The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows

Abstract

Abstract. The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness” of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, “New Arctic”, sea ice regime.

Variations in Rates of Biological Production in the Beaufort Gyre as the Arctic Changes: Rates From 2011 to 2016

“…Sea ice is melting, with sea ice in recent years consistently being 2 standard deviations below the previous 30-year mean. In particular, all 10 lowest Arctic September sea ice extents compared to the past several decades were measured in the last 10 years (Petty et al, 2018) and the Arctic is expected to be ice-free in summertime by the middle of this century (Jahn et al, 2016). Additionally, terrigenous input is increasing (Abbott et al, 2016), the seawater is freshening (Carmack et al, 2016), and temperatures are warming (Boisvert & Stroeve, 2015).…”

Warm Arctic, Increased Winter Sea Ice Growth?

“…Myriad processes contribute to sea ice growth and melt, with arguably more attention given to sea ice melt processes given the more rapid declines observed in summer, and ongoing debates around the timing of an ice-free Arctic (e.g., Notz & Marotzke, 2012;Notz & Stroeve, 2016;Perovich et al, 2007;Stroeve et al, 2011;Tsamados et al, 2015). However, recent winters in the Arctic have featured record high air temperatures and record low sea ice conditions (e.g., Boisvert et al, 2016;Cullather et al, 2016;Graham et al, 2017;Petty et al, 2018), motivating increased discussion of winter Arctic sea ice thickness and growth variability (e.g., Ricker, Hendricks, Girard-Ardhuin, et al, 2017;Stroeve et al, 2018). Reductions in end of winter ice volume can also precondition the ice pack for enhanced summer ice loss, increasing the potential for new record low summer minima and a possible ice-free Arctic.…”

The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

“…Here we adapt the ice concentration budget approach used in e.g. Holland and Kimura (2016) (and more recently in Petty et al, 2018) to snow volume as 5…”

“…Due to differences in satellite orbit and sensor characteristics, 20 the SIC data feature a time-varying pole hole depending on the passive microwave sensor used. As we require consistent SIC data across the pole hole, we follow the approach of Petty et al, (2018) …”

“…We grid the daily ice drift data onto our 100 km model domain (using linear interpolation) and smooth the data using a simple Gaussian filter (as in Kimura, 2016 andPetty et al, 2018).…”