Physics of arctic landfast sea ice and implications on the cryosphere : An overview

Landfast sea ice (LFSI) is a critical component of the Arctic sea ice cover, and is changing as a result of Arctic amplification of climate change. Located in coastal areas, LFSI is of great significance to the physical and ecological systems of the Arctic shelf and in local indigenous communities. We present an overview of the physics of Arctic LFSI and the associated implications on the cryosphere. LFSI is kept in place by four fasten mechanisms. The evolution of LFSI is mostly determined by thermodynamic processes, and can therefore be used as an indicator of local climate change. We also present the dynamic processes that are active prior to the formation of LFSI, and those that are involved in LFSI freeze-up and breakup. Season length, thickness and extent of Arctic LFSI are decreasing and showing different trends in different seas, and therefore, causing environmental and climatic impacts. An improved coordination of Arctic LFSI observation is needed with a unified and systematic observation network supported by cooperation between scientists and indigenous communities, as well as a better application of remote sensing data to acquire detailed LFSI cryosphere physical parameters, hence revolving both its annual cycle and long-term changes. Integrated investigations combining in situ measurements, satellite remote sensing and numerical modeling are needed to improve our understanding of the physical mechanisms of LFSI seasonal changes and their impacts on the environment and climate.Peer reviewe

Meteorological and sea ice anomalies in the western Arctic Ocean during the 2018–2019 ice season: a Lagrangian study

Rapid changes in the Arctic climate and those in Arctic sea ice in recent decades are closely coupled. In this study, we used atmospheric reanalysis data and satellite remote sensing products to identify anomalies of meteorological and sea ice conditions during the ice season of 2018–2019 relative to climatological means using a Lagrangian methodology. We obtained the anomalies along the drifting trajectories of eight sea ice mass balance buoys between the marginal ice zone and the pack ice zone in the western Arctic Ocean (~160°W–170°W and 79°N–85°N) from September 2018 to August 2019. The temporary collapse of the Beaufort High and a strong positive Arctic Dipole in the winter of 2018–2019 drove the three buoys in the north to drift gradually northeastward and merge into the Transpolar Drift Stream. The most prominent positive temperature anomalies in 2018–2019 along the buoy trajectories relative to 1979–2019 climatology occurred in autumn, early winter, and April, and were concentrated in the southern part of the study area; these anomalies can be partly related to the seasonal and spatial patterns of heat release from the Arctic ice-ocean system to the atmosphere. In the southern part of the study area and in autumn, the sea ice concentration in 2018–2019 was higher than that averaged over the past 10 years. However, we found no ice concentration anomalies for other regions or seasons. The sea ice thickness in the freezing season and the snow depth by the end of the winter of 2018–2019 can also be considered as normal. Although the wind speed in 2018–2019 was slightly lower than that in 1979–2019, the speed of sea ice drift and its ratio to wind speed were significantly higher than the climatology. In 2019, the sea ice surface began to melt at the end of June, which was close to the 1988–2019 climatology. However, spatial variations in the onsets of surface melt in 2019 differed from the climatology, and can be explained by the prevalence of a high-pressure system in the south of the Beaufort Sea in June 2019. In addition to seasonal variations, the meteorological and sea ice anomalies were influenced by spatial variations. By the end of summer 2019, the buoys had drifted to the west of the Canadian Arctic Archipelago, where the ice conditions was heavier than those at the buoy locations in early September 2018. The meteorological and sea ice anomalies identified in this study lay the foundations for subsequent analyses and simulations of sea ice mass balance based on the buoy data

Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks is trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model's performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.Comment: Accepted in IJC

Physics of Arctic landfast sea ice and implications on the cryosphere: an overview

Landfast sea ice (LFSI) is a critical component of the Arctic sea ice cover, and is changing as a result of Arctic amplification of climate change. Located in coastal areas, LFSI is of great significance to the physical and ecological systems of the Arctic shelf and in local indigenous communities. We present an overview of the physics of Arctic LFSI and the associated implications on the cryosphere. LFSI is kept in place by four fasten mechanisms. The evolution of LFSI is mostly determined by thermodynamic processes, and can therefore be used as an indicator of local climate change. We also present the dynamic processes that are active prior to the formation of LFSI, and those that are involved in LFSI freeze-up and breakup. Season length, thickness and extent of Arctic LFSI are decreasing and showing different trends in different seas, and therefore, causing environmental and climatic impacts. An improved coordination of Arctic LFSI observation is needed with a unified and systematic observation network supported by cooperation between scientists and indigenous communities, as well as a better application of remote sensing data to acquire detailed LFSI cryosphere physical parameters, hence revolving both its annual cycle and long-term changes. Integrated investigations combining in situ measurements, satellite remote sensing and numerical modeling are needed to improve our understanding of the physical mechanisms of LFSI seasonal changes and their impacts on the environment and climate

The seasonal cycle and break-up of landfast sea ice along the northwest coast of Kotelny Island, East Siberian Sea

Arctic landfast sea ice (LFSI) represents an important quasi-stationary coastal zone. Its evolution is determined by the regional climate and bathymetry. This study investigated the seasonal cycle and interannual variations of LFSI along the northwest coast of Kotelny Island. Initial freezing, rapid ice formation, stable and decay stages were identified in the seasonal cycle based on application of the visual inspection approach (VIA) to MODIS/Envisat imagery and results from a thermodynamic snow/ice model. The modeled annual maximum ice thickness in 1995-2014 was 2.02 +/- 0.12 m showing a trend of -0.13 m decade(-1). Shortened ice season length (-22 d decade(-1)) from model results associated with substantial spring (2.3 degrees C decade(-1)) and fall (1.9 degrees C decade(-1)) warming. LFSI break-up resulted from combined fracturing and melting, and the local spatiotemporal patterns of break-up were associated with the irregular bathymetry. Melting dominated the LFSI break-up in the nearshore sheltered area, and the ice thickness decreased to an average of 0.50 m before the LFSI disappeared. For the LFSI adjacent to drift ice, fracturing was the dominant process and the average ice thickness was 1.56 m at the occurrence of the fracturing. The LFSI stages detected by VIA were supported by the model results.Peer reviewe

Model simulations of the annual cycle of the landfast ice thickness in the East Siberian Sea

The annual cycle of the thickness and temperature of landfast sea ice in the East Siberian Sea has been examined using a one-dimensional thermodynamic model. The model was calibrated for the year August 2012–July 2013, forced using the data of the Russian weather station Kotel’ny Island and ECMWF reanalyses. Thermal growth and decay of ice were reproduced well, and the maximum annual ice thickness and breakup day became 1.64 m and the end of July. Oceanic heat flux was 2 W.m–2 in winter and raised to 25 W.m–2 in summer, albedo was 0.3–0.8 depending on the surface type (snow/ice and wet/dry). The model outcome showed sensitivity to the albedo, air temperature and oceanic heat flux. The modelled snow cover was less than 10 cm having a small influence on the ice thickness. In situ sea ice thickness in the East Siberian Sea is rarely available in publications. This study provides a method for quantitative ice thickness estimation by modelling. The result can be used as a proxy to understand the sea ice conditions on the Eurasian Arctic coast, which is important for shipping and high-resolution Arctic climate modelling

Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control

Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements