Thermodynamic and Dynamic Variations in Sea Ice Thickness of the Ross Sea, Antarctica, Driven by Atmospheric Circulation

Previous studies have shown that sea ice extent in the Southern Ocean is influenced by the intensity and location of the Amundsen Sea Low (ASL), through their effect on the meridional winds. However, the inhomogeneous nature of the influence of the ASL on sea ice as well as its influence during critical periods of the sea ice annual cycle is not clear. In this study, we do a spatio-temporal analysis of links between the ASL and the sea ice during the advance and retreat periods of the ice over the period 1979–2013 focusing on the role of the meridional and zonal winds. We use the ERA-Interim monthly-averaged 500 mb geopotential height and 10 m wind data along with monthly Passive Microwave Sea Ice Concentrations (SIC) to examine the seasonal and interannual relationships between the ASL and SIC in the Ross–Amundsen sea ice sector. To characterize the state of the ASL we use indices that describe its location and its intensity. We show that the ASL has preferred locations and intensities during ice advance and retreat seasons. The strength and direction of the influence of the ASL are not spatially homogeneous and can change from advance to retreat season and there are strong significant relationships between the characteristics of the ASL and SIC, within and across seasons and interannually.

The GISS coupled model is used to investigate the sensitivity of sea ice to each of the following parameterizations: (1) two sea ice dynamics (CF: cavitating fluid; VP: viscous‐plastic), (2) the specification of oceanic isopycnal mixing coefficients in the Gent and McWillams isopyncal mixing (GM), and (3) the wajsowicz viscosity diffusion (WV). The large‐scale sea ice properties are highly sensitive to sea ice dynamics. With the inclusion of resistance to shear stress, VP captures the major observed sea ice drift features and improves the simulations of sea ice concentrations, thickness, and export through Fram Strait relative to CF. GM significantly improves the simulation of vertical temperature distributions in the Southern Ocean, although it leads to a dramatic reduction of Antarctic sea ice cover. The reduced oceanic isopycnal mixing coefficients lead to Arctic sea ice that tends to be less and thinner in almost the entire Arctic except in the North Pacific and Labrador Sea, while Antarctic sea ice that extends more equatorward throughout the circumpolar regions. The responses of sea ice to WV show an enlargement and thickening of sea ice in the Arctic, within the ice packs around the Antarctic and a reduction and thinning of sea ice in the northern Weddell and Ross Seas. On the basis of these experiments, two composite experiments with the best parameterizations are investigated. The atmospheric responses associated with sea ice changes are discussed. While improvements are seen, there are still many unrealistic aspects that will require further improvements to sea ice and ocean components.

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.

Over the years, global warming has gained much attention from the global com- munity. The fact that the sea ice plays an important role and has signiflcant efiects towards the global climate has prompted scientists to conduct various researches on the sea ice in the Polar Regions. One of the important parameters being studied is the sea ice thickness as it is a direct key indication towards the climate change. However, to conduct studies on the sea ice scientists are often facing with tough challenges due to the unfavorable harsh weather conditions and the remoteness of the Polar Regions. Thus, microwave remote sensing ofiers an attractive mean for the observation and monitoring of the changes of sea ice in the Polar Regions for the scientists. In this paper, we will be presenting 2 approaches using passive microwave remote sensing to retrieve sea ice thickness. The flrst approach involves the training and testing of the neural network (NN) by using data sets generated from the Radiative Transfer Theory with Dense Medium Phase and Amplitude Correction Theory (RT-DMPACT) forward scattering model. Once training is com- pleted, the inversion for sea ice thickness could be done speedily. The second approach utilizes a genetic algorithm (GA) which would perform a search routine to identify possible solutions in sea ice thickness that would match the corresponding brightness temperatures proflle of the sea ice. The results obtained from both approaches are presented and tested by using Special Scanning Microwave Imager (SSM/I) data with the aid of the sea ice measurements in the Arctic sea. In order to understand the interactions between the wave and sea ice medium, a forward scattering model based on Radiative Transfer Theory was constructed. This forward scattering model was further improved by incorporating Dense Medium Phase and Amplitude Correction Theory (RT- DMPACT) to take into account of the efiect of the closely placed scatterers in the sea ice medium. This forward scattering model formed the basis of our inverse model for the sea ice thickness retrieval process. For the NN approach, multiple pairs of data set consist of difierent sea ice parameters and thicknesses with the corresponding brightness temperatures are flrst generated using the forward scattering model. This data set will be provided to the NN to create a range of sea ice thickness proflle to be used for NN training. The training process is completed when the error generated by NN is acceptably small. After that, inversion is done by providing the brightness temperature proflles of the sea ice to obtain the corresponding sea ice thicknesses. As for GA, a pool of chromosomes representing sea ice thicknesses is created to be fed into the forward scattering model. The chromosomes are then evolved and carried forward to the next generation according to the natural selection concept, whereby the flttest candidate is more likely to survive and to reproduce. The generation and creation continues until the one of the chromosomes has been found to be suitable to be the thickness solution for a given brightness temperature proflle. 2. DATA TRAINING AND SEA ICE THICKNESS INVERSION BY NN The RT-DMPACT Model mentioned above is used to calculate the passive microwave returns in terms of brightness temperatures of vertically (TBv) and horizontally (TBh) polarized wave. The Neural Network (NN) constructed consists of an input layer, two hidden layers and an output layer. Each layer employs several neurons, which are connected to other neurons in the adjacent layer with difierent weights. The signals propagate from input layer, through hidden layers and to the output. The network is trained by the input-output data generated from the RT-DMPACT Model. The training process is carried out by changing the values of the interconnecting weights of the neurons in the layers by using Levenberg-Marquardt Algorithm (Martin H. & Mohammad B. M. 1994), according to the error generated. The weights in the NN are then changed in each iteration to reduce the error to an acceptable margin.

<p>Arctic sea ice is steadily retreating due to climate warming, but regional and seasonal variations in Arctic sea ice are important. This study aims in understanding how the winter atmospheric circulation affects the sea ice drift and how ice motion contributes to regional sea ice concentration and thickness anomalies. Sea ice conditions in late winter and spring are crucial for predicting summer sea ice. Understanding the mechanism that affect the spring sea ice concentration and thickness have potential to improve sea ice predictions year-round.  </p><p>Atmospheric pressure patterns affect the thermodynamic vertical structure of the atmosphere, heat and moisture transport to the Arctic, and radiative and turbulent fluxes at the surface (eg. Nygård et al 2021). Circulation types also control surface wind speed and direction, and are closely linked to ice drift speed (eg. Mallett et al 2021). Winter atmospheric circulation can precondition spring sea ice anomalies and summer melt by ice dynamics (eg. sea ice transport to lower latitudes where it is more vulnerable to melt) and thermodynamics (eg. positive surface energy balance anomalies prohibiting ice growth in winter leading to thinner ice in spring).</p><p>In this study we present a Self Organizing Maps (SOM) clustering of the winter (December-March) mean sea level pressure to detect the typical circulation patterns. The SOM-analysis covers the period from December 2000 to March 2021. The circulation patterns, or SOM nodes, are linked to atmospheric conditions (surface energy balance and wind speed) and sea ice conditions (concentration, drift speed and thickness). We use data from ERA5, ORAS5, and PIOMAS reanalyses, and Polar Pathfinder Sea Ice Motion and Cryosat2-SMOS ice thickness remote sensing products. We show how the circulation types are linked to near surface wind speed and direction, and consequent sea ice drift.  As a result, we analyze if the circulation patterns can be linked to sea ice anomalies thought sea ice dynamics or thermodynamics.</p>

The advection of sea ice and associated freshwater/salt fluxes in the Weddell Sea in 1986 and 1987 are investigated with a large‐scale dynamic‐thermodynamic sea ice model. The model is validated and optimized by comparison of simulated sea ice trajectories with observed drift paths of six buoys deployed on the Weddell Sea ice. The skill of the model is quantified by an error function that measures the deviations of simulated trajectories from observed 30‐day sea ice drift. A large number of sensitivity studies show how simulated sea ice transports and associated freshwater/salt fluxes respond to variations in physical parameterizations. The model reproduces the observed ice drift well, provided ice dynamics parameters are set to appropriate values. Optimized values for the drag coefficients and for the ice strength parameter are determined by applying the error function to various sensitivity studies with different parameters. The optimized model yields a mean northward sea ice volume export out of the southern Weddell Sea of 1693 km3 in 1986 and 2339 km3 in 1987. This shows the important role of sea ice transport for the freshwater budget of the Weddell Sea and gives an indication of its high interannual variability.

The Amundsen Sea Low (ASL) is an area of climatologically low atmospheric pressure situated over the Southern Ocean. The depth and location of this feature have significant effects on winds, temperature, moisture transport, and sea ice in its vicinity. In this article, we quantify the modulating effect of this feature on winds over the Ross Sea and Ross Ice Shelf. We examine composites of surface winds sampled according to extrema in ASL depth, longitude, and latitude. We employ the output of a previously developed synoptic climatology to identify the explanatory synoptic‐scale forcings. In autumn, winter, and spring (AWS) we find that the impact of the depth of the ASL is smaller than that of its location. The ASL moves eastward when it is deep, thereby reducing its influence on Ross Sea winds. When the ASL is northward, we find strongly enhanced southerly flows over the Ross Sea and Ice Shelf, forced by greater cyclonic activity in the north of the Ross Sea. In summer, we find increased cyclonic flow coinciding with a deeper ASL, despite the ASL being located in the Bellingshausen Sea at this time. The responses to the ASL longitude and latitude are profoundly different to those in AWS, suggesting that relationships are strongly dependent on the varying seasonal location of the low. We examine two metrics of the ASL depth and identify that the absolute mean sea level pressure (MSLP) has a more widespread response than that of the relative MSLP.

Arctic sea ice density observation and its impact on sea ice thickness retrieval from CryoSat–2

A model reconstruction of the Antarctic sea ice thickness and volume changes over 1980–2008 using data assimilation

ABSTRACTThis study investigates the impact of wind‐induced sea ice drift on sea ice cover over the Indian Ocean sector of the Southern Ocean (IOS) in the contrasting Southern Annular Mode (SAM) years during a summer (February) and a winter (July) month. Analysis of reanalysis wind shows that during positive SAM events, westerlies show stronger and more zonal flow over the west IOS (west of 45°E), while a stronger northerly component is seen over the east IOS, to the south of 55°S during both February and July. This is attributed to the zonally asymmetric feature of sea level pressure over the IOS. A coupled ocean–sea ice model was forced with dynamical wind forcing for positive and negative SAM events during above months. The zonal contrast as seen in wind and surface current is transferred to the sea ice drift. A stronger zonal eastward sea ice drift is apparent over the west IOS, suggesting increased transport of sea ice from the Weddell Sea region in July. The eastward advection of sea ice results in piling of sea ice over west IOS and causes an increase in sea ice concentration and thickness. Over east IOS, the sea ice drift shows a strong southeastward anomaly from the sea ice edge towards the coast. This results in a piling of sea ice near the coast and a divergence of sea ice near the edge. This results in a negative anomaly in sea ice concentration and sea ice thickness over east IOS. Thus, the dynamical SAM forcing leads to a non‐annular response in sea ice cover over the IOS.

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

One outstanding feature of the recent Arctic climate is the contrast of the changes of sea ice concentration and thickness between the Beaufort Sea and the Chukchi Sea. Since the Arctic Oscillation (AO) plays a critical role in driving Arctic sea ice changes and the Beaufort and Chukchi seas have been hypothesized as a region in which sea ice anomalies originate, we employed a coupled sea ice–ocean model and carried out simulations forced by the AO signal to examine sea ice changes in these regions, focusing on seasonality. With the AO phase transition from negative to positive, anticyclonic windstress weakens broadly in both winter and summer; however, the surface air temperature response shows remarkable seasonal dependence. Positive temperature anomalies spread over the entire domain in winter, while negative anomalies occur in the shelf seas in summer, although positive anomalies remain in the deep-water portion. The simulated sea ice concentration resembles the observed concentration. The strong seasonality of sea ice concentration changes suggests that accumulation of sea ice concentration in the Beau fort Sea and reduction in the Chukchi Sea are mainly produced in sum mer. Changes of ice thickness are robust through the seasonal cycle. Generally, sea ice dynamics play a critical role in creating the anomalous sea ice pattern and sea ice thermodynamics partially compensate the dynamically-driven changes. However, considerable seasonal differences occur.

One outstanding feature of the recent Arctic climate is the contrast of the changes of sea ice concentration and thickness between the Beaufort Sea and the Chukchi Sea. Since the Arctic Oscillation (AO) plays a critical role in driving Arctic sea ice changes and the Beaufort and Chukchi seas have been hypothesized as a region in which sea ice anomalies originate, we employed a coupled sea ice–ocean model and carried out simulations forced by the AO signal to examine sea ice changes in these regions, focusing on seasonality. With the AO phase transition from negative to positive, anticyclonic windstress weakens broadly in both winter and summer; however, the surface air temperature response shows remarkable seasonal dependence. Positive temperature anomalies spread over the entire domain in winter, while negative anomalies occur in the shelf seas in summer, although positive anomalies remain in the deep-water portion. The simulated sea ice concentration resembles the observed concentration. The strong seasonality of sea ice concentration changes suggests that accumulation of sea ice concentration in the Beau fort Sea and reduction in the Chukchi Sea are mainly produced in sum mer. Changes of ice thickness are robust through the seasonal cycle. Generally, sea ice dynamics play a critical role in creating the anomalous sea ice pattern and sea ice thermodynamics partially compensate the dynamically-driven changes. However, considerable seasonal differences occur.

Satellite imagery shows that there was substantial variability in the sea ice extent in the Ross Sea during 2001–2003. Much of this variability is thought to be due to several large icebergs that moved through the area during that period. The effects of these changes in sea ice on circulation and water mass distributions are investigated with a numerical general circulation model. It would be difficult to simulate the highly variable sea ice from 2001 to 2003 with a dynamic sea ice model since much of the variability was due to the floating icebergs. Here, sea ice concentration is specified from satellite observations. To examine the effects of changes in sea ice due to iceberg C‐19, simulations were performed using either climatological ice concentrations or the observed ice for that period. The heat balance around the Ross Sea Polynya (RSP) shows that the dominant term in the surface heat budget is the net exchange with the atmosphere, but advection of oceanic warm water is also important. The area average annual basal melt rate beneath the Ross Ice Shelf is reduced by 12% in the observed sea ice simulation. The observed sea ice simulation also creates more High‐Salinity Shelf Water. Another simulation was performed with observed sea ice and a fixed iceberg representing B‐15A. There is reduced advection of warm surface water during summer from the RSP into McMurdo Sound due to B‐15A, but a much stronger reduction is due to the late opening of the RSP in early 2003 because of C‐19.

The relatively stable conditions of the sea ice cover in the Antarctic, observed for almost 40 years, seem to be changing recently. Therefore, it is essential to provide sea ice thickness (SIT) and volume (SIV) estimates in order to anticipate potential multi-scale changes in the Antarctic sea ice. For that purpose, the main objectives of this work are: (1) to assess a new sea ice freeboard, thickness and volume altimetry dataset over 2003–2020 and (2) to identify first order impacts of the sea ice recent conditions. To produce these series, we use a neuronal network to calibrate Envisat radar freeboards onto CryoSat-2 (CS2). This method addresses the impacts of surface roughness on Low Resolution Mode (LRM) measurements. During the 2011 common flight period, we found a mean deviation between Envisat and CryoSat-2 radar freeboards by about 0.5 cm. Using the Advanced Microwave Scanning Radiometer (AMSR) and the dual-frequency Altimetric Snow Depth (ASD) data, our solutions are compared with the Upward looking sonar (ULS) draft data, some in-situ measurement of the SIMBA campaign, the total freeboards of 6 Operation Ice Bridge (OIB) missions and ICESat-2 total freeboards. Over 2003–2020, the global mean radar freeboard decreased by about −14% per decade and the SIT and SIV by about −10% per decade (considering a snow depth climatology). This is marked by a slight increase through 2015, which is directly followed by a strong decrease in 2016. Thereafter, freeboards generally remained low and even continued to decrease in some regions such as the Weddell sea. Considering the 2013–2020 period, for which the ASD data are available, radar freeboards and SIT decreased by about −40% per decade. The SIV decreased by about −60% per decade. After 2016, the low SIT values contrast with the sea ice extent that has rather increased again, reaching near-average values in winter 2020. The regional analysis underlines that such thinning (from 2016) occurs in all regions except the Amundsen-Bellingshausen sea sector. Meanwhile, we observed a reversal of the main regional trends from 2016, which may be the signature of significant ongoing changes in the Antarctic sea ice.