Spatiotemporal variability in the diffuse attenuation coefficient of sea ice

The diffuse attenuation coefficient (Kd) is known to be closely related to the light transmittance of sea ice, which plays a critical role in the energy balance and biological processes of the upper ocean. However, the commercial instruments cannot easily measure Kd in sea ice because sea ice is a solid. The authors of this study are developing an instrument with a high spectral solution to measure the irradiance profile of sea ice and the irradiance in the atmosphere. Three Kd experiments were carried out, including two in-situ experiments in the Liaodong Bay and one in the laboratory. The results showed that the Kd of the sea ice varied with depth, and the values in adjacent sea ice layers differed by up to 2 times. In addition, due to changes in the climate environment, the Kd of sea ice showed temporal variations. For example, there was a 1.38-fold difference in the Kd values of the surface layer of sea ice at different times in 2022. The values in different sea ice layers also showed different trends over time, and the coefficient of determination (R2) of Kd between adjacent layers over time was as low as 0.008. To explain the driving mechanism of spatio-temporal variability of Kd, an additional experiment focusing on the physical microstructure of sea ice was conducted in Liaodong Bay in 2022. The result shows that the change in air bubbles in the sea ice may be the main the reason for the change in Kd. For example, when the sea ice was exchanging brine and bubbles with the atmosphere above and the seawater below, the highly absorbent particles in it tend to remain in their original position. Considering that the total absorption coefficient changed slightly, the bubbles with the characteristic of intense scattering were found to be the main factor influencing the Kd changes. This conclusion is supported by the fact that the value of R2 between the bubbles and Kd was 0.52. If climatic changes have led to an increase in the volume of bubbles, the more bubbles will increase the scattering properties of sea ice and lead to an increase in Kd. Conversely, the reduced bubble volume would reduce the scattering properties of sea ice, which in turn would reduce Kd.

Physiographic Controls on Landfast Ice Variability from 20 Years of Maximum Extents across the Northwest Canadian Arctic

Landfast ice is a defining feature among Arctic coasts, providing a critical transport route for communities and exerting control over the exposure of Arctic coasts to marine erosion processes. Despite its significance, there remains a paucity of data on the spatial variability of landfast ice and limited understanding of the environmental processes’ controls since the beginning of the 21st century. We present a new high spatiotemporal record (2000–2019) across the Northwest Canadian Arctic, using MODIS Terra satellite imagery to determine maximum landfast ice extent (MLIE) at the start of each melt season. Average MLIE across the Northwest Canadian Arctic declined by 73% in a direct comparison between the first and last year of the study period, but this was highly variable across regional to community scales, ranging from 14% around North Banks Island to 81% in the Amundsen Gulf. The variability was largely a reflection of 5–8-year cycles between landfast ice rich and poor periods with no discernible trend in MLIE. Interannual variability over the 20-year record of MLIE extent was more constrained across open, relatively uniform, and shallower sloping coastlines such as West Banks Island, in contrast with a more varied pattern across the numerous bays, headlands, and straits enclosed within the deep Amundsen Gulf. Static physiographic controls (namely, topography and bathymetry) were found to influence MLIE change across regional sites, but no association was found with dynamic environmental controls (storm duration, mean air temperature, and freezing and thawing degree day occurrence). For example, despite an exponential increase in storm duration from 2014 to 2019 (from 30 h to 140 h or a 350% increase) across the Mackenzie Delta, MLIE extents remained relatively consistent. Mean air temperatures and freezing and thawing degree day occurrences (over 1, 3, and 12-month periods) also reflected progressive northwards warming influences over the last two decades, but none showed a statistically significant relationship with MLIE interannual variability. These results indicate inferences of landfast ice variations commonly taken from wider sea ice trends may misrepresent more complex and variable sensitivity to process controls. The influences of different physiographic coastal settings need to be considered at process level scales to adequately account for community impacts and decision making or coastal erosion exposure.

Application of Radar Image Fusion Method to Near-Field Sea Ice Warning for Autonomous Ships in the Polar Region

Mastering the real-time dynamics of near-field sea ice is the primary condition to guaranteeing the navigation safety of autonomous ships in the polar region. In this study, a radar image fusion process combining marine radar and ice radar is proposed, which can effectively solve the problems of redundant information and spatial registration during image fusion. Then, using the fused radar images, this study proposes a set of near-field sea ice risk assessment and warning processes applicable to both low- and high-sea-ice-concentration situations. The sea ice risk indexes in these two situations are constructed by using four variables: sea ice area, sea ice grayscale, distance between sea ice and the own-ship, and relative bearing of sea ice and the own-ship. Finally, visualization processing is carried out according to the size of the risk index values of each piece of sea ice to achieve a better near-field sea ice risk assessment and warning effect. According to the example demonstration results, through the radar image fusion process and the set of near-field sea ice risk assessment and warning processes proposed in this study, the sea ice risk distribution in the near-field area of the ship can be well obtained, which provides effective support for the assisted decision-making of autonomous navigation in the polar region.

Periodic Oscillation of Sediment Transport Influenced by Winter Synoptic Events, Bohai Strait, China

Instruments on two bottom-mount platforms deployed in the Bohai Strait during a cruise from January 6–13, 2018 recorded an intense northerly wind event. The responses of hydrodynamic and hydrographical characteristics in Bohai Sea and Yellow Sea to the wind event were analyzed aided by the wind, wave, sea surface suspended sediment concentration and sea surface height datasets from open sources. It is shown that the strong wind event had a significant impact on the redistribution of sea surface height, regional wave conditions, regional circulations and the accompanying sediment transport pattern. Specifically, the sediment transport through the Bohai Strait may be divided into two chronological phases related to the wind event: (1) the enhanced sediment transport phase during the buildup and peak of the wind event when both the Northern Shandong Coastal Current and regional suspended sediment concentration were sharply increased; and (2) the relaxation phase when the northerly wind subsided or even reversed, accompanied by the enhanced Yellow Sea Warm Current with lowered suspended sediment concentration. Such results at synoptic scale would improve our capability of quantifying sediment exchange between the Bohai and Yellow sea, through the Bohai Strait and provide valuable reference for the study of other similar environments worldwide.

Variability of sea ice area in the Bohai Sea from 1958 to 2015

With the backdrop of continuous global change, it is beneficial to create consistent long-term records of sea ice area on regional scales for ice disaster prevention and risk mitigation. In this study, a piecewise multiple nonlinear regression model was developed to reconstruct long-term daily sea ice area dataset in the Bohai Sea from 1958 to 2015 by linking the related meteorological data and the satellite-derived ice area. The validation analysis show that related meteorological status corresponding to physical process had stable skill of predictive ability, which was able to account for 81% of the observational variance under consideration of sea ice state, freezing and melting phases. The reconstructed daily sea ice area dataset was further used to study the interannual and seasonal variability of sea ice area. The annual maximum ice area (AMIA) and the annual average ice area (AAIA) in the Bohai Sea exhibited a decreasing trend with fluctuation of -0.33 ± 0.18% yr^-1 and -0.51 ± 0.16% yr^-1 over the period of 1958-2015, respectively. The most obvious change of the Bohai Sea ice area occurred in time scale of ~30 years. The whole study period could be divided into slight increasing stage (1958-1980), significant decreasing stage (1980-1995), and moderate increasing stage (1995-2015). In most years, the annual changes of sea ice area showed an unimodal variation and the freezing period (~65 days) was longer than the melting phase (~40 days) due to the relatively higher freezing rate. In addition, high correlations between AAIA and Arctic Oscillation (AO) index (r = -0.60, p < .01) and North Atlantic Oscillation (NAO) index (r = -0.69, p < .01) from 1958 to 2015 suggested AO and NAO are the primary large-scale climate factors driving the sea ice variability in the Bohai Sea.

Sea Ice Extent Detection in the Bohai Sea Using Sentinel-3 OLCI Data

Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter.

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance ( R ) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R . This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms.

Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta

The Slave River is a northern river in Canada, with ice being an important component of its flow regime for at least half of the year. During the spring breakup period, ice jams and ice-jam flooding can occur in the Slave River Delta, which is of benefit for the replenishment of moisture and sediment required to maintain the ecological integrity of the delta. To better understand the ice jam processes that lead to flooding, as well as the replenishment of the delta, the one-dimensional hydraulic river ice model RIVICE was implemented to simulate and explore ice jam formation in the Slave River Delta. Incoming ice volume, a crucial input parameter for RIVICE, was determined by the novel approach of using MODIS space-born remote sensing imagery. Space-borne and air-borne remote sensing data were used to parameterize the upstream ice volume available for ice jamming. Gauged data was used to complement modeling calibration and validation. HEC-RAS, another one-dimensional hydrodynamic model, was used to determine ice volumes required for equilibrium jams and the upper limit of ice volume that a jam can sustain, as well as being used as a threshold for the volumes estimated by the dynamic ice jam simulations using RIVICE. Parameter sensitivity analysis shows that morphological and hydraulic properties have great impacts on the ice jam length and water depth in the Slave River Delta.