MASIE: “high-resolution, accurate charts of ice conditions”
Walt Meier, NSIDC, October 2015 article in Annals of Glaciology.
I’ve been waiting for September 30 results to compare the monthly average for this year with previous ones. But the remarkable rate of refreezing in the Arctic needs reporting. MASIE counts ice extent using 40% coverage of 4k km2 grid cells, making it the highest resolution dataset. As well, it incorporates estimates from satellite passive microwave sensors, supplemented with satellite imagery and reports from buoys and ships.
The red line is September 2007, which was the lowest in the last 10 years, except for 2012 which was hit by the great Arctic Cyclone. More importantly, 2007 had the smallest annual average ice extent in the MASIE record (since 2006). The blue line is the ten-year average for days in September (2006 to 2015 inclusive). MASIE 2015 is in purple, MASIE 2016 in green, and 2016 NOAA SII (Sea Ice Index) is in yellow.
While the minimums all occurred days 260 to 262, 2007 extents were already trending lower, and presently the other four measures are converging. Since the September rate of regaining ice was at a decadal high in 2015, it is remarkable for 2016 to be improving on that. Since 2007 will end the month close to where it is now, we can project that 2016 monthly average will be considerably higher, likely to exceed also 2008. With SII virtually tied with MASIE, that index will also be showing a September average well over 4.4M km2.
With 2016 ice extents surging, we can project that Arctic ice has continued on a flat or slightly increasing trendline with no evidence of a decline since 2007.
Why the Discrepancy between SII and MASIE?
The issue also concerns Walter Meier who is in charge of SII, and as a true scientist, he is looking to get the best measurements possible. He and several colleagues compared SII and MASIE and published their findings last October. The purpose of the analysis was stated thus:
Our comparison is not meant to be an extensive validation of either product, but to illustrate as guidance for future use how the two products behave in different regimes.
The Abstract says:
Passive microwave sensors have produced a 35 year record of sea-ice concentration variability and change. Operational analyses combine a variety of remote-sensing inputs and other sources via manual integration to create high-resolution, accurate charts of ice conditions in support of navigation and operational forecast models. One such product is the daily Multisensor Analyzed Sea Ice Extent (MASIE). The higher spatial resolution along with multiple input data and manual analysis potentially provide more precise mapping of the ice edge than passive microwave estimates. However, since MASIE is based on an operational product, estimates may be inconsistent over time due to variations in input data quality and availability. Comparisons indicate that MASIE shows higher Arctic-wide extent values throughout most of the year, largely because of the limitations of passive microwave sensors in some conditions (e.g. surface melt). However, during some parts of the year, MASIE tends to indicate less ice than estimated by passive microwave sensors. These comparisons yield a better understanding of operational and research sea-ice data products; this in turn has important implications for their use in climate and weather models.
The whole document is informative and worth the read.
For instance MASIE is described thus:
Human analysis of all available input imagery, including visible/infrared, SAR, scatterometer and passive microwave, yields a daily map of sea-ice extent at a 4 km gridded resolution, with a 40% concentration threshold for the presence of sea ice. In other words, if a gridcell is judged by an analyst to have >40% of its area covered with ice, it is classified as ice; if a cell has <40% ice, it is classified as open water.
The fact that MASIE employs human judgment is discomforting to climatologists as a potential source of error, so Meier and others prefer that the analysis be done by computer algorithms. Yet, as we shall see, the computer programs are themselves human inventions and when applied uncritically by machines produce errors of their own.
The passive microwave sea-ice algorithms are capable of distinguishing three surface types (one water and two ice), and the standard algorithms are calibrated for thick first-year and multi-year ice (Cavalieri, 1994). When thin ice is present, the algorithms underestimate the concentration of new and thin ice, and when such ice is present in lower concentrations they may detect only open water. The underestimation of concentration and extent of thin-ice regions has been noted in several evaluation studies. . .Melt is another well-known cause of underestimation of sea ice by passive microwave sensors.
The paper by Meier et al. is a good analysis, as far as it goes. In a post NOAA is Losing Arctic Ice I showed the gory details and brought the comparison up to date.