that too - but came to the conclusion that if the ocean is warming the atmosphere more than heretofore, it is symptomatic of something new - and none of the possibilities I can think of seem ultimately conducive to higher than expected volume. viz:
- thinner ice causing more ocean heat to be lost to the atmosphere. No volume increase there.
- less snow cover causing greater heat loss and more freezing? Yes, but it's been stormy, so why would there be less snow? Also, the storms would seem to imply greater mixing with the warmer southern latitudes.
- halocline breakdown. Ouch.
>"- thinner ice causing more ocean heat to be lost to the atmosphere. No volume increase there."
My reaction is depends on circumstances: If it is thinner ice allowing more heat to be lost then rate of heat loss is a good indicator of ice mass being formed. However if it is stormy weather stirring up heat from below then it is much less of a good indicator of ice mass formed.
Cause and effect problems are complicated and I am not at all sure I am getting my head around it all correctly. I have no real reason to think PIOMAS is not doing it all correctly. Nevertheless with the FDD total being so weird, I think we should be cautious and not go jumping too far ahead to too many conclusions.
Thank you Diablobanquisa. :)
There is a problem with the palette used in these maps, in that it is easier to distinguish ice from 2 to 4 m than from 0 to 2 m. Very difficult to compare the main extensions of ice which are under 2m !
;)
(https://forum.arctic-sea-ice.net/proxy.php?request=http%3A%2F%2Fimages.meteociel.fr%2Fim%2F322%2Fjan16vs17_cryosat2_epa0.gif&hash=ec9530cc134834fb7bda0b1120d67533)
Ku-Band radar (CryoSat) is sensitive to snow grain size. A larger anomaly in the snow microstructure (depth hoar, ice lenses) may result in too high freeboard/thickness values.
Radar altimetry measurements of the current satellite mission CryoSat-2 show an increase of Arctic sea-ice thickness in autumn 2013, compared to previous years but also related to March 2013. Such an increase over the melting season seems unlikely and needs to be investigated. Recent studies show that the influence of the snow cover is not negligible and can highly affect the CryoSat-2 range measurements if it is assumed that the main scattering horizon is given by the snow-ice interface. Our analysis of Arctic ice mass-balance buoy records and coincident CryoSat-2 data between 2012 and 2014 adds observational evidence to these findings. Linear trends of snow and ice freeboard measurements from buoys and nearby CryoSat-2 freeboard retrievals are calculated during accumulation events. We find a positive correlation between buoy snow-freeboard and CryoSat-2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat-2 sea-ice thickness in autumn 2013.
Moreover, backscatter from both interfaces superimpose each other and cause broadened radar returns, which is largest for snow depths >20 cm (Kwok, 2014). As a result, freeboard estimates can be biased high with the presence of thick snow layers.
(...)
For wet snow at the beginning and the end of the melting season, the dielectric properties of the snow layer might even limit the physical penetration of radar waves.
(...)
We hypothesize that the snow cover significantly affects the CS-2 freeboard retrieval by snow backscatter which would affect also sea-ice thickness and volume, independently of the range retrieval method.
(...)
We find high differences of up to 45 cm (30 cm) for the 40 % threshold retrieval and up to 30 cm (20 cm) for the 80 % threshold retrieval from the comparison between November 2013 and 2012 (November 2013 and March 2013) north of Canada.
(...)
It is still difficult to quantify the snow-scatter induced bias without knowledge of the regional distribution and temporal evolution of snow depth and snow stratigraphy. Snow, accumulated early, may undergo a partial melting and subsequent freezing as well as wind compaction. This leads to a very heterogeneous snow density distribution, while for the propagation of the Ku-band signal it is widely assumed that the snow density is homogeneous. In this way formed layers may affect the location of the main reflecting horizon.
(...)
We conclude that snowfall can have a significant impact on CryoSat-2 range measurements and therefore on ice freeboard, thickness and volume. The assumption that the CryoSat-2 main scattering horizon is given by the snow-ice interface cannot be justified in regions with a thick snow layer.
I've been able to look some more into this, with the help of Michael, who sent me the data that allowed me to create this graph:Neven, this is worthy of a blog post, if you wouldn't mind.
(https://lh3.googleusercontent.com/6jRsgGfz3uf16mwg1UNPCC77bpKnyzZUExYRel7QovPU0xd3C22nDqipFHb-T87ALjHhbbgpMzkB7a-FskbjVEvjKF36LSxPdaPWJYNCNddQpGZ2_H5aWkSX4riiNGuejdtUqh0-hqZZiNMXCXx7zJiAbJtst7qlMmhtNenA5L3eAcnKXCcCy6Uv_T6tovRjfJAdoQilAiMvkDqYRQvOFwelZN5_24evZwyiASKosvJ8s0RbZY8lhdmvBLqUemu7Tt-dxI5XIeFSw3gbl_TFEcTAEwUnvtA-umDyIngnss4IMdkdDrJIvVFDun_h0a6fG895AXwmXNoPmMkJRKWdJunia45hSg8YRN0HIQCYL6NcVq-yRaaMZVmdxCoLWVjsPcDCYOuQZYKItfwKzGMnEg6NMOoGGN0MJg0YeDXzqqlVT9pKLCGmfNq_9_hwjCUbLmdhoqPjcWuOz4_JK0-0dw9DnEiNDgXAHYKcA9HZzkW6_azr1EncBVdFQSUIGCjBLEqSs2R6IWVfkON_6oxp6BETy1MDwm_8YSNQUnWIZhdd-_Pn0oGGV_mGV0VNyhXs3lEpXkWh2Q6rBcTgBJy97fMs4KkbXDLi5INFO-Hxuf6IKLI7zv3B=w799-h332-no)
Michael sent me the CryoSat-2 data from AWI (CryoSat-2 average volume on a combined PIOMAS-AWI grid), and I simply added the daily PIOMAS volume data for January and then divided by 31 to get an average as well.
As you can see the trend lines more or less move the same way, except for this year. According to CryoSat-2 there is slightly more volume now than last year and so the trend line goes up a bit. For PIOMAS the trend line crashes.
The same thing happens in December:
(https://lh3.googleusercontent.com/l0704PJVo0xXZyxZvyAN6WFZPfxbpM8ax89iq2H9xgutFScqsrRAwwuHsBKLUiVuq1vUFSTu8mLU2USRqSVxoOnYfy9w1RIjGI9jAMNYbWrMqWOVI33ej-0Ok8Kn5HMX_4Iylx3TDTujPvjAb7waiW12wJMam5TxSO69qnNysXwmiY3eul509n7SjQ0_D3GyOYMHnnkxq_5v8S7gIDuS5n1qtzTMAxcD3K_1bzuxoHcL7_v48Rr0J6z8OQPIDsuEij1FpoL7OQooCmsdeXqFZJG7t-pvPskMjYPnHmjwNqqdtW1fLLkjhEQ4AwaGxHECQjV1QBGZYevd-nQzkVGaYPeicNfDEKwm7_cUk9ItEBoPQLsI0TX2jgQFv9ogQwqVYj6msiRnFs5S1NBfBu_Ei4y2cB1lrD4TtR41AcVoSLV1ePM8jk33GGh-ICYMGVTVBm5Y9siXb2aNyAsGX8XaJ8w_W7kshIWoKuNuJcDq6EGmFZIkajM6NFvxX1mAM36jpMIswZ3r678YkEl_Be_k1HMLHotQtUANBC1Y3THorqAiaSbi6J1W65GqlHMIUc9-NnERa9eMazNLEuPjCLmkyog5MBQ6gxrmqQxucF7TT_a6-NhM_bnY=w799-h332-no)
As shendric said:QuoteKu-Band radar (CryoSat) is sensitive to snow grain size. A larger anomaly in the snow microstructure (depth hoar, ice lenses) may result in too high freeboard/thickness values.
This is most probably the reason for this enormous divergence, as there have been so many Atlantic storms hurled into the Arctic this winter.
Here are a couple of quotes from Robert Ricker's PhD thesis paper (https://epic.awi.de/37878/1/PhD_20150312_robertricker.pdf). Abstract:QuoteRadar altimetry measurements of the current satellite mission CryoSat-2 show an increase of Arctic sea-ice thickness in autumn 2013, compared to previous years but also related to March 2013. Such an increase over the melting season seems unlikely and needs to be investigated. Recent studies show that the influence of the snow cover is not negligible and can highly affect the CryoSat-2 range measurements if it is assumed that the main scattering horizon is given by the snow-ice interface. Our analysis of Arctic ice mass-balance buoy records and coincident CryoSat-2 data between 2012 and 2014 adds observational evidence to these findings. Linear trends of snow and ice freeboard measurements from buoys and nearby CryoSat-2 freeboard retrievals are calculated during accumulation events. We find a positive correlation between buoy snow-freeboard and CryoSat-2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat-2 sea-ice thickness in autumn 2013.
Look at the December graph where you can clearly see a similar jump (relative to PIOMAS) in 2013.
More quotes:QuoteMoreover, backscatter from both interfaces superimpose each other and cause broadened radar returns, which is largest for snow depths >20 cm (Kwok, 2014). As a result, freeboard estimates can be biased high with the presence of thick snow layers.
(...)
For wet snow at the beginning and the end of the melting season, the dielectric properties of the snow layer might even limit the physical penetration of radar waves.
(...)
We hypothesize that the snow cover significantly affects the CS-2 freeboard retrieval by snow backscatter which would affect also sea-ice thickness and volume, independently of the range retrieval method.
(...)
We find high differences of up to 45 cm (30 cm) for the 40 % threshold retrieval and up to 30 cm (20 cm) for the 80 % threshold retrieval from the comparison between November 2013 and 2012 (November 2013 and March 2013) north of Canada.
(...)
It is still difficult to quantify the snow-scatter induced bias without knowledge of the regional distribution and temporal evolution of snow depth and snow stratigraphy. Snow, accumulated early, may undergo a partial melting and subsequent freezing as well as wind compaction. This leads to a very heterogeneous snow density distribution, while for the propagation of the Ku-band signal it is widely assumed that the snow density is homogeneous. In this way formed layers may affect the location of the main reflecting horizon.
(...)
We conclude that snowfall can have a significant impact on CryoSat-2 range measurements and therefore on ice freeboard, thickness and volume. The assumption that the CryoSat-2 main scattering horizon is given by the snow-ice interface cannot be justified in regions with a thick snow layer.
My preliminary conclusions:
- PIOMAS has it more right than CryoSat-2, although probably underestimating thickness slightly.
- The discrepancy is caused by either a thick snow layer, or short melt events due to heat incursions changing the snow stratigraphy, or a combination of both.
- Bad news for the ice, as 1) snow insulates, causing the ice to thicken less, 2) snow melts more easily than ice, which can set off feedback processes earlier (melt ponding, etc), especially if it has already melted for short periods during the winter-spring transition (Stoeve published a paper about this last year).
Questions:
- Is there any observational data (buoys, atmospheric data) that enables us to quantify this, or...
- Give an idea of which areas are affected most? Unfortunately PIOMAS seems to be experiencing a problem (that bulge of thick ice hovering over Fram Strait), so I don't know how useful a regional breakdown would be.
Either way, this is pretty big, IMO, as it tells us something about snow depth on the sea ice which may have consequences for the state in which the ice pack enters the melting season.
Might layered snow be a more effective insulator as there is little scope for convection though snow layers?
That would be a bad combination but 2014 melt season was poor. However the Dec 2013 mismatch had corrected by Jan 2014 so perhaps we shouldn't expect a poor 2014 melt season.
QuoteHowever the Dec 2013 mismatch had corrected by Jan 2014 so perhaps we shouldn't expect a poor 2014 melt season.
I'm not seeing that. I see the PIOMAS trend line going in a straight line from 2013 to 2015, but CryoSat still has a spike (relatively speaking) in 2014. And it's still there in February, March and April.
It will be corrected once the snow melts and there is not as much snow during the next freeze-up.
I've been able to look some more into this, with the help of Michael, who sent me the data that allowed me to create this graph:Of all this, what amazes most is your eyeballing capabilities. Man, that you were able to detect this from the Cryosat maps with the colors they have ...
...
Perhaps yes. However, I don't think you should compare only with 2013 and 2015. The Jan 2014 gap of 2.5k is similar to 2011 and all the gaps are 2.5k to 3.5k with just one exception of 2017 gap of very weird 0.4k.
With Feb, yes 2014 is the smallest gap but this is no more unusual than 2013 being the largest gap.
Thanks, Diablo, that could have something to do with it as well. Either way, both factors seem for a large part to be caused by the series of Atlantic storms. If ridging does play a large part in this, the tables would be turned and it might be an indication that there could be a rebound this year. But I'm jumping to conclusions here.Considering the deficit of cold weather, I'm at a loss as how we could possibly have a volume rebound at this juncture. The thermodynamics don't justify it.
I've notified the PIOMAS folks and they might ask around as well. So, let's see what happens.
Thanks, Diablo, that could have something to do with it as well. Either way, both factors seem for a large part to be caused by the series of Atlantic storms. If ridging does play a large part in this, the tables would be turned and it might be an indication that there could be a rebound this year. But I'm jumping to conclusions here.Considering the deficit of cold weather, I'm at a loss as how we could possibly have a volume rebound at this juncture. The thermodynamics don't justify it.
I've notified the PIOMAS folks and they might ask around as well. So, let's see what happens.
And new ice growing where the winds have pushed the ice away.I'd be more optimistic and more in agreement were it not for the continuous melting along the Atlantic and export to melt from Baffin bay and Bering Sea.
Considering the deficit of cold weather, I'm at a loss as how we could possibly have a volume rebound at this juncture. The thermodynamics don't justify it.
And new ice growing where the winds have pushed the ice away.
CryoSat-2 derived Sea Ice Thickness for February 2017 is now available so it is possible to compare them with the PIOMAS model output.
As before, PIOMAS daily gridded data compared to CPOM 2 day 1 km Sea ice Thickness.
Diference is calculated as PIOMAS minus CPOM.
There are radial striations visible in the plots which apparently line up with the orbital plane of the satellite (i.e. 92 degrees - tangential to edge of 'pole hole'). These just have to be artifacts, don't they?The radial striations are due to the CryoSat-2 Satellite having limited coverage. Adjacent grid cells may contain data dated many days apart.
There are radial striations visible in the plots which apparently line up with the orbital plane of the satellite (i.e. 92 degrees - tangential to edge of 'pole hole'). These just have to be artifacts, don't they?The radial striations are due to the CryoSat-2 Satellite having limited coverage. Adjacent grid cells may contain data dated many days apart.
The CryoSat-2 satellite has a nominal orbital periodicity of 100 minutes, which means there are 14 to 15 orbits a day. Combine that with the fact that the SIRAL instrument uses “Synthetic aperture radar altimetry” to reduce the size of the instrument footprint to approximately 0.3 km by 1.5 km along track and across track, respectively (Laxon et al 2013), means that the area covered in one day is relatively small.
To help overcome this paucity of information, AWI use a 25 km grid. While CPOM utilise a gridding procedure that gives each data point a footprint roughly 10 km in diameter on the 1 km grid, and 50 km in diameter on the 5 km grid.
To illustrate this limited coverage I have done a comparison between the PIOMAS daily gridded data and CPOM 2 day 1 km data for 27_28 February 2017, on the EASE-2 12.5 km grid.Even this is potentialy misleading because the satellite track is much narrower than shown and there are two days worth of orbital tracks.
http://onlinelibrary.wiley.com/doi/10.1002/grl.50193/abstract (http://onlinelibrary.wiley.com/doi/10.1002/grl.50193/abstract)
Maybe they've been reading here, but it seems people at the NSIDC are aware (http://nsidc.org/arcticseaicenews/2017/03/arctic-sea-ice-maximum-at-record-low/) of the situation:
Data from the European Space Agency’s CryoSat-2 satellite indicate that this winter’s ice cover may be only slightly thinner than that observed at this time of year for the past four years. However, an ice-ocean model at the University of Washington (PIOMAS) that incorporates observed weather conditions suggests the volume of ice in the Arctic is unusually low.
...and am I correct in saying that in Beaufort, at least, the (few) on-the-ground thickness measurements (from the new 2017 IMBs) indicate that PIOMAS may itself be somewhat on the high side?
...and am I correct in saying that in Beaufort, at least, the (few) on-the-ground thickness measurements (from the new 2017 IMBs) indicate that PIOMAS may itself be somewhat on the high side?
...and am I correct in saying that in Beaufort, at least, the (few) on-the-ground thickness measurements (from the new 2017 IMBs) indicate that PIOMAS may itself be somewhat on the high side?
No, as Peter pointed out. If the buoy doesn't get crushed in a ridge it should give a good idea how thermodynamic thickening (and eventually melting) goes from here. 2017A will certainly be on first year ice, but we can't say with any certainty when the floe started to freeze. < 1m seems on the low side to me for ice that's been increasing in thickness all winter long, and the Canadian Ice Service maps show thicker ice in that area. However I haven't checked location/motion/temperature yet to see if that helps explain matters.
I've been too busy winding up Anthony Watts (http://greatwhitecon.info/2017/03/alice-f-convicted-in-wuwt-show-trial/) amongst others!
An anomalous warm winter 2015-16 lead to the lowest winter ice-extent and highlights the sensitivity of the Arctic sea ice. Here, we use the 6-year record of an improved sea-ice thickness product retrieved from data fusion of CryoSat-2 radar altimetry and SMOS radiometry measurements to examine the impact of recent temperature trend on the Arctic ice-mass balance. Between November 2015 and March 2016, we find a consistent drop of cumulative freezing degree days across the Arctic, with a negative peak anomaly of about 1000 degree days in the Barents Sea, coinciding with an Arctic-wide average thinning of 10 cm in March with respect to the 6-year average. In particular, the loss of ice volume is associated with a significant decline of March first-year ice volume by 13%. This reveals that due to the loss of multiyear ice during previous years, the Arctic ice cover becomes more sensitive to climate anomalies.
...and am I correct in saying that in Beaufort, at least, the (few) on-the-ground thickness measurements (from the new 2017 IMBs) indicate that PIOMAS may itself be somewhat on the high side?
As soon as the CAA Garlic Press gets underway, all the remaining 'thick' ice will head south to oblivion. Then all that will remain is a mush of barely-one-year-old ice filling the basin. The risk of that melting out in anything like an above average melt season seems acute.+PDO has mixed things up in the Pacific. No news about RRR yet.
The atmospheric circulation arising from this year's melt season (ever more open water) re heat transport from equator to the Arctic will be very interesting to watch. Are there any signs of the Ridiculously Resilient Ridge or its relations restoring itself?
SST anomalies and wind vector anomalies continue to indicate the presence of the nega-blob, the inverse of the original blob. The overlay with the classic blob is a little eerie in its symmetry. I don't see the same obverse elsewhere, but I have no meteorological training, so I would rely on FishOutOfWater.
https://en.wikipedia.org/wiki/The_Blob_(Pacific_Ocean)
I have no idea whether the huge patch of cold water is cause or effect, but it's had plenty of effect here in California through the winter, dragging storms far enough south to give us some pretty good lashings.
Again, it seems PIOMAS places the thick blob away northbl of Greenland, while the actual very thick ice is not there but closer to the shore, as Hycom also shows.
What does that say about PIOMAS volume accuracy this year? I am quite concerned.
End of the season SMOS thickness / anomalies for comparison
Compared to the mean of all previous years since 2011. You have to click on the image to start the animation.End of the season SMOS thickness / anomalies for comparison
Is that compared to 2016?
Is it just a color choice effect, or is there different data being used for the Cryosat plot and the Cryosat vs. PIOMAS plot? The satellite pass pattern is so much more obvious in the PIOMAS-Cryosat plot that it makes me wonder if there's some sort of smoothing step thats been omitted.
For those who like to make the comparison, here is PIOMAS for the end of May, using the DMI and HYCOM CICE palettes.Finally the gif works
Do you perchance have the maps colored in terms of percent differences, ie (piomas - awi)/piomas and (piomas - awi)/awi?
Could you possibly elaborate on what 'effective ice thickness' for piomas means in terms of the model making experimentally testable predictions?
Did AWI (or anyone else) make maps yet of the original (as opposed to the difference with PIOMAS) thickness detected by Icesat?
Is it fair to say that from this animation of PIOMAS-AWI :
that at least w.r.t. Cryosat2 that PIOMAS overestimates thickness of the thinner ice on the boundaries of the Arctic basin, and possibly underestimates ice in the CAB ?
If so, it would explain some of the features in Wipneus' PIOMAS volume anomaly graph :
https://sites.google.com/site/arctischepinguin/home/piomas (https://sites.google.com/site/arctischepinguin/home/piomas)
and specifically the fast drop in June and the re-bound in July of the years that go low...
There are two sources for CryoSat-2 derived Sea Ice Thickness. The Alfred Wegener Institute (AWI), and the Centre for Polar Observation and Modelling (CPOM). I have not seen any comparison between these two products, so here is a comparison of the AWI final product and the CPOM 2 day, 1km NRT product for January to April 2017. There is no great difference between the NRT and final AWI products, so this seems a reasonable comparison.
Nevertheless, if I am reading these correctly, CPOM agrees with PIOMAS (a bit) more closely than AWI does?I have previously posted monthly comparisons of PIOMAS Sea Ice Thickness and CPOM CryoSat-2 derived Sea Ice Thickness. For convenience I am reposting them here reworked with the new colour bar.
The comparison is between PIOMAS hiday daily gridded Effective Sea Ice Thickness and the CPOM 2 day, 1 km NRT product.
The difference is calculated by subtracting the AWI value from the PIOMAS value.
Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval
Kevin Guerreiro1, Sara Fleury1, Elena Zakharova1,2, Alexei Kouraev1,2,3, Frédérique Rémy1, and Philippe Maisongrande1
Abstract. Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010–present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002–2012) still require some large improvements.
In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010–April 2011 and November 2011–March 2012). The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker.
Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD = 1.5 cm) during the two ice growth seasons and for all months of the period of study.
The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSE = 12–28 cm) is as accurate as CryoSat-2 (RMSE = 15–21 cm) and much more accurate than the uncorrected Envisat data set (RMSE = 178–179 cm).
The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period. The good agreement between the improved Envisat data set and the in situ ice draught data set (RMSE = 13–32 cm) demonstrates the potential of the PP correction to produce accurate freeboard estimates over the entire Envisat mission lifetime.
“It has been assumed by the scientific community that CryoSat-2 can accurately measure the sea ice freeboard, which is the ice we can see above sea level,” says Nandan. “But that ice is covered in snow and the snow is salty close to where the sea ice surface is. The problem is, microwave measurements from satellites don’t penetrate the salty snow very well, so the satellite is not measuring the proper sea ice freeboard and the satellite readings overestimate the thickness of the ice.”
By providing unparalleled coverage of the Northern Hemisphere, data from ESA’s CryoSat-2 mission have allowed us to produce hemisphere-wide sea ice thickness and volume estimates since November 2010. The provision of sea ice thickness data from satellite is crucial in understanding how the ice pack as a whole is changing. Here we have provided an end-to-end, comprehensive description of the processing steps that we use at CPOM to obtain estimates of Arctic sea ice thickness and volume from CryoSat-2 data, along with a detailed analysis of the uncertainties associated with our retrieval and an evaluation of our sea ice thickness product. In theory, the method presented could be used to retrieve sea ice thicknesses in the Southern Hemisphere, and it provides the foundation to develop sea ice processing systems for other Polar orbiting satellite radar altimeters.
Ideally, our uncertainty analysis would provide an error for each point measurement of sea ice thickness rather than grid cell values. This is currently hampered by a lack of knowledge regarding the correlation length scales and temporal variations of contributing factors such as snow depth and density, and sea ice density.
#CryoSat #SeaIce Feb 2018 thickness and volume update: Extent is at record low, but sea ice volume in Arctic Basin at levels of previous years (6th lowest / 3rd highest).
Hendricks asks: "Above average Jan to Feb growth due to ice dynamics?"
Hendricks asks: "Above average Jan to Feb growth due to ice dynamics?"
Actually Hendricks' graphs show that the growth in February was slightly below average. (The growth in January was above average, though).
How accurately can snow depth and density be determined from satellites alone? Evidently not that well because California still has people going out on skis to make calibration measurements, even with massive installed snow telemetry (SNOTEL). https://www.wcc.nrcs.usda.gov/products.htmlCyroSat-2 does not measure snow depth. Modified climatology is used instead. The climatology, based on Warren 1999, is modified to to allow for fact that snow thickness on FYI is approximately 50% of that on MYI.
What does it mean for Cryosat to measure monthly average ice thickness given that the icepack is in constant motion, with some regions moving much more rapidly than others? February saw displacements of a km per hour on some days.
CyroSat-2 does not measure snow depthCryoSat-2 seeks to determine the freeboard of floating ice. The primary instrument is a synthetic aperture interferometric X-band radar altimeter. Precise altimetry alone has been found not to give accurate ice thickness -- hence the other 31 companion files (such as ice density) in the netCDF under discussion.
From the netCDF file data header:QuoteCyroSat-2 does not measure snow depthThe question here is where does this netCDF file get its snow depth, snow depth uncertainty, snow density, snow density uncertainty from and how good are they. It is a near-real time winter product under active development, not a static journal article.
We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.4 ± 1.07 (2011) to 2.60 ± 1.15 m (2014), and snow depth from 30.0 ± 8.51 (2016) to 43.6 ± 10.2 cm (2014); suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2), sampled along the same transect, are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow-ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thickness computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore general higher; mean differences in sea ice thickness along a transect are up to ~ 0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice sea thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, where the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding limits of retrievals of Weddell Sea ice thickness from satellite altimeters.
Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.
Ice thickness anomalies anomalies 2018 relative to 2011-2017 (Fig 6) show widespread negative anomalies and with thicker than normal ice only in the eastern Beaufort Sea. Thick ice in this area is to anomalous ice motion over the last 4 month that pushed sea ice against Banks Island and the western part of the Canadian Archipelago (Fig 7). This thickness anomaly pattern is supported by CryoSat thickness anomalies using the new version 2.1 from AWI. CryoSat thickness anomalies (Fig 9) are similar to PIOMAS but there are substantial differences in the Lincoln Sea and North of Fram Strait where CryoSat has positive anomalies. PIOMAS and CryoSat time series for October times series show little further decline in October sea ice volume since the exceptionally low values first seen in 2011 and 2012 (Figure 10).
Well this region has been very cold this winter, but we can only guess if this has translated into extra thickness. From Lebedev ice growth formula we know that sea ice over 2m grows very slowly no matter how cold the air is.I'm HIGHLY suspicious of any model that shows large contiguous areas of ice with a thickness in excess of 3M, for exactly the reason you suggest.
All I know for sure is that my AMSR2 thickness is high as well, but it is definitely affected by snow.
You can not trust these AMSR2 thickness maps. From these kind of microwave radiometer you can just derive a very uncertain proxy. For AMSR2 frequencies the penetration depth is not more than a few centimeters, therefore the signal comes just from the sea ice surface. It is rather related to snow grain sizes, layering and sea ice surface salinity.Well this region has been very cold this winter, but we can only guess if this has translated into extra thickness. From Lebedev ice growth formula we know that sea ice over 2m grows very slowly no matter how cold the air is.I'm HIGHLY suspicious of any model that shows large contiguous areas of ice with a thickness in excess of 3M, for exactly the reason you suggest.
All I know for sure is that my AMSR2 thickness is high as well, but it is definitely affected by snow.
The Centre for Polar Observation and Monitoring have just published the first CryoSat-2 Arctic sea ice thickness map of the 2019/20 freezing season:
http://GreatWhiteCon.info/2019/10/facts-about-the-arctic-in-october-2019/#Oct-16QuoteNote in particular the dark blue area north of the Canadian Arctic Archipelago.
It is good to have it on this topic:The CryoSat-2 image is from the laser thingy, from which estimates of freeboard are generated from which estimates of thickness are made, i.e. starting with a physical measure of the ice, not from a model ?The Centre for Polar Observation and Monitoring have just published the first CryoSat-2 Arctic sea ice thickness map of the 2019/20 freezing season:
http://GreatWhiteCon.info/2019/10/facts-about-the-arctic-in-october-2019/#Oct-16QuoteNote in particular the dark blue area north of the Canadian Arctic Archipelago.
The CryoSat-2 image is from the laser thingy?
And do we have any idea on the reliability of the assumptions of snow thickness ?The CryoSat-2 image is from the laser thingy?
IceSat-2 is "the laser thingy". CryoSat-2 is the "interferometric radar range-finder thingy":
https://en.wikipedia.org/wiki/CryoSat-2
It's a "measurement" of sorts, but still has to make assumptions about snow thickness to turn freeboard measurements into sea ice thickness numbers.
Do we have any info if the Polar Science Center are doing comparisons of their model data with CryoSat-2 data?
In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS).
<snip>concentration in this instance is a measurement of surface area and does not correlate to thickness or volume in any respect. Specifically it is the fraction of surface area that has ice in it per unit area.
Not only do the thickness maps vary, but it seems strange to see 2 meter thick ice in some images at same location of well below 100% concentration in other images.
<snip>
concentration in this instance is a measurement of surface area and does not correlate to thickness or volume in any respect. Specifically it is the fraction of surface area that has ice in it per unit area.
Thickness is a measurement that is perpendicular to the surface area.
In real terms you can have 5m thick ice that only covers 3% of the surface
<snip> concentration in this instance is a measurement of surface area and does not correlate to thickness or volume.
PIOMAS vs. JAXA AMSR2 thickness (vs. Bremen, NSIDC, ASCAT concentration.)In cleaning up the previous message I accidentally replaced the PIOMAS link. Too much time gone by so now I can't edit that message anymore, but for the historical record here is the correct link to the message showing PIOMAS thickness. Sorry for my dyslexic typing.
April ice thickness anomalies from PIOMAS agree well with the multi-sensor CryoSat/SMOS thickness analysis from the Alfred Wegener Institute/ESA (Fig 7) with the strongest positive and negative anomalies in the right places. An area of thicker than normal ice north of Greenland that was present in PIOMAS but missing from CryoSat/SMOS in March is now is now showing up in Cryosat/SMOS though considerably smoothed out. The time series for CryoSat/SMOS total volume shows April 2020 a lower relative to the 2011-2020 period while PIOMAS shows a bit of an uptick. Neither time series indicates a trend over the past 10 years contrasting the drastic thinning over the last 40-years. Note that Cryosat/SMOS retrievals only go through April 15 as the microwave based retrieval of both system forces a summer hiatus.