Search for: All records

Data collected by two buoy arrays that operated during the ice seasons of 2014/2015 and 2016/2017 were used to characterize annual cycles of ice motion and deformation in the western Arctic Ocean. An anomalously strong and weak Beaufort Gyre in 2014/2015 and 2016/2017 induced generally anticyclonic and cyclonic sea ice drift during 2014/2015 and 2016/2017, respectively. Cyclonic ice motion resulted in higher contributions of ice divergence to total ice deformation in 2016/2017 than in 2014/2015. In 2014, the autumn ice concentration and multiyear ice coverage were higher than in 2016; consequently, the response of ice motion to wind forcing was weak, and less ice deformation was observed in autumn 2014. During the autumn‐winter transition, the ice‐wind speed ratio, ice deformation rate and its spatial and temporal scaling exponents, and localization of ice deformation decreased markedly in both 2014/2015 and 2016/2017 as a result of freeze‐up and consolidation of ice floes. Such dynamic behavior was maintained through to spring with the further thickening of ice cover. Ice deformation increased due to weakened ice strength as summer approached. The amplitude of the annual cycle of ice deformation rate in the western Arctic Ocean in 2014/2015 and especially in 2016/2017 was larger than that observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) program in 1997/1998. We attribute this phenomenon to ice loss during the recent summers, especially of thick multiyear ice.

 

Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided a unique opportunity to study seasonal sea ice thickness changes of the same sea ice. We analyzed 11 large-scale (∼50 km) airborne electromagnetic sea thickness and surface roughness surveys from October 2019 to September 2020. Data from ice mass balance and position buoys provided additional information. We found that thermodynamic growth and decay dominated the seasonal cycle with a total mean sea ice thickness increase of 1.4 m (October 2019 to June 2020) and decay of 1.2 m (June 2020 to September 2020). Ice dynamics and deformation-related processes, such as thin ice formation in leads and subsequent ridging, broadened the ice thickness distribution and contributed 30% to the increase in mean thickness. These processes caused a 1-month delay between maximum thermodynamic sea ice thickness and maximum mean ice thickness. The airborne EM measurements bridged the scales from local floe-scale measurements to Arctic-wide satellite observations and model grid cells. The spatial differences in mean sea ice thickness between the Central Observatory (<10 km) of MOSAiC and the Distributed Network (<50 km) were negligible in fall and only 0.2 m in late winter, but the relative abundance of thin and thick ice varied. One unexpected outcome was the large dynamic thickening in a regime where divergence prevailed on average in the western Nansen Basin in spring. We suggest that the large dynamic thickening was due to the mobile, unconsolidated sea ice pack and periodic, sub-daily motion. We demonstrate that this Lagrangian sea ice thickness data set is well suited for validating the existing redistribution theory in sea ice models. Our comprehensive description of seasonal changes of the sea ice thickness distribution is valuable for interpreting MOSAiC time series across disciplines and can be used as a reference to advance sea ice thickness modeling. 

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond. 

Abstract The accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant ( p < 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (<11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation. 

Abstract. The objective of this note is to provide the backgroundand basic tools to estimate the statistical error of deformation parametersthat are calculated from displacement fields retrieved from syntheticaperture radar (SAR) imagery or from location changes of position sensors inan array. We focus here specifically on sea ice drift and deformation. Inthe most general case, the uncertainties of divergence/convergence, shear,vorticity, and total deformation are dependent on errors in coordinatemeasurements, the size of the area and the time interval over which theseparameters are determined, as well as the velocity gradients within the boundary ofthe area. If displacements are calculated from sequences of SAR images, atracking error also has to be considered. Timing errors in position readingsare usually very small and can be neglected. We give examples for magnitudesof position and timing errors typical for buoys and SAR sensors, in thelatter case supplemented by magnitudes of the tracking error, and apply thederived equations on geometric shapes frequently used for derivingdeformation from SAR images and buoy arrays. Our case studies show that thesize of the area and the time interval for calculating deformationparameters have to be chosen within certain limits to make sure that theuncertainties are smaller than the magnitude of deformation parameters.