Assessment of the synoptic variability of the Antarctic marginal ice zone with in Situ observations

Knowledge of sea ice variability, which contributes to the detection of climate change trends, stems primarily from remote sensing information. However, sea ice in the Southern Ocean is characterised by large variability that remains unresolved and limits our confidence on the remotely sensed products. Although one of the biggest seasonal changes on Earth is the annual advance and retreat of the Antarctic sea ice cover, relatively little attention has been given to the processes by which the marginal ice zone (MIZ) edge forms and responds to synoptic events. This study aimed to assess the seasonal sea ice extent (SIE) of the MIZ by comparing sea ice observations estimated from aboard ship to high resolution passive microwave (PM) satellite imagery when transecting the MIZ. To achieve this, sea ice concentration (SIC) was derived from two AMSR (Advanced Microwave Scanning Radiometer ) products; the ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI-AMSR ) and the bootstrap (BST-AMSR ). Theice concentration estimated from these PM satellite products was assessed against SIC observations collected from the S.A. Agulhas II (using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol). This assessment took place over summer and winter for the years 2016 and 2017. After evaluating how well these PM-SIC estimates compared against the ASPeCt SIC observations, we found that there was good correlation over summer MIZ conditions, while over winter MIZ conditions the correlation was relatively poor. This highlighted winter limitations inherent in PM SIC estimates. Therefore, from these comparison results, an analysis of the seasonal SIE was accomplished while being aware of the winter limitations linked to the PM products. We inferred that the MIZ acts as an indicator for what the evolution of winter SIE might look like over the following months. In addition to winter limitations associated with PM-SIC retrievals, the ASPeCt SIC estimates, based on human interpretation of the sea ice conditions, was limited because of subjective bias. This resulted in the development of an algorithm to automatically acquire SIC from image stills and videos. This method can be used to obtain quantitative seaice data from vessels of opportunity without the need to have trained personnel on-board. In summary, this study assesses seasonal MIZ SIE within the Atlantic sector after highlighting the limitations associated with various SIC-retrieval methods

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

We report on results of a systematic intercomparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of - 0.4 % to -1.0 % (Arctic) and -0.3 % to -1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from -0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and -3.7 % (ASI), but similar for the Antarctic, -5.4 % and -5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends

Measurement, Knowledge, and Representation: A Sociological Study of Arctic Sea-Ice Science

Satellite-derived observations of Arctic sea ice are instrumental in contemporary sea-ice research. Through the production and dissemination of data products, these observations shape our understanding of Arctic sea-ice conditions, knowledge of which is essential for informing policy responses, decision-making, and action in the face of unprecedented climate change. However, due to the complex, dynamic, and indeterminate nature of sea ice and various scientific and technological challenges involved in its observation, measurement, and representation, the accuracy to which these products depict Arctic sea ice is limited. Moreover, the methodologies used to acquire, process, and report satellite data vary between scientific institutions, resulting in inconsistent estimates of key sea-ice parameters. Informed by social constructivist arguments developed within science and technology studies and critical cartography, this thesis contends that satellite-derived sea-ice data products represent a particular way of observing, interpreting, and classifying complex geophysical conditions that is socially and culturally contingent. This raises important questions about how sea-ice knowledge is constructed through the interactions between sea ice, sensing technologies, and social practices. Accordingly, this thesis integrates ethnographic and visual methodologies to critically explore how dynamic and indeterminate geophysical data are acquired, processed, and reported in Arctic sea-ice science. By examining sea-ice data products in terms of their underlying practices and technologies, institutional settings, and the broader socio-cultural, political, and historical contexts in which they are embedded, this thesis provides insights into the sociological nature of contemporary sea-ice research. It concludes that greater recognition of the social contingencies shaping how sea-ice data products are generated and disseminated is needed to foster more democratic and socially responsible forms of scientific knowledge. The findings presented in this thesis may provide valuable starting points for critically examining how sea-ice science may be made more equitable and enriched or improved by alternative perspectives

Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR &amp; SSM/I &amp; SSMIS or AMSR-E &amp; AMSR2), in the imaging frequency channels (37&thinsp;GHz and either 6 or 19&thinsp;GHz), in their horizontal resolution (25 or 50&thinsp;km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.</p

Monitoring and Characterization of Arctic Sea Ice using Radar Altimetry

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Launching CryoSat-2, which is a current radar altimeter mission for the monitoring of polar region enables to produce monthly based sea ice thickness since April 2010. The Sea ice thickness cannot be measured directly by satellite. Sea ice freeboard that is an elevation above sea level can be converted in to sea ice thickness by assuming hydrostatic equilibrium. Sea ice leads (e.g., linear cracks in sea ices) are regarded as sea surface tie points for the estimation of sea ice freeboard. Identifying the sea ice leads is one of the core factors to retrieve sea ice thickness. The surface elevation is estimated by the use of Threshold First maxima Retracker Algorithm (TFMRA) for a 40% threshold using CryoSat-2 L1b data and the leads are detected by machine learning approaches such as decision trees and random forest. The machine learning produces better accuracy for the sea ice thickness than previous simple thresholding approach, validating EM-31, airborne sea ice thickness observations. A novel method to overcome previous threshold based lead detection methods for identifying leads is developed, which is waveform mixture algorithm that linear mixture analysis is applied in terms of waveforms. The waveform mixture algorithm can distinguish leads without beam behavior parameters and backscatter sigma-0 but just use waveforms, which is less affected by updating baseline for CryoSat-2. In addition to the development of the algorithms, a scientific research is carried out. Causes for sea ice anomaly phenomenon in November 2016 is investigated. Eventually, sea ice the volume derived by thickness is used for the analysis of sea ice extent minimum in November 2016 and suggest a new insight of sea ice minimum phenomenon. Unlike sea ice extent, the sea ice volume is not a minimum in November 2016. However, since the base period for sea ice volume is short, it is hard to mention climatology of sea ice volume.ope

An Examination of Sea Ice Spring and Summer Retreat in the Canadian Arctic Archipelago: 1989 to 2010

The sea ice extent change and variability of the Canadian Arctic Archipelago (CAA) are quite different compared to the Arctic as a whole due to its unique geographic settings. In this thesis, the sea ice retreat processes, the connection with other Arctic regions, and the linkages to the surface radiation flux in the CAA are examined. The sea ice retreat processes in the CAA follow a four-phase process: a slow ice melt phase that usually lasts until early June (phase 1); a quick melt phase with large daily sea ice extent change which lasts close to half-a-month (phase 2); a slow melt phase that looks like slow sea ice melt or even a small ice increase that lasts another half-a-month (phase 3); and a steady ice decrease phase (phase 4). With the help of Moderate-Resolution Imaging Spectroradiometer (MODIS) data, it is identified that the quick melt in phase 2 is actually melt ponding, with melt ponds being falsely identified as open water by passive microwave. A simplified data assimilation method is then developed to improve the passive microwave sea ice concentration estimation by fusion with MODIS ice surface temperature data. The ice concentration from the analysis is found to improve the original passive microwave sea ice concentration estimation, with the largest improvements during sea ice melt. The sea ice retreat patterns in the CAA region are correlated with the sea ice retreat patterns in other regions of the Arctic. A decision tree classifier is designed to segment the sea ice retreat patterns in the CAA into several classes and classification maps are generated. These maps are effective in identifying the geographic locations that have large changes in the sea ice retreat patterns through the years. The daily progressions of the surface radiation components are described in detail. Due to the lack of multiple reflection, the percentage of shortwave radiation at the top of atmosphere that reaches the surface is influenced by the form of melt ponds over ice surface. The roles that each surface radiation component plays in forcing sea ice retreat are different in different years.1 yea

Insights on past and future sea-ice evolution from combining observations and models

We discuss the current understanding of past and future sea-ice evolution as inferred from combining model simulations and observations. In such combined analysis, the models allow us to enhance our understanding behind the observed evolution of sea ice, while the observations allow us to assess how realistically the models represent the processes that govern sea-ice evolution in the real world. Combined, observations and models thus provide robust insights into the functioning of sea ice in the Earth's climate system, and can inform policy decisions related to the future evolution of the ice cover. We find that models and observations agree well on the sensitivity of Arctic sea ice to global warming and on the main drivers for the observed retreat. In contrast, a robust reduction of the uncertainty range of future sea-ice evolution remains difficult, in particular since the observational record is often too short to robustly examine the impact of internal variability on model biases. Process-based model evaluation and model evaluation based on seasonal-prediction systems provide promising ways to overcome these limitations

The Southern Ocean Observing System (SOOS)

[in “State of the Climate in 2014” : Special Supplement to the Bulletin of the American Meteorological Society Vol. 96, No. 7, July 2015