scholarly journals Arctic sea ice drift-strength feedback modelled by NEMO-LIM3.6

2017 ◽  
Author(s):  
David Docquier ◽  
François Massonnet ◽  
Neil F. Tandon ◽  
Olivier Lecomte ◽  
Thierry Fichefet
Keyword(s):  
Sea Ice ◽  
Positive Feedback ◽  
Seasonal Cycle ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ice Strength

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. The resulting increased sea ice export is thought to further lower sea ice concentration and thickness. We use the global ocean-sea ice NEMO-LIM3.6 model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite and buoy observations, as well as reanalysis data over the period from 1979 to 2013 to study this positive feedback for the first time in such detail. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. Although the seasonal cycle of sea ice drift speed is reasonably well reproduced by the model, the recent positive trend in drift speed is weaker than observations in summer. NEMO-LIM3.6 is able to capture the relationships between sea ice drift speed, concentration and thickness in terms of seasonal cycle, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Sensitivity experiments are carried out by varying the initial ice strength and show that higher values of ice strength lead to lower sea ice thickness. We demonstrate that higher ice strength results in a more uniform sea ice thickness distribution, leading to lower heat conduction fluxes, which provide lower ice production, and thus lower ice thickness. This shows that the positive feedback between sea ice drift speed and strength is more than just dynamic, more complex than originally thought and that other processes are at play. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the interactions between sea ice drift speed and strength.

Farewell to IceBridge: 10 years of polar sea ice remote sensing

2020 ◽  
Author(s):  
Linette Boisvert ◽  
Joseph MacGregor ◽  
Brooke Medley ◽  
Nathan Kurtz ◽  
Ron Kwok ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
Laser Altimeters ◽  
Sea Ice Drift ◽  
To Come ◽  
The Antarctic

<p>NASA’s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by ‘bridging the gap’ between NASA’s ICESat (2003–2009) and ICESat-2 (launched September 2018) satellite missions. OIB’s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community’s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA’s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane’s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.</p><p> </p>

scholarly journals Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

2018 ◽  
Author(s):  
Jiping Xie ◽  
Francois Counillon ◽  
Larent Bertino
Keyword(s):  
Sea Ice ◽  
Degrees Of Freedom ◽  
The Arctic ◽  
Ice Thickness ◽  
Large Set ◽  
Sea Ice Thickness ◽  
Ice Drift ◽  
Accurate Forecast ◽  
Sea Ice Drift ◽  
The Impact

Abstract. Accurate forecast of Sea Ice Thickness (SIT) represents a major challenge for Arctic forecasting systems. The new CS2SMOS SIT product merges measurements from the CryoSat-2 and SMOS satellites and is available weekly during the winter months since October 2010. The impact of assimilating CS2SMOS is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Service (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted with and without assimilation of the previously weekly CS2SMOS for the period from 19th March 2014 to 31st March 2015. The SIT bias (too thin) is reduced from 16 cm to 5 cm and the RMSD decreases from 53 cm to 38 cm (reduction by 28 %) when compared to the simultaneous SIT from CS2SMOS. Furthermore, compared to independent SIT observations, the errors are reduced by 24 % against the Ice Mass Balance (IMB) buoy 2013F and by 11 % against SIT data from the IceBridge campaigns. When compared to sea ice drift derived from International Arctic Buoy Program (IABP) drifting buoys, we find that the assimilation of C2SMOS is beneficial in the sea ice pack areas, where the influence of SIT on the sea ice drift is strongest, with an error reduction of 0.2–0.3 km/day. Finally, we quantify the influence of C2SMOS compared to the other assimilated data by the number of Degrees of Freedom for Signal (DFS) and find that CS2SMOS is the main source of observations in the central Arctic and in the Kara Sea. These results suggest that C2SMOS observations should be included in Arctic reanalyses in order to improve the ice thickness and the ice drift, although some inconsistencies were found in the version of the data used.

scholarly journals Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

2017 ◽  
Vol 11 (6) ◽  
pp. 2829-2846 ◽  
Author(s):  
David Docquier ◽  
François Massonnet ◽  
Antoine Barthélemy ◽  
Neil F. Tandon ◽  
Olivier Lecomte ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Global Ocean ◽  
Model Intercomparison ◽  
Drift Speed ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift ◽  
Ice Strength

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean–sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P*. These show that higher values of P* generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P* is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6).

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

Unprecedented decline of sea ice thickness in Fram Strait in 2017-2018

2021 ◽  
Author(s):  
Hiroshi Sumata ◽  
Laura de Steur ◽  
Dmitry Divine ◽  
Olga Pavlova ◽  
Sebastian Gerland
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Barents Sea ◽  
North Atlantic Ocean ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Drift Speed ◽  
The Arctic Ocean

<p><span><span>Fram Strait is the major gateway connecting the Arctic Ocean and the northern North Atlantic Ocean where about 80 to 90% of sea ice outflow from the Arctic Ocean takes place. Long-term observations from the Fram Strait Arctic Outflow Observatory maintained by the Norwegian Polar Institute captured an unprecedented decline<!-- should we somehow add information that this statement is limited to the time since the early 1990s? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:45): "..." I slightly modified the sentence to mention this. --> of sea ice thickness in 2017 – 2018 since comprehensive observations started in the early 1990s. Four Ice Profiling Sonars moored in the East Greenland Current in Fram Strait simultaneously recorded 50 – 70 cm decline of annual mean ice thickness in comparison with preceding years. A backward trajectory analysis revealed that the decline was attributed to an anomalous sea level pressure pattern from 2017 autumn to 2018 summer. Southerly wind associated with a dipole pressure anomaly between Greenland and the Barents Sea prevented southward motion of ice floes north of Fram Strait. Hence ice pack was exposed to warm Atlantic Water in the north of Fram Strait 2 – 3 times longer than the average year, allowing more melt <!-- should also slower freezing or reduced freezing rates mentioned here during winter and spring (in addition to melt in summer and autumn)? --><!-- Reply to Sebastian Gerland (2021/01/12, 15:46): "..." I would like to keep this sentence as it is, since the analysis implies sea ice melt occurred in the vicinity of Fram Strait in winter (probably due to ocean heat flux), though we don’t have direct measurements of 2018 event. This could be an interesting implications of this study, and seeds for further investigation. -->to happen. At the same time, the dipole anomaly was responsible for the slowest observed annual mean ice drift speed in Fram Strait in the last two decades. As a consequence of the record minimum of ice thickness and the slowest drift speed, the sea ice volume transport through the Fram Strait dropped by more than 50% in comparison with the 2010 – 2017 average.</span></span></p>

scholarly journals A Multithickness Sea Ice Model Accounting for Sliding Friction

2006 ◽  
Vol 36 (9) ◽  
pp. 1719-1738 ◽  
Author(s):  
Alexander V. Wilchinsky ◽  
Daniel L. Feltham ◽  
Paul A. Miller
Keyword(s):  
Sea Ice ◽  
Sliding Friction ◽  
Kinematic Model ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Model Code ◽  
Ice Strength ◽  
Discrete Element Simulations ◽  
Ice Model

Abstract A multithickness sea ice model explicitly accounting for the ridging and sliding friction contributions to sea ice stress is developed. Both ridging and sliding contributions depend on the deformation type through functions adopted from the Ukita and Moritz kinematic model of floe interaction. In contrast to most previous work, the ice strength of a uniform ice sheet of constant ice thickness is taken to be proportional to the ice thickness raised to the 3/2 power, as is revealed in discrete element simulations by Hopkins. The new multithickness sea ice model for sea ice stress has been implemented into the Los Alamos “CICE” sea ice model code and is shown to improve agreement between model predictions and observed spatial distribution of sea ice thickness in the Arctic.

scholarly journals Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

2018 ◽  
Vol 12 (11) ◽  
pp. 3671-3691 ◽  
Author(s):  
Jiping Xie ◽  
François Counillon ◽  
Laurent Bertino
Keyword(s):  
Sea Ice ◽  
Degrees Of Freedom ◽  
The Arctic ◽  
Ice Thickness ◽  
Large Set ◽  
Sea Ice Thickness ◽  
The North ◽  
Ice Drift ◽  
Marine Environment Monitoring ◽  
The Impact

Abstract. Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5 cm and the standard errors decrease from 53 to 38 cm (by 28 %) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24 % against the ice mass balance (IMB) buoy 2013F and by 12.5 % against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8 %–9 % in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.

Patterns of sea ice drift and polar bear (Ursus maritimus) movement in Hudson Bay

2020 ◽  
Vol 641 ◽  
pp. 227-240
Author(s):  
NJ Klappstein ◽  
RR Togunov ◽  
JR Reimer ◽  
NJ Lunn ◽  
AE Derocher
Keyword(s):  
Energy Expenditure ◽  
Sea Ice ◽  
Body Condition ◽  
Polar Bear ◽  
Ursus Maritimus ◽  
The Arctic ◽  
Hudson Bay ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Sea ice habitats are highly dynamic, and ice drift may affect the energy expenditure of travelling animals. Several studies in the high Arctic have reported increased ice drift speeds, and consequently, polar bears Ursus maritimus in these areas expended more energy on counter-ice movement for station-keeping. However, little is known about the spatiotemporal dynamics of ice drift in Hudson Bay (HB) and its implications for the declining Western Hudson Bay (WH) polar bear subpopulation. Using sea ice drift data from 1987-2015 and polar bear satellite telemetry location data from 2004-2015, we examined trends in drift speeds in HB, polar bear movement relative to drift, and assessed annual and individual variation. In contrast to other areas of the Arctic, we did not find an increase in ice drift speed over the period examined. However, variability in ice drift speed increased over time, which suggests reduced habitat predictability. Polar bear movement direction was not strongly counter to ice drift in any month, and ice drift speed and direction had little effect on bear movement rates and, thus, energy expenditure. On an annual scale, we found individuals varied in their exposure and response to ice drift, which may contribute to variability in body condition. However, the lack of a long-term increase in ice drift speed suggests this is unlikely to be the main factor affecting the body condition decline observed in the WH subpopulation. Our results contrast findings in other subpopulations and demonstrate the need for subpopulation-specific research and risk evaluation.

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