University of Wisconsin–Madison
Spatial Analysis For Conservation and Sustainability

Evaluating Trends in Arctic Sea Ice Loss Using the Multi-Decadal SMMR/SMMI Passive Microwave Archive (1979-2020)

Posted 02/8/21

We analyzed the SMMR/SMMI archive (1979-2020) to assess trends in Arctic sea ice extent. We quantified sea ice extent as the number of days of ice coverage in 25-km pixels in September, when Arctic sea ice is at its minima. By fitting constrained least squares regression, we accounted for temporal autocorrelation in the trends. Three regions; the Beaufort Sea, the Laptev Sea, and Prince Patrick Island were found to have highly autocorrelated trends in sea ice loss.

Maximum Arctic sea ice extent (million km^2) during the month of September (1979 – 2018).
Maximum Arctic sea ice extent (million km^2) during the month of September (1979 – 2018).

Rapid warming of the Arctic is expected to cause substantial loss arctic sea ice volume and surface area. This will have numerous ecological and economic implications including habitat loss for sensitive species such as Polar Bear and Walrus, accelerated heating of the Arctic Ocean due to lowered albedo, and heightened shipping activity within the Arctic due to the increased availability of ice-free ports.

Sea ice loss has already been rapid, particularly during the summer months (Figure 1), and that this trend will only accelerate. The question is when and when exactly these losses occurred, and if observed trends are statistically significant. A major reason why this is difficult to ascertain is spatial and temporal autocorrelation, that is the influence nearby ice, and of previous ice abundance has on current conditions. We account for autocorrelation in our analyses of Arctic sea ice loss using a constrained least squares autoregressive model that predicts ice abundance based on the prior year’s conditions.

We found that three regions have experienced significant and highly temporally autocorrelated sea ice loss; a) the Laptev Sea near Severnaya Zemlya, Russia, b) the Beaufort Sea near northeastern Alaska, USA, and c) the Arctic Ocean near Prince Patrick island, Canada (Maps 1, 2, 3). Our next step is to apply a Generalized Least Squares model intended to detect spatial patterns in trends of sea ice loss.

T-Scores corresponding to trends in September Arctic sea ice extent (1979 – 2020) Negative values correspond to decreased sea ice abundance.
T-Scores corresponding to trends in September Arctic sea ice extent (1979 – 2020) Negative values correspond to decreased sea ice abundance.
Temporal autocorrelation in September Arctic sea ice extent (1979 – 2020). Extreme values correspond to higher degrees of temporal autocorrelation in trend of sea ice abundance.
Temporal autocorrelation in September Arctic sea ice extent (1979 – 2020). Extreme values correspond to higher degrees of temporal autocorrelation in trend of sea ice abundance.
Mean days of sea ice presence for the month of September (1979 – 2020) Negative values correspond to decreased sea ice abundance.
Mean days of sea ice presence for the month of September (1979 – 2020) Negative values correspond to decreased sea ice abundance.

 

Story by Stephens, Connor