Mapping and assessing variability in the Antarctic Marginal Ice Zone, the Pack Ice and coastal Polynyas
Résumé
Sea ice variability within the marginal ice zone
(MIZ) and polynyas plays an important role for phytoplankton
productivity and krill abundance. Therefore, mapping
their spatial extent as well as seasonal and interannual variability
is essential for understanding how current and future
changes in these biologically active regions may impact the
Antarctic marine ecosystem. Knowledge of the distribution
of MIZ, consolidated pack ice and coastal polynyas in the
total Antarctic sea ice cover may also help to shed light
on the factors contributing towards recent expansion of the
Antarctic ice cover in some regions and contraction in others.
The long-term passive microwave satellite data record
provides the longest and most consistent record for assessing
the proportion of the sea ice cover that is covered by each
of these ice categories. However, estimates of the amount of
MIZ, consolidated pack ice and polynyas depend strongly on
which sea ice algorithm is used. This study uses two popular
passive microwave sea ice algorithms, the NASA Team and
Bootstrap, and applies the same thresholds to the sea ice concentrations
to evaluate the distribution and variability in the
MIZ, the consolidated pack ice and coastal polynyas. Results
reveal that the seasonal cycle in the MIZ and pack ice is generally
similar between both algorithms, yet the NASA Team
algorithm has on average twice the MIZ and half the consolidated
pack ice area as the Bootstrap algorithm. Trends also
differ, with the Bootstrap algorithm suggesting statistically
significant trends towards increased pack ice area and no statistically
significant trends in the MIZ. The NASA Team algorithm
on the other hand indicates statistically significant
positive trends in the MIZ during spring. Potential coastal
polynya area and amount of broken ice within the consolidated
ice pack are also larger in the NASA Team algorithm.
The timing of maximum polynya area may differ by as much
as 5 months between algorithms. These differences lead to
different relationships between sea ice characteristics and biological
processes, as illustrated here with the breeding success
of an Antarctic seabird.