scholarly journals A new sea ice state dependent parameterization for the free drift of sea ice

2021 ◽  
Author(s):  
Charles Brunette ◽  
L. Bruno Tremblay ◽  
Robert Newton
Keyword(s):  
Sea Ice ◽  
Transfer Coefficient ◽  
Root Mean Square ◽  
Surface Wind ◽  
Ocean Current ◽  
Mean Square ◽  
Turning Angle ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. Free drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motion vectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables (concentration and thickness) and ocean current (as a residual). Building on the fact that the spatially varying standard wind-ice transfer coefficient (considering only surface wind stress) has a structure as the spatial distribution of sea ice thickness, we introduce a wind-ice transfer coefficient that scales linearly with thickness. Results show a mean error of −0.5 cm/s (low-speed bias) and a root-mean-square error of 5.1 cm/s, considering daily buoy drift data as truth. This represents a 31 % reduction of the error on drift speed compared to the free drift estimates used in the Polar Pathfinder dataset. The thickness-dependent wind transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum) drift speed in May (October), compared to July (January) for the constant wind transfer coefficient parameterizations which simply follow the peak in mean surface wind stresses. The trend in sea ice drift in this new model is +0.45 cm/s decade−1 compared with +0.39 cm/s decade−1 from the buoy observations, whereas there is essentially no trend in the standard free drift parameterization (−0.01 cm/s decade−1) or the Polar Pathfinder free drifts (−0.03 cm/s decade−1). The wind turning angle that minimize the cost function is equal of 25°, with a mean and root-mean square error of +2.6° and 51° on the direction of the drift, respectively. The residual from the minimization procedure (i.e. the ocean currents) resolves key large scale features such as the Beaufort Gyre and Transpolar Drift Stream, and is in good agreement with ocean state estimates from the ECCO, GLORYS and PIOMAS ice-ocean reanalyses, and geostrophic currents from dynamical ocean topography, with a root-mean-square difference of 2.4, 2.9, 2.6 and 3.8 cm/s, respectively. Finally, a repeat of the analysis on a two sub-section of the time series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion of the gyre in the post-2000 concurrent with a thinning of the sea ice cover and observations acceleration of the ice drift speed and ocean current. This new dataset is publicly available for complementing merged observations-based sea ice drift datasets that includes satellite and buoy drift records.

scholarly journals Enhancement of Sea Ice Drift due to the Dynamical Interaction between Sea Ice and a Coastal Ocean

2012 ◽  
Vol 42 (1) ◽  
pp. 179-192 ◽  
Author(s):  
Yoshihiro Nakayama ◽  
Kay I. Ohshima ◽  
Yasushi Fukamachi
Keyword(s):  
Sea Ice ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Coastal Ocean ◽  
Ocean Current ◽  
Dynamical Interaction ◽  
Ice Drift ◽  
Key Factor ◽  
Sea Ice Drift ◽  
Shelf Waves

Abstract Wind factor, the ratio of sea ice drift speed to surface wind speed, is a key factor for the dynamics of sea ice and is generally about 2%. In some coastal oceans, however, the wind factor tends to be larger near the coast. This study proposes the enhancement mechanism of the sea ice drift caused by the dynamical coupling between sea ice and a coastal ocean. In a coastal ocean covered with sea ice, wind-forced sea ice drift excites coastal trapped waves (shelf waves) and generates fluctuating ocean current. This ocean current can enhance sea ice drift when the current direction is the same as that of the wind-driven drift. The authors consider a simplified setting where spatially uniform oscillating wind drifts sea ice parallel to the coast. When a barotropic long shelf wave is assumed for the ocean response, sea ice drifts driven by wind and ocean are obtained analytically. The ratio of ocean-driven to wind-driven sea ice drifts is used for the evaluation of the oceanic contribution to the enhancement of sea ice drift. The enhancement is mostly determined by the characteristics of the shelf waves, and sea ice drift is significantly enhanced close to the coast with lower-frequency wind forcing. Comparison with the observation off the Sakhalin coast shows that the degree of enhancement of sea ice drift and its characteristic such that larger enhancement occurs near the coast are mostly consistent with our theoretical solution, suggesting that this mechanism is present in the real ocean.

scholarly journals Evaluation of Arctic sea-ice drift and its dependency on near-surface wind and sea-ice concentration and thickness in the coupled regional climate model HIRHAM-NAOSIM

2019 ◽  
Author(s):  
Xiaoyong Yu ◽  
Annette Rinke ◽  
Wolfgang Dorn ◽  
Gunnar Spreen ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. We examine the simulated Arctic sea-ice drift speed for the period 2003–2014 in the coupled Arctic regional climate model HIRHAM-NAOSIM 2.0. In particular, we evaluate the dependency of the drift speed on the near-surface wind speed and sea-ice conditions. Considering the seasonal cycle of Arctic basin averaged drift speed, the model reproduces the summer-autumn drift speed well, but significantly overestimates the winter-spring drift speed, compared to satellite-derived observations. Also, the model does not capture the observed seasonal phase lag between drift and wind speed, but the simulated drift speed is more in phase with near-surface wind. The model calculates a realistic negative relationship between drift speed and ice thickness and between drift speed and ice concentration during summer-autumn when concentration is relatively low, but the correlation is weaker than observed. A daily grid-scale diagnostic indicates that the model reproduces the observed positive relationship between drift and wind speed. The strongest impact of wind changes on drift speed occurs for high and moderate wind speeds, with a low impact for calm conditions. The correlation under low-wind conditions is overestimated in the simulations, compared to observation/reanalysis. A sensitivity experiment demonstrates the significant effects of sea-ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice. However, this does not improve the agreement of the modelled drift speed/wind speed ratio with observations based on reanalysis for wind and remote sensing for sea ice drift. An improvement might be possible, among others, by tuning the open parameters of the parameterization in future.

scholarly journals Evaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIM

2020 ◽  
Vol 14 (5) ◽  
pp. 1727-1746
Author(s):  
Xiaoyong Yu ◽  
Annette Rinke ◽  
Wolfgang Dorn ◽  
Gunnar Spreen ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. We examine the simulated Arctic sea ice drift speed for the period 2003–2014 in the coupled Arctic regional climate model HIRHAM–NAOSIM 2.0. In particular, we evaluate the dependency of the drift speed on the near-surface wind speed and sea ice conditions. Considering the seasonal cycle of the Arctic basin averaged drift speed, the model reproduces the summer–autumn drift speed well but significantly overestimates the winter–spring drift speed, compared to satellite-derived observations. Also, the model does not capture the observed seasonal phase lag between drift and wind speed, but the simulated drift speed is more in phase with the near-surface wind. The model calculates a realistic negative correlation between drift speed and ice thickness and between drift speed and ice concentration during summer–autumn when the ice concentration is relatively low, but the correlation is weaker than observed. A daily grid-scale diagnostic indicates that the model reproduces the observed positive correlation between drift and wind speed. The strongest impact of wind changes on drift speed occurs for high and moderate wind speeds, with a low impact for rather calm conditions. The correlation under low-wind conditions is overestimated in the simulations compared to observation/reanalysis data. A sensitivity experiment demonstrates the significant effects of sea ice form drag from floe edges included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice. However, this does not improve the agreement of the modeled drift speed / wind speed ratio with observations based on reanalysis data for wind and remote sensing data for sea ice drift. An improvement might be achieved by tuning parameters that are not well established by observations.

scholarly journals Sea-ice drift characteristics revealed by measurement of acoustic Doppler current profiler and ice-profiling sonar off Hokkaido in the Sea of Okhotsk

2011 ◽  
Vol 52 (57) ◽  
pp. 1-8 ◽  
Author(s):  
Yasushi Fukamachi ◽  
Kay I. Ohshima ◽  
Yuji Mukai ◽  
Genta Mizuta ◽  
Masaaki Wakatsuchi
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Acoustic Doppler Current Profiler ◽  
Ice Thickness ◽  
Coastal Station ◽  
Turning Angle ◽  
The Sea Of Okhotsk ◽  
Ice Drift ◽  
Current Profiler ◽  
Sea Ice Drift

AbstractIn the southwestern part of the Sea of Okhotsk off Hokkaido, sea-ice drift characteristics are investigated using the ice and water velocities obtained from a moored upward-looking acoustic Doppler current profiler (ADCP) during the winters of 1999–2001. Using hourly-mean values of these data along with the wind data measured at a nearby coastal station, the wind factor and turning angle of the relative velocity between the ice and water velocities with respect to the wind are calculated assuming free drift under various conditions. Since the simultaneous sea-ice draft data are also available from a moored ice-profiling sonar (IPS), we examine the dependence of drift characteristics on ice thickness for the first time. As ice thickness increases and wind decreases, the wind factor decreases and the turning angle increases, as predicted by the theory of free drift. This study clearly shows the utility of the moored ADCP measurement for studying sea-ice drift, especially with the simultaneous IPS measurement for ice thickness, which cannot be obtained by other methods.

Pan-arctic winter drift speeds and changing patterns of sea ice motion: 1979–2015

Polar Record ◽  
2018 ◽  
Vol 54 (5-6) ◽  
pp. 303-311 ◽  
Author(s):  
Satwant Kaur ◽  
Jens K. Ehn ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Large Variability ◽  
Drift Speed ◽  
Motion System ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
The Mean

AbstractMonthly mean passive microwave-derived sea-ice motion maps for 36 winters (October–April) are used to examine pan-Arctic sea-ice drift speeds and patterns. The mean Arctic Ocean sea-ice motion consists of three well-known primary circulation regimes: the Beaufort Gyre (BG), transpolar drift (TPD), and a motion system from the Kara Sea (KS). The 36-year mean winter sea-ice drift pattern is used to identify the average boundaries between the circulation regimes mentioned above. Regression analyses of the ice drift speed anomalies show statistically significant positive drift speed trends in BG, TPD and KS. Non-significant trends are associated with negative trends of generally weak drift speeds north of the Canadian Arctic and over the Chukchi/East Siberian Shelf. The first three modes of Empirical Orthogonal Functions were found to explain 30.2%, 13.5% and 8.7% of the spatial variance in the mean winter ice drift patterns and highlight the large variability in the ice drift patterns.

scholarly journals Sea Ice and Current Response to the Wind: A Vector Regressional Analysis Approach

2007 ◽  
Vol 24 (6) ◽  
pp. 1086-1101 ◽  
Author(s):  
Alexander B. Rabinovich ◽  
Georgy V. Shevchenko ◽  
Richard E. Thomson
Keyword(s):  
Sea Ice ◽  
Drift Velocity ◽  
Surface Wind ◽  
Ocean Current ◽  
Parameter Method ◽  
Current Response ◽  
Wind Vector ◽  
Ice Drift ◽  
Velocity Drift ◽  
Factor Α

The authors describe a two-dimensional (vector) regressional model for examining the anisotropic response of ice drift and ocean current velocity (“drift velocity”) to surface wind forcing. Illustration of the method is limited to sea ice response. The principal mathematical and physical properties of the model are outlined, together with estimates of the “response matrices” and the corresponding “response ellipses” (drift velocity response to a unity wind velocity forcing). For each direction, φ, of the wind vector the method describes a corresponding “wind factor” α(φ) (relative drift speed) and “turning angle” θ(φ) (the angle between the drift velocity and wind vector). The major ellipse axis corresponds to the direction of the “effective wind” (φ = φmax) and the minor axis to the direction of the “noneffective” wind. The eigenvectors of the response matrix define wind directions that are the same as the wind-induced drift velocity directions. Depending on the water depth and offshore distance, six analytical cases are possible, ranging from rectilinear response ellipses near the coast to purely circular response ellipses in the open ocean. The model is used to examine ice drift along the western shelf of Sakhalin Island (Sea of Okhotsk). Responses derived from the vector regression (four parameter) method are less constrained and therefore more representative of wind-induced surface motions than those derived using the traditional complex transfer function (two parameter) approach.

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.

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).

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