1. Assessment of Antarctic sea ice thickness in the ORA-IP
Barcelona, June 2016
Assessment of Antarctic sea ice thickness in the ORA-IP
Contribution to Evaluation of Ocean Syntheses action in the Polar Regions
François Massonnet

2. Antarctic sea ice thickness at a glance
Among all essential climate variables, Antarctic sea ice thickness (AA-SIT) is probably one with poorest coverage.
AA-SIT is the product of a subtle balance between large atmospheric and oceanic heat fluxes and is almost entirely seasonal. The Antarctic pack is essentially divergent and subject to very powerful negative feedbacks; ice is thin (~1 m on avg).
Recent satellite technology has allowed SIT retrievals but large uncertainties remain due to substantial snow load.
The proportion of deformed, and therefore locally thick Antarctic sea ice, is probably more deformed than previously thought (Williams et al., Nat. Geosci. 2015)
It is important to assess to what extent current reanalyses simulate realistically (or not) Antarctic sea ice thickness. If they do, these reanalyses can help to estimate the mass balance of sea ice (and perhaps more) in the Southern Hemisphere.

3. ASPeCt thickness: heterogeneous and subject to thin bias
ASPeCt: “Antarctic Sea Ice Processes and Climate”
A unique data set: , all sectors of Antarctica
81 voyages + 2 helicopter flights nearly everywhere
Over 23,000 individual measurements of sea ice thickness
Level ice thickness, weighted for open water, is considered here (equal to sea ice volume divided by area of region of sampling, including open water, excluding deformed ice).
Data is subject to large uncertainties (and error statistics are not provided)
Representativity error: data were collected during localized expeditions
Systematic bias: towards thin ice (ice-breaker-based measurements)
Measurement error: “The thickness is estimated on the level parts of floes when they are broken and turned sideways along the hull of the ship” (Worby et al., JGR, 2008). In addition the snow/ice interface is difficult to localize visually from a ship.
A careful evaluation process has to be proposed
More info on ASPeCt: Worby et al., JGR, 2008

4. Rebinning ASPeCt data
All 23,391 daily ASPeCt measurements ( ) were first binned into 1°x1° grid boxes, for each month of each year. The mean, min, max, and number of data for each bin were retained. The number of daily observations follows a power law:
That is, there are less than three measurements available for 50% of the cases, meaning that any obs-model comparison must account for possible sampling issues.
By comparison, ORA-IP reanalyses provide the monthly-mean sea ice thickness over 1°x1° boxes.

5. Are the ORA-IP compatible with the ASPeCt data?
For each grid box and each month, we want to verify whether the collected ASPeCt samples are compatible (statistically speaking) with the mean provided by some ORA-IP reanalysis.
When only one, two or three observations are available (~50% of the cases), no evaluation is conducted: sample size is just too small and nothing robust can be concluded since nothing is known about the variance of the SIT distribution.
When four or more observations, the ORA-IP is deemed successful it its mean value lies within the range of ASPeCt data. Assuming symmetrical PDF, the probability for Type-I error is 12.5% (1 / 24 – 1)
Note: the test has limitations!
ORA-IP is compatible with ASPeCT
The test is positive
ORA-IP mean value, assuming it is correct
PDF of true thickness
BUT
ORA-IP is compatible with ASPeCt
The test is positive
Five ASPeCt measurements
Or, stated differently: a negative test indicates mismatch but a positive test does not necessarily indicate a match.

6. Are the ORA-IP compatible with the ASPeCt data?
For each month of each year, and each 1°x1° grid box,
If less than 3 observations are available during that month and at that location, no assessment is conducted
Else, « 1 » is assigned to that grid box for that month if the reanalyzed SIT is within the ASPeCt range (« 0 » otherwise)
The number of « succeed » and « fail » is cumulated
Reanalysis value: succeeds (within range)
Example July 1995 1° 1° 1
Ice thickness
ASPeCt measurements
Reanalysis value: fails (outside range)
Reanalysis value: undefined (not enough ASPeCt data)
Ice thickness
ASPeCt measurements
ASPeCt measurements
Ice thickness

7. Compatibility index # successes Compatibility index =
# successes + # fails
Here: CI = 3 / (3 + 4) = 0.43

8. Ensemble of reanalyses considered
Data were downloaded from the ORA-IP FTP: ftp://ftp.icdc.zmaw.de/ora_ip/
Thanks to those who organized this server and the datasets!
The evaluation is conducted on (longest common period between reanalyses and ASPeCt data).
Label
Institution
Compatibility index
Mean abs error (cm)
GloSea5
UK Met Office
0.44 13
GECCO2
U. Hamburg
0.28 22
ECDA
GFDL
0.48
GLORYSv1
MERCATOR
0.42
GLORYSv3
0.43
C-GLORS
CMCC
0.49
ECCO
NASA 12
Given the heterogeneity of the ASPeCt data set, it is difficult to make absolute statements. Nevertheless, none of the reanalysis simulates more than 50% of the time a SIT value that lies within the ASPeCt range. The ORA-IPs cannot be considered consistent with that dataset (Type-I probability of error: 12.5%)

9. For further information please contact
Thank you!
For further information please contact