Ice Charts Research Articles

U-net with ResNet-34 backbone for dual-polarized C-band baltic sea-ice SAR segmentation

Abstract In this study, the U-net with ResNet-34, i.e. a residual neural network with 34 layers, backbone semantic segmentation network is applied to C-band sea-ice SAR imagery over the Baltic Sea. Sentinel-1 Extra Wide Swath mode HH/HV-polarized SAR data acquired during the winter season 2018–2019, and corresponding segments derived from the daily Baltic Sea ice charts were used for training the segmentation algorithm. C-band SAR image mosaics of the winter season 2020–2021 were then used to evaluate the segmentation. The major objective was to study the suitability of semantic segmentation of SAR imagery for automated SAR segmentation and also to find a potential replacement for the outdated iterated conditional modes (ICM) algorithm currently in operational use. The results compared to the daily Baltic Sea ice charts and the operational ICM segmentation and visual interpretation were encouraging from the operational point of view. Open water areas were located very well and the oversegmentation produced by ICM was significantly reduced. The correspondence between the ice chart polygons and the segmentation results was also reasonably good. Based on the results, the studied method is a potential candidate to replace the operational ICM SAR segmentation used in the Copernicus Marine Service automated sea-ice products at Finnish Meteorological Institute.

Reconstructing and Hindcasting Sea Ice Conditions in Hudson Bay Using a Thermal Variability Framework

The Hudson Bay seasonal sea ice record has been well known since the advent of satellite reconnaissance, with a continuous record since 1971. To extend the record to earlier decades, a thermal variability framework is used with the surface temperature climatological records from four climate stations along the Hudson Bay shoreline: Churchill, Manitoba; Kuujjurapik, Quebec; Inukjuak, Quebec; and Coral Harbour, Nunavut. The day-to-day surface temperature variation for the minimum temperature of the day was found to be well correlated to the known seasonal sea ice distribution in the Bay. The sea ice/thermal variability relationship was able to reproduce the existing sea ice record (the average breakup and freeze-up dates for the Bay) largely within the error limits of the sea ice data (1 week), as well as filling in some gaps in the existing sea ice record. The breakup dates, freeze-up dates, and ice-free season lengths were generated for the period of 1922 to 1970, with varying degrees of confidence, adding close to 50 years to the sea ice record. Key periods in the spring and fall were found to be critical, signaling the time when the changes in the sea conditions are first notable in the temperature variability record, often well in advance of the 5/10th ice coverage used for the sea ice record derived from ice charts. These key periods in advance of the breakup and freeze-up could be potentially used, in season, as a predictor for navigation. The results are suggestive of a fundamental change in the nature of the breakup (faster) and freeze-up (longer) in recent years.

Baltic sea ice thickness estimation based on X-band SAR data and background information

Abstract In this study an operational sea ice thickness (SIT) estimation algorithm, based on HH-polarized X-band synthetic aperture radar (SAR) data, background information from the most recent, typically from the previous day, available daily Baltic Sea ice chart and the operational Finnish Meteorological Institute (FMI) thermodynamic ice model, was developed and evaluated. The algorithm was designed to complement the C-band SAR SIT algorithm developed earlier at FMI and applied daily as part of the operational Copernicus Marine Service (CMS). The X-band SIT algorithm was developed by utilizing the sea ice thickness measurements made onboard the Finnish and Swedish ice breakers during two winters seasons: 2021–2022 and 2022–2023. The former season measurements were used for defining the algorithm parameters and the later season for evaluation of the algorithm performance. According to the evaluation metrics the X-band algorithm performance is slightly better than that of the operational CMS C-band SAR SIT algorithm, indicating its suitability for operational use in CMS.

Analysis of fast ice anomalies and their causes in 2023 in Prydz Bay, East Antarctica

In 2023, Antarctica experienced its lowest sea ice extent in the satellite era, with extreme polar events gaining widespread attention. Prydz Bay, where the Chinese Zhongshan Station is located, is the third largest embayment in Antarctica. Changes in sea ice, fast ice and polynyas directly affect local heat and mass exchanges between the ocean and atmosphere, as well as ecosystems and research activities. In 2023, substantial fast ice anomalies were observed in Prydz Bay: the extent of fast ice off Zhongshan Station (ZSFI) was anomalously low, while that within Barrier Bay (BaFI) was anomalously high. This study analysed the seasonal evolution and underlying main causes for the extreme conditions using ice charts, satellites and reanalysis data. From 2014 to 2022, the extent of ZSFI typically increased during the cold season, reaching a maximum of (9.41 ± 2.47) × 103 km2, whilst the Barrier Bay Polynya (BaP) persisted throughout this period. However, in 2023, ZSFI did not increase from June onwards, peaking at a maximum extent of only 5.49 × 103 km2, and the BaP closed in mid-winter, leading to the formation of extensive BaFI. Air temperature and wind speed continuously dropped in July, and these conditions persisted for approximately 1 month, leading to the closure of BaP. However, ZSFI did not expand further under these extreme meteorological conditions, indicating its independence from these factors. The limited expansion of ZSFI could be attributed to high ocean temperatures. Overall, this study provides valuable insights into the mechanisms driving extreme fast ice conditions.

The AutoICE Challenge

Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.

Information on operational sea ice products and current and future activities of the German ice service

The ice service at the German Federal Maritime and Hydrographic Agency and its predecessors has been committed to the safety and easiness of ship navigation for more than 100 years. Within this paper, an overview of the operational products issued by the German ice service on a daily to weekly basis throughout the northern hemisphere winter season is given. These comprise written reports, ice charts, NAVTEX messages, and messages via the Global Telecommunication System to inform about the sea ice situation in German coastal waters, the Baltic Sea, and worldwide. Furthermore, the ice service has systematically collected ice observation data along the German North Sea and Baltic Sea coast since the winter of 1896/1897. The history of the German ice service is presented to put the sea ice data into context of the observation technologies used in the course of the existence of the ice service. These long-term observations enable climatological analyses of the sea ice cover in German coastal waters necessary for the safe operation of offshore infrastructure. An evaluation of the data shows a recent decline in sea ice occurrence in the Baltic Sea and German Bight. Current work is ongoing to preserve more of the historic data in digital form and also to transform the products to conform to modern standards in digital technologies and interactive solutions for the customers.

Spatial and Temporal Evolution of Seasonal Sea Ice Extent of Hudson Strait, Canada, 1971–2018

The temporal and spatial variation in seasonal sea ice in Hudson Strait is examined using time series and spatial clustering analyses. For the period from 1971 to 2018, a time series of sea ice breakup and freeze-up dates and ice-free season length at twenty-four grid points were generated from sea ice charts derived from satellite and other data. These data were analyzed temporally and spatially. The temporal analyses indicated an unambiguous response to a warming climate with statistically significant earlier breakup dates, later freeze-up dates, and longer ice-free seasons, that were statistically linked to coincident regional surface air temperatures. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less so in the 2010s. There was evidence that breakup date behaviour was not only coincident with regional temperatures but likely with temperature and ice conditions of the previous year. Later freeze-up dates were directly linked to earlier breakup dates using detrended time series. Spatial clustering analysis on the Hudson Strait gridded sea ice data revealed distinctive signatures for Ungava Bay, Frobisher Bay, and for grid points close to the shore and a clear linkage to the underlying circulation of Hudson Strait.

Automated Sentinel-1 sea ice type mapping and in-situ validation during the CIRFA-22 cruise

Abstract We present a fully-automated workflow to map sea ice types from Sentinel-1 data and transfer the results in near real-time to the research vessel Kronprins Haakon (KPH) in order to support tactical navigation and decision-making during a research cruise conducted towards Belgica Bank in April and May 2022. We used overlapping SAR and optical imagery to train a pixel-wise classifier for the required season and region, and implemented a processing chain with the Norwegian Ice Service at MET Norway that automatically classifies all Sentinel-1 images covering the area of interest. During the cruise, classification results were available on KPH within hours after image acquisition, which is significantly faster than manually produced ice charts. We evaluate the results both quantitatively, based on manually selected validation regions, and qualitatively in comparison to in-situ observations and photographs. Our findings show that open water, level ice, and deformed ice are classified with high accuracy, while young ice remains challenging due to its variable small-scale surface roughness. This work presents one of the first attempts to transfer automated ice type classification results into the field in near real-time and contributes to bridging the gap between research and operations in automated sea ice mapping.

Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022

Abstract. The Canadian Arctic Archipelago (CAA) serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. We use observations from Sentinel-1, RADARSAT-2, the RADARSAT Constellation Mission (RCM), and CryoSat-2, together with the Canadian Ice Service ice charts, to quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage shipping route. Results indicate that the CAA primarily exports ice to the Arctic Ocean and Baffin Bay, with an average annual (October to September) ice area flux of 137 ± 72 × 103 km2 and a volume flux of 58 ± 68 km3. The CAA contributes a larger area but smaller volume of ice downstream to the North Atlantic than what is delivered via Nares Strait. The average annual ice area flux from the Queen Elizabeth Islands to Parry Channel was 27 ± 10 × 103 km2 and the volume flux was 34 ± 12 km3, with a majority occurring through Byam Martin Channel, which is directly above the central region of Northwest Passage. Over our study period, annual multi-year ice (MYI) replenishment within the CAA was resilient, with an average of 14 ± 38 × 103 km2 imported from the Arctic Ocean and an average of 56 ± 36 × 103 km2 of first-year ice (FYI) retained following the melt season. The considerable ice flux to Parry Channel, together with sustained MYI replenishment, emphasizes the continued risk that sea ice poses to practical utilization of key shipping routes in the CAA, including the Northwest Passage.

Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (TB) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath TB datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of TB signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks.

Multisensor data fusion of operational sea ice observations

Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.

СЕЗОННЫЕ И МЕЖГОДОВЫЕ ИЗМЕНЕНИЯ ВОЗРАСТНОЙ СТРУКТУРЫ ЛЕДЯНОГО ПОКРОВА ГРЕНЛАНДСКОГО МОРЯ В ЗИМНИЙ ПЕРИОД

Seasonal and interannual changes in the Greenland Sea ice age composition during winter months for seven standard age categories are analyzed. To estimate the amount of the ice of different age categories, the digital archive of the region’s ice charts for the period 1997-2022 produced by the Arctic and Antarctic Research Institute is used. Statistical insignificance of linear trends has been revealed for the main age categories of the Greenland Sea ice. A comparison of the obtained estimates of the variability in the amount of the ice of different age categories and earlier studies shows that the quantitative changes in the Greenland Sea ice age composition started before 1997. The data on the ice age composition over a 25-year observation period alone are insufficient to draw an unambiguous conclusion about a decrease in the Greenland Sea ice thickness.

Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts

Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts

Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Abstract. Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM version 2.2.98) and the Los Alamos multi-category sea ice model (CICE version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (observation minus model) is designed to be proportionally distributed to the multiple sea ice categories. A hindcast experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022, in which the extra computational cost for the LAON assimilation is about 5 % of the free run without assimilation. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness, and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4, and neXtSIM, which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. LAON also produces a continuous and smooth evolution of sub-daily SIC, which avoids abrupt jumps often seen in other assimilated products. This efficient and accurate method is promising for data assimilation in global and high-resolution models.

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

ABSTRACT Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification.

A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020

Abstract. This study presents a long-term winter sea ice thickness proxy product for the Canadian Arctic based on a random forest regression model – applied to ice charts and scatterometer data, trained on CryoSat-2 observations, and applying an ice type–sea ice thickness correction using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) – that provides 25 years of sea ice thickness in the Beaufort Sea, Baffin Bay, and, for the first time, the Canadian Arctic Archipelago. An evaluation of the product with in situ sea ice thickness measurements shows that the presented sea ice thickness proxy product correctly estimates the magnitudes of the ice thickness and accurately captures spatial and temporal variability. The product estimates sea ice thickness within 30 to 50 cm uncertainty from the model. The sea ice thickness proxy product shows that sea ice is thinning over most of the Canadian Arctic, with a mean trend of −0.82 cm yr−1 in April over the whole study area (corresponding to 21 cm thinning over the 25-year record), but that trends vary locally. The Beaufort Sea and Baffin Bay show significant negative trends during all months, though with peaks in November (−2.8 cm yr−1) and April (−1.5 cm yr−1), respectively. The Parry Channel, which is part of the Northwest Passage and relevant for shipping, shows significant thinning in autumn. The sea ice thickness proxy product provides, for the first time, the opportunity to study long-term trends and variability in sea ice thickness in the Canadian Arctic, including the narrow channels in the Canadian Arctic Archipelago.

AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting

For maritime navigation in the Arctic, sea ice charts are an essential tool, which still to this day is drawn manually by professional ice analysts. The total Sea Ice Concentration (SIC) is the primary descriptor of the charts and indicates the fraction of ice in an ocean surface area. Naturally, automating the SIC chart creation is desired. However, the optimal representation of the corresponding machine-learning task is ambivalent and discussed in the community. In this study, we explore the representation with either regressional or classification objectives, each with two different (weighted) loss functions: Mean Square Error and Binary Cross-Entropy, and Categorical Cross-Entropy and the Earth Mover’s Distance, respectively. While all models achieve good results they differ as the regression-based models obtain the highest numerical similarity to the reference charts, whereas the classification-optimised models generate results more visually pleasing and consistent. Rescaling the loss functions with inverse class weights improves the performance for intermediate classes at the expense of open water and fully-covered sea ice areas.

Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

AbstractSea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation.

Possibilities of investigating ice development of the Kara sea based on CryoSat-2 and SMOS data

Sea ice age is a proxy for thickness, which can be obtained through the use of satellite data. The paper presents the results of comparison of CryoSat-2, SMOS and CryoSat-2 & SMOS fusion data with detailed ice charts (October–April, 2010–2018). The AARI ice charts were chosen as references for comparison because they integrate knowledge and data from various sources, including expert analysis of operational satellite information, in-situ measurements at coastal stations, data on ice conditions from ships of the Northern Sea Route (NSR). The division of satellite data elements into classes (stages of development) was performed according to the maximum likelihood classifier. The recognition result for each stage of development was evaluated by means of three criteria, in accordance with the class value of Mode, Median and Mean. The effectiveness of satellite data in determining the Kara Sea ice thickness varies depending on the sea ice stage of development and winter season time. Four stages of development (old ice, thick first-year ice, medium first-year ice, nilas) showed the best recognition results. Although the CryoSat-2 mission was designed primarily to detect climate-dependent variations of the thickness of floating ice, in terms of statistical recognition of the Kara Sea ice stages of development, CryoSat-2 data can also be used to retrieve the thickness of thick first-year ice (January–April) and the thickness of medium first-year ice (January–February). For the remaining stages within the study area, the altimetry method shows a significant uncertainty, which can be resolved for nilas, thin first-year ice and medium first-year ice (March) by using SMOS data and CryoSat-2 & SMOS fusion data. In general, altimetric data, radiometric data and combination thereof can be applied in the complex analysis of all available information to ensure hydrometeorological and navigation support. Also, it is proposed to use the data of the ICESat-2 laser altimeter and to make a general comparison with in-situ measurements.

Seasonal Sea Ice Conditions Affect Caribou Crossing Areas Around Qikiqtaq, Nunavut: Uqsuqtuurmiut Knowledge Guides Ice Chart Analysis

Though polar ecologists consider sea ice primarily as a habitat for marine mammals, caribou use sea ice to complete their reproductive cycles, to access areas with preferred climatic and vegetation conditions, and to avoid predators seasonally and sporadically. Building on previous caribou research in Uqsuqtuuq (Gjoa Haven, Nunavut), we explored the connections between caribou and sea ice phenology in 5 community-identified caribou crossing areas around Qikiqtaq (King William Island). We defined freeze-up and breakup based on Uqsuqtuurmiut (people of Uqsuqtuuq) knowledge of caribou habitat requirements, to orient our analysis to the complex and multifaceted hazards that caribou can encounter while moving through their dynamic and unpredictable sea ice habitat. We investigated the reliability of caribou sea ice habitat surrounding Qikiqtaq, prioritizing key transitional periods with intensified caribou movement. We use regional ice charts produced by the Canadian Ice Service (CIS) and held workshops with Uqsuqtuurmiut to understand how sea ice phenology and caribou mobility have changed over time. The high spatial and temporal variability of sea ice phenology around Qikiqtaq facilitates caribou moving across sea ice should they need to respond to seasonal or unpredictable changes in ecological conditions or anthropogenic disturbance. Therefore, these localized sea ice conditions may increase caribou resiliency to changes or extreme events by providing alternative options for movement across the sea ice. We encourage others to consider the needs of wildlife sea ice users when assessing or providing ice information.