Justin Beckers

Evidence of Arctic sea ice thinning from direct observations

The Arctic sea ice cover is rapidly shrinking, but a direct, longer-term assessment of the ice thinning remains challenging. A new time series constructed from in situ measurements of sea ice thickness at the end of the melt season in Fram Strait shows a thinning by over 50% during 2003-2012. The modal and mean ice thickness along 79 degrees N decreased at a rate of 0.3 and 0.2 m yr(-1), respectively, with long-term averages of 2.5 and 3 m. Airborne observations reveal an east-west thickness gradient across the strait in spring but not in summer due to advection from more different source regions. There is no clear relationship between interannual ice thickness variability and the source regions of the ice. The observed thinning is therefore likely a result of Arctic-wide reduction in ice thickness with a potential shift in exported ice types playing a minor role.

Large-scale ice thickness distribution of first-year sea ice in spring and summer north of Svalbard

Abstract The large-scale thickness distribution of sea ice was measured during several campaigns in the European Arctic north of Svalbard from 2007 using an airborne electromagnetic induction device. In August 2010 and April-May 2011, this was complemented by extensive on-ice work including measurements of snow thickness and freeboard. Ice thicknesses show a clear difference between the seasons, with thicker ice during spring than in summer. In spring 2011, negative freeboard and flooding were observed as a result of the extensive snow cover. We find that the characteristics of the first-year sea ice allow combining observations from different years. The ice thickness in the marginal ice zone increases with increasing latitude and increasing distance to the ice edge; however, in the inner ice pack from ∼100 km from the ice edge the thickness remains almost constant. Modal ice thickness in spring reaches 2.4 m whereas in summer it is 1.0–1.4 m. Our study provides new insight into ice thickness distributions of a typical ice cover consisting of mainly first- and second-year ice, which may become the dominant ice type in the Arctic in the future.

Evaluation of Operation IceBridge quick‐look snow depth estimates on sea ice

We evaluate Operation IceBridge (OIB) ‘quick-look’ (QL) snow depth on sea ice retrievals using in situ measurements taken over immobile first-year ice (FYI) and multi-year ice (MYI) during March of 2014. Good agreement was found over undeformed FYI (-4.5 cm mean bias) with reduced agreement over deformed FYI (-6.6 cm mean bias). Over MYI, the mean bias was -5.7 cm but 54% of retrievals were discarded by the OIB retrieval process as compared to only 10% over FYI. Footprint scale analysis revealed a root mean square error (RMSE) of 6.2 cm over undeformed FYI with RMSE of 10.5 cm and 17.5 cm in the more complex deformed FYI and MYI environments. Correlation analysis was used to demonstrate contrasting retrieval uncertainty associated with spatial aggregation and ice surface roughness.

Distinct bacterial assemblages reside at different depths in Arctic multiyear sea ice.

Bacterial communities in Arctic sea ice play an important role in the regulation of nutrient and energy dynamics in the Arctic Ocean. Sea ice has vertical gradients in temperature, brine salinity and volume, and light and UV levels. Multiyear ice (MYI) has at least two distinct ice layers: old fresh ice with limited permeability, and new saline ice, and may also include a surface melt pond layer. Here, we determine whether bacterial communities (1) differ with ice depth due to strong physical and chemical gradients, (2) are relatively homogenous within a layer, but differ between layers, or (3) do not vary with ice depth. Cores of MYI off northern Ellesmere Island, NU, Canada, were subsectioned in 30-cm intervals, and the bacterial assemblage structure was characterized using 16S rRNA gene pyrotag sequencing. Assemblages clustered into three distinct groups: top (0-30 cm); middle (30-150 cm); and bottom (150-236 cm). These layers correspond to the occurrence of refrozen melt pond ice, at least 2-year-old ice, and newly grown first-year ice at the bottom of the ice sheet, respectively. Thus, MYI houses multiple distinct bacterial assemblages, and in situ conditions appear to play a less important role in structuring microbial assemblages than the age or conditions of the ice at the time of formation.

Comparing Springtime Ice-Algal Chlorophyll a and Physical Properties of Multi-Year and First-Year Sea Ice from the Lincoln Sea

With near-complete replacement of Arctic multi-year ice (MYI) by first-year ice (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of sea ice associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln Sea during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-ice portions of MYI, upper old-ice portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial extent and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic sea ice algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus ice) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom ice algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest ice with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on ice–associated production than generally assumed.

Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard

Abstract We present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data

In this study, we investigate the influence of Arctic sea ice surface roughness on full polarimetric C-band SAR features in the late summer season. Surface roughness measured from a helicopter-borne laser altimeter is compared to 14 individual SAR features holding a documented theoretical or empirical relation to surface roughness. Three C-band Radarsat-2 SAR scenes, covering a study area with both first-year ice and old ice surfaces with a variety of roughnesses are included in the study. Nine of the individual investigated SAR features were, in at least one of the scenes, correlated to laser altimeter measured root-mean-square height with a Spearmans correlation coefficient better than ±0.6. The strength of the correlation varied from scene to scene, and the between-scene variation was explained by differences in incidence angle, changes in the meteorological conditions, and changes in the microscale surface roughness of both the sea ice and the melt pond surfaces. We highlight that knowledge of temperature and weather history could improve the quality of surface roughness interpretation in SAR scenes in the late summer. The findings are important for improved interpretation of satellite SAR sea ice imagery, relevant to, e.g., shipping and detection of sea ice changes in the context of climate research and monitoring.

Ice and Snow Thickness Variability and Change in the High Arctic Ocean Observed by In Situ Measurements

In April 2017, we collected unique, extensive in situ data of sea ice and snow thickness. At 10 sampling sites, located under a CryoSat-2 overpass, between Ellesmere Island and 87.1°N mean and modal total ice thicknesses ranged between 2 to 3.4 m and 1.8 to 2.9 m, respectively. Coincident snow thicknesses ranged between 0.3 to 0.47 m (mean) and 0.1 to 0.5 m (mode). The profile spanned the complete multiyear ice zone in the Lincoln Sea, into the first-year ice zone farther north. Complementary snow thickness measurements near the North Pole showed a mean thickness of 0.31 m. Compared with scarce measurements from other years, multiyear ice was up to 0.75 m thinner than in 2004, but not significantly different from 2011 and 2014. We found excellent agreement with a commonly used snow climatology and with published long-term ice thinning rates. There was reasonable agreement with CryoSat-2 thickness retrievals.

Bacterial communities from Arctic seasonal sea ice are more compositionally variable than those from multi-year sea ice

Arctic sea ice can be classified into two types: seasonal ice (first-year ice, FYI) and multi-year ice (MYI). Despite striking differences in the physical and chemical characteristics of FYI and MYI, and the key role sea ice bacteria play in biogeochemical cycles of the Arctic Ocean, there are a limited number of studies comparing the bacterial communities from these two ice types. Here, we compare the membership and composition of bacterial communities from FYI and MYI sampled north of Ellesmere Island, Canada. Our results show that communities from both ice types were dominated by similar class-level phylogenetic groups. However, at the operational taxonomic unit (OTU) level, communities from MYI and FYI differed in both membership and composition. Communities from MYI sites had consistent structure, with similar membership (presence/absence) and composition (OTU abundance) independent of location and year of sample. By contrast, communities from FYI were more variable. Although FYI bacterial communities from different locations and different years shared similar membership, they varied significantly in composition. Should these findings apply to sea ice across the Arctic, we predict increased compositional variability in sea ice bacterial communities resulting from the ongoing transition from predominantly MYI to FYI, which may impact nutrient dynamics in the Arctic Ocean.

The Impact of Geophysical Corrections on Sea-Ice Freeboard Retrieved from Satellite Altimetry

Satellite altimetry is the only method to monitor global changes in sea-ice thickness and volume over decades. Such missions (e.g., ERS, Envisat, ICESat, CryoSat-2) are based on the conversion of freeboard into thickness by assuming hydrostatic equilibrium. Freeboard, the height of the ice above the water level, is therefore a crucial parameter. Freeboard is a relative quantity, computed by subtracting the instantaneous sea surface height from the sea-ice surface elevations. Hence, the impact of geophysical range corrections depends on the performance of the interpolation between subsequent leads to retrieve the sea surface height, and the magnitude of the correction. In this study, we investigate this impact by considering CryoSat-2 sea-ice freeboard retrievals in autumn and spring. Our findings show that major parts of the Arctic are not noticeably affected by the corrections. However, we find areas with very low lead density like the multiyear ice north of Canada, and landfast ice zones, where the impact can be substantial. In March 2015, 7.17% and 2.69% of all valid CryoSat-2 freeboard grid cells are affected by the ocean tides and the inverse barometric correction by more than 1 cm. They represent by far the major contributions among the impacts of the individual corrections.