A.N. Vasulkar
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Arctic sea ice leads to a significant dissipation of tidal energy, necessitating its inclusion in global tidal models. However, most global tidal models either neglect or only partially incorporate the impact of sea ice on tides. This study proposes a method to model the dissipative forces exerted by sea ice on tides without directly coupling to a sea ice model, yet utilizing sea ice parameters such as thickness and concentration. Our approach involves (re)-categorizing the sea ice cover into regions dominated either by the velocity difference between sea ice and tides (Vertical Shear (VS)) or by the shear from drifting sea ice on tides (Horizontal Shear (HS)), which primarily govern the energy dissipation between tides and sea ice. The subdivision and resulting areas of these HS and VS regions are based on a nondimensional number referred to as the Friction number, which is the ratio of the internal stress of the sea ice field to the ice–water frictional stress, and directly depends on the thickness and concentration of the sea ice. The new parameterization is validated through a performance assessment comparing it to a commonly used approach of assuming all the sea ice to be stationary (landfast). The seasonal modulation of the M2 tidal component, quantified as the March–September difference, serves as the performance metric, demonstrating that the new parameterization has better agreement with observations from altimeter- and tide gauge-derived seasonal modulation. The results indicate that the physics of ice–tide interaction is better represented with the new parameterization of sea ice-induced dissipation, making it suitable for investigating the effects of declining sea ice thickness on tides.
The first research objective of this thesis was to develop a physically consistent parameterization for modeling sea ice-induced dissipation in barotropic global tide models. Chapter 2 explores the physics of air-ice-ocean interactions and the challenges of acquiring accurate sea ice drift velocities on tidal time scales. A parametric approach is introduced, which incorporates dissipation from three distinct sea ice regimes: landfast ice, drifting sea ice with internal stresses, and free-drift sea ice. The findings indicate that landfast sea ice can significantly influence the seasonal modulation of the M2 tide, particularly in regions like Hudson Bay, where it can reach up to 0.25 meters, underlining the need for further research on the long-term effects of Arctic sea ice decline.
Chapter 3 delves into the dissipation of tides caused by free drift sea ice. While this dissipation is negligible in deep, open ocean areas without wind influence, its impact in shallow water regions, such as the Spitzbergen Shelf, remains uncertain. An analysis using a beacon dataset and a physics-based ice model in the Barents Sea suggests that under low wind conditions, the dissipation from free drift sea ice is minimal, contributing only 2-3% of the bottom frictional stress.
Chapter 4 presents a new parametric approach for modeling tidal energy dissipation due to sea ice, dividing the sea ice cover into regions dominated by either Vertical Shear (VS) or Horizontal Shear (HS) energy dissipation. This approach is based on a non-dimensional Friction Number (F) that depends on sea ice thickness and concentration. The new parameterization demonstrates a stronger correlation (0.6) with altimetry data compared to current state-of-the-art methods (0.4). Additionally, it shows lower differences when compared to tide gauge observations, making it more suitable for studying the impact of sea ice decline on tides.
A significant challenge in this research was the lack of accurate, time-specific observations of tidal currents or water levels in the Arctic. To address this, the second research objective focused on developing a method for deriving tidal current constituents from GNSS buoy data, resulting in the novel ‘Model-derived fitting method’ detailed in Chapter 5. This method is evaluated through case studies, demonstrating its accuracy and robustness, especially in regions with dominant barotropic or baroclinic tidal currents.
This thesis successfully addresses key questions regarding the impact of sea ice on tides, introducing innovative parameterizations and exploring new data sources for tidal current estimation, thus advancing the understanding of Arctic tidal dynamics and ice-water interactions. ...
The first research objective of this thesis was to develop a physically consistent parameterization for modeling sea ice-induced dissipation in barotropic global tide models. Chapter 2 explores the physics of air-ice-ocean interactions and the challenges of acquiring accurate sea ice drift velocities on tidal time scales. A parametric approach is introduced, which incorporates dissipation from three distinct sea ice regimes: landfast ice, drifting sea ice with internal stresses, and free-drift sea ice. The findings indicate that landfast sea ice can significantly influence the seasonal modulation of the M2 tide, particularly in regions like Hudson Bay, where it can reach up to 0.25 meters, underlining the need for further research on the long-term effects of Arctic sea ice decline.
Chapter 3 delves into the dissipation of tides caused by free drift sea ice. While this dissipation is negligible in deep, open ocean areas without wind influence, its impact in shallow water regions, such as the Spitzbergen Shelf, remains uncertain. An analysis using a beacon dataset and a physics-based ice model in the Barents Sea suggests that under low wind conditions, the dissipation from free drift sea ice is minimal, contributing only 2-3% of the bottom frictional stress.
Chapter 4 presents a new parametric approach for modeling tidal energy dissipation due to sea ice, dividing the sea ice cover into regions dominated by either Vertical Shear (VS) or Horizontal Shear (HS) energy dissipation. This approach is based on a non-dimensional Friction Number (F) that depends on sea ice thickness and concentration. The new parameterization demonstrates a stronger correlation (0.6) with altimetry data compared to current state-of-the-art methods (0.4). Additionally, it shows lower differences when compared to tide gauge observations, making it more suitable for studying the impact of sea ice decline on tides.
A significant challenge in this research was the lack of accurate, time-specific observations of tidal currents or water levels in the Arctic. To address this, the second research objective focused on developing a method for deriving tidal current constituents from GNSS buoy data, resulting in the novel ‘Model-derived fitting method’ detailed in Chapter 5. This method is evaluated through case studies, demonstrating its accuracy and robustness, especially in regions with dominant barotropic or baroclinic tidal currents.
This thesis successfully addresses key questions regarding the impact of sea ice on tides, introducing innovative parameterizations and exploring new data sources for tidal current estimation, thus advancing the understanding of Arctic tidal dynamics and ice-water interactions.
One of the major challenges facing global hydrodynamic tidal models is the modelling of the interaction between sea ice and tides in high-latitude waters. Recent studies have shown strong seasonal correlation between sea ice and tides. Hence, it is important to accurately model the effect of sea ice in a tidal model. Presence of sea ice leads to a frictional dissipation of tides. Most models either completely ignore sea ice or partly include it by assuming a fixed sea ice cover (landfast ice). However, sea ice can also be drifting and the nature of dissipation between drifting sea ice and tides is partly unknown. We assess the dissipation of tides due to free drift sea ice. In the absence of wind, this is negligible in the deeper and open ocean. For the shallow water regions, however, this dissipation is unknown. Here, we evaluate this dissipation for the Spitzbergen Shelf region using a beacon dataset showing strong free drift subdaily sea ice oscillations and a physics based point ice model. Two analyses were done which compared the model and observed motion. The analyses showed that for winds speeds below 8m/s and with low subdaily signals, the subdaily free drift sea ice motion is strongly connected to the tides and that the frictional dissipation is low. In the context of global tide and storm surge models, the dissipation from free drift sea ice on tides should be evaluated based on the region (deep ocean or shallow water) and existing wind conditions. In the presence of strong winds the dissipation between free drift sea ice and air can be significant on a subdaily scale even if there are no subdaily signals in the wind itself.
Seasonal modulation of the M2 tide has been quantified for the entire Arctic Ocean and connected regional seas, using tidal harmonic analysis of water levels derived from Synthetic Aperture Radar altimetry. Results are compared to numerical simulations that model the effect of two limiting cases of seasonal landfast ice cover on the M2 tide. The largest seasonal modulation (up to 0.25 m) is observed along coastlines and in bays. Locally, the presence of landfast ice decreases amplitudes, but in some cases, the opposite effect was observed further afield. In most of the Arctic, winter months experience a later arrival of the tide, except for Hudson Bay where phase advance is observed. Most of the altimeter-derived seasonal modulation could be explained by the modeled impact of landfast ice. However, particularly in the Hudson Bay system there is a discrepancy between model- and altimeter-derived seasonal modulation. This suggests that other seasonal processes are important. Finally, results suggest that the consequences of variations in Arctic landfast ice are not restricted to the Arctic, but affect tidal water levels on a global scale.