Passive Microwave Sea Ice Concentration Research Articles

Characterization of north Greenland polynyas with super-resolved passive microwave sea ice concentration

ABSTRACT Located at the easternmost sector of the Last Ice Area (LIA) with the oldest and thickest sea ice, the north of Greenland has witnessed some unprecedented polynyas since 2018, receiving considerable attention. Sea ice concentration (SIC) derived from passive microwave data can provide near daily observations of these polynyas. However, these SIC products are limited by the coarse spatial resolution of passive microwave data, with mixed pixels and large uncertainties at the ice–water divide. It is especially difficult in the case of newly formed polynyas in the form of leads with narrow openings, preventing accurate estimation of the regional energy budget and heat fluxes as well as the hindering causal analysis of the polynyas. In this paper, a novel state-of-the-art deep-learning-based super-resolution (SR) method is adopted, and passive microwave SR images, with spatial resolutions of up to 4 times the original, are generated and employed to derive super-resolved SIC (SR-SIC). Experimental results confirm the benefits of SR-SIC for finer monitoring of sea ice at both the polynya and lead scale compared to the original lower-resolution SIC, with an average 7.55% improvement in F1-Score for polynya extent and 31.17% for lead width. Using the SR-SIC, more accurate quantitative parameters of polynyas and leads are extracted to present more detailed spatial distributions at their different development stages. For example, SR-SIC with reduced mixed pixels can distinguish more SIC values below 40%, advantageous for detecting open water pixels within polynya areas and improving the accuracy of heat fluxes estimation and ice production calculation. Moreover, SR-SIC has the capability to identify leads with openings less than 3 km and to locate the finer boundaries of the polynyas. Finally, it is found that the lead development along the 2018 winter polynya is associated with regional wind, air temperature, and ice drift. The prominent summer polynya events in 2018, 2020, and 2021 may be in part caused by the thinning sea ice in the north of Greenland.

Evaluation of Sea Ice Concentration Data Using Dual-Polarized Ratio Algorithm in Comparison With Other Satellite Passive Microwave Sea Ice Concentration Data Sets and Ship-Based Visual Observations

The dual-polarized ratio (DPR) algorithm is a new algorithm that enable calculation of Arctic sea ice concentration from the 36.5-GHz channel of the sensor Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2). In this paper, we demonstrate results that the sea ice concentration data using DPR algorithm (DPR-AMSR) are evaluated and compared with other eight Arctic sea ice concentration data products with respect to differences in sea ice concentration, sea ice area, and sea ice extent. On a pan-Arctic scale, the evaluation results are mostly very similar between DPR-AMSR and the bootstrap algorithm from AMSR-E/AMSR2 (BT-AMSR), the bootstrap algorithm from SSM/I or SSMIS (BT-SSMI), the ARTIST Sea Ice algorithm from AMSR-E/AMSR2 (ASI-AMSR), and the enhanced NASA Team algorithm from AMSR-E/AMSR2 (NT2-AMSR). Among of these products, the differences in sea ice concentration agree within ±5%. However, European Space Agency Climate Change Initiative algorithm from AMSR-E/AMSR2 (SICCI-AMSR), the European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility from SSM/I or SSMIS (OSI-SSMI), the ARTIST Sea Ice algorithm from SSM/I or SSMIS (ASI-SSMI), and the NASA Team algorithm from SSM/I or SSMIS (NT1-SSMI) are all lower than DPR-AMSR at sea ice edge. And NT1-SSMI had the largest negative difference, which was lower than -15% or even 20%.The difference of sea ice area was consistently within ±0.5 million km2 between DPR-AMSR and BT-AMSR, BT-SSMI, ASI-AMSR, and NT2-AMSR in all years. The smallest difference was with BT-SSMI (less than 0.1 million km2), whereas the largest difference was with NT1-SSMI (up to 1.5 million km2). In comparisons of sea ice extent, BT-AMSR, NT1-SSMI, and NT2-AMSR estimates were consistent with that of DPR-AMSR and were within ±0.5 million km2. However, differences exceeded 0.5 million km2 between DPR-AMSR and the other data sets. When ship-based visual observation (OBS) values ranged from 85% to 100%, the difference between DPR-AMSR and OBS was less than 1%. There were relatively large differences between DPR-AMSR and OBS when OBS values were less than 85% or were recorded during the summer, although those differences were also within 10%.

Wind amplifies the polar sea ice retreat

The rapid polar sea ice retreat and its drivers are challenging and still unresolved questions in climate change research. In particular, the relationship between near-surface wind speed and sea ice extent remains unclear for two main reasons: (1) observed wind speeds over Polar Regions are very sparse, and (2) simulated winds by climate models are dependent on subjective parameterizations of boundary layer stratification, ultimately leading to large uncertainty. Here, we use observation-based data (passive microwave sea ice concentration and six different reanalysis datasets) together with output from 26 climate models (from the CMIP5 archive) to quantify the relationships between near-surface wind speed and sea ice concentration over the past 40 years. We find strong inverse relationships between near-surface wind speed and sea ice concentration that are consistent among the six reanalysis datasets. The poleward wind component is particularly increasing in years of reduced sea ice concentration, which contributes to the enhancement of the atmospheric (surface oceanic) poleward heat flux by up to 24 ± 1% (29 ± 2%) in the Arctic and 37 ± 3% (51 ± 3%) in the Antarctic seas, therefore boosting the impact of polar sea ice loss and contributing to polar amplification of climate warming. In addition, our results show a marginal contribution of the dynamical (pushing/opening/compacting) effects of wind on sea ice compared to the thermodynamic effects which in turn play a lower role than the associated change in local surface Autumn–Winter turbulent and Spring–Summer radiative fluxes. Climate models generally produce similar results but with lower magnitude, and one model even simulates the opposite relationship wind/sea-ice. Given the rapid changes in polar climate and the potential impacts on the mid-latitudes, it is urgent that model developments make use of evidence from satellite observations and reanalysis datasets to reduce uncertainties in the representation of relationships between polar winds and sea ice.

Evaluation of a New Merged Sea-Ice Concentration Dataset at 1 km Resolution from Thermal Infrared and Passive Microwave Satellite Data in the Arctic

Sea-ice concentration (SIC) data with fine spatial resolution and spatially continuous coverage are needed, for example, for estimating heat fluxes. Passive microwave measurements of the Advanced Scanning Microwave Radiometer 2 (AMSR2) offer spatial continuity, but are limited to spatial resolutions of 5 km and coarser. Thermal infrared data of the Moderate Resolution Imaging Spectroradiometer (MODIS) provide a spatial resolution of 1 km, but are limited to cloud-free scenes. We exploit the benefits of both and present a merged SIC dataset with 1 km spatial resolution and spatially continuous coverage for the Arctic. MODIS and AMSR2 SIC are retrieved separately and then merged by tuning the MODIS SIC to preserve the mean AMSR2 SIC. We first evaluate the variability of the dynamically retrieved MODIS ice tie-point. Varying the starting position of the area used for the tie-point retrieval changes the MODIS SIC by on average 1.9%, which we mitigate by considering different starting positions and using the average as ice tie-point. Furthermore, the SIC datasets are evaluated against a reference dataset derived from Sentinel-2A/B reflectances between February and May 2019. We find that the merged SIC are 1.9% smaller than the reference SIC if thin ice is considered as ice and 4.9% higher if thin ice is considered as water. There is only a slight bias (0.3%) between the MODIS and the merged SIC; however, the root mean square deviation of 5% indicates that the two datasets do yield different results. In an example of poor-quality MODIS SIC, we identify an unscreened cloud and high ice-surface temperature as reasons for the poor quality. Still, the merged SIC are of similar quality as the passive microwave SIC in this example. The benefit of merging MODIS and AMSR2 data is demonstrated by showing that the finer resolution of the merged SIC compared to the AMSR2 SIC allows an enhanced potential for the retrieval of leads. At the same time, the data are available regardless of clouds. Last, we provide uncertainty estimates. The MODIS and merged SIC uncertainty are between 5% and 10% from February to April and increase up to 25% (merged SIC) and 35% (MODIS SIC) in May. They are identified as conservative uncertainty estimates.

Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from −3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.

Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) – SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %–25 % for groups I and III and up to 30 %–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %–5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field.

Extended Categorical Triple Collocation for Evaluating Sea Ice/Open Water Data Sets

A method to extend categorical triple collocation (CTC) to five data sets, three of which must have conditionally independent errors, is presented. The method is shown to enable comparison of three passive microwave sea ice concentration data sets, and can easily be extended to for use with more data sets. The method is evaluated in the Gulf of Saint Lawrence, on the east coast of Canada, during the freeze-up period. It is found that the ice concentration data set from the European space agency sea ice climate change initiative (ESA-SICCI) and the NASA Team 2 (NT2) algorithms have higher balanced accuracy than that from the Artist sea ice (ASI) algorithm.

Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay

Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.

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

Abstract. We report on results of a systematic inter-comparison 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.

Assessment of Categorical Triple Collocation for Sea Ice/Open Water Observations: Application to the Gulf of Saint Lawrence

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system.

Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.

Heat Flux Sources Analysis to the Ross Ice Shelf Polynya Ice Production Time Series and the Impact of Wind Forcing

The variation of Ross Ice Shelf Polynya (RISP) ice production is a synergistic result of several factors. This study aims to analyze the 2003–2017 RISP ice production time series with respect to the impact of wind forcing on heat flux sources. RISP ice production was estimated from passive microwave sea ice concentration images and reanalysis meteorological data using a thermodynamic model. The total ice production was divided into four components according to the amount of ice produced by different heat fluxes: solar radiation component (Vs), longwave radiation component (Vl), sensible heat flux component (Vfs), and latent heat flux component (Vfe). The results show that Vfs made the largest contribution, followed by Vl and Vfe, while Vs had a negative contribution. Our study reveals that total ice production and Vl, Vfs, and Vfe highly correlated with the RISP area size, whereas Vs negatively correlated with the RISP area size in October, and had a weak influence from April to September. Since total ice production strongly correlates with the polynya area and this significantly correlates with the wind speed of the previous day, strong wind events lead to sharply increased ice production most of the time. Strong wind events, however, may only lead to mildly increasing ice production in October, when enlarged Vs reduces the ice production. Wind speed influences ice production by two mechanisms: impact on polynya area, and impact on heat exchange and phase transformation of ice. Vfs and Vfe are influenced by both mechanisms, while Vs and Vl are only influenced by impact on polynya area. These two mechanisms show different degrees of influence on ice production during different periods. Persistent offshore winds were responsible for the large RISP area and high ice production in October 2005 and June 2007.

Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast

Monitoring the marginal ice zone (MIZ) is becoming increasingly important due to recent evidence that the width of the MIZ is changing with climate. A method to automatically detect the MIZ in synthetic aperture radar (SAR) imagery is proposed. The method utilizes the curve-like features of MIZ in SAR images. A multiscale strategy, the curvelet transform, is chosen to extract features from the SAR images. The statistical and co-occurrence features of curvelet coefficients at an appropriate scale are used to identify the MIZ from open water and consolidated ice. Experimental results show a significant increase in classification accuracy (89.7%) compared with the most commonly used MIZ definition from passive microwave sea ice concentration (74%), especially in the diffuse MIZ.

Reduced sea ice protection period increases storm exposure in Kivalina, Alaska

On Arctic coasts, erosion is limited by the presence of nearshore sea ice, which creates a protective barrier from storms. In Kivalina, an Alaskan Inupiaq Inuit community, decreasing seasonal sea ice extent and a lengthening of the open-water season may be resulting in fall storms that (1) generate higher, longer, and more destructive waves and (2) cause damage later in the year, resulting in increased flooding and erosion. We assess trends in the duration of nearshore sea ice and their relationship with storm occurrence over the period 1979–2015 in Kivalina. Analysis of passive microwave sea ice concentration data indicates that the open-water season has increased by 5.6 ± 1.2 days/decade over the last 37 years, with moderate evidence that it is extending further into the fall than into the spring. This is correlated with an increased reporting frequency of high-damage storms; 80% of reported storms since 1970 occurred in the last 15 years. Each high-damage storm event occurred during the open-water season for that year. Our findings support Kivalina villagers’ assertions that climate change increases storm exposure and associated damages from flooding and erosion.

Links between the Amundsen Sea Low and sea ice in the Ross Sea: seasonal and interannual relationships

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.

Statistical Analysis of SSMIS Sea Ice Concentration Threshold at the Arctic Sea Ice Edge during Summer Based on MODIS and Ship-Based Observational Data.

The threshold of sea ice concentration (SIC) is the basis for accurately calculating sea ice extent based on passive microwave (PM) remote sensing data. However, the PM SIC threshold at the sea ice edge used in previous studies and released sea ice products has not always been consistent. To explore the representable value of the PM SIC threshold corresponding on average to the position of the Arctic sea ice edge during summer in recent years, we extracted sea ice edge boundaries from the Moderate-resolution Imaging Spectroradiometer (MODIS) sea ice product (MOD29 with a spatial resolution of 1 km), MODIS images (250 m), and sea ice ship-based observation points (1 km) during the fifth (CHINARE-2012) and sixth (CHINARE-2014) Chinese National Arctic Research Expeditions, and made an overlay and comparison analysis with PM SIC derived from Special Sensor Microwave Imager Sounder (SSMIS, with a spatial resolution of 25 km) in the summer of 2012 and 2014. Results showed that the average SSMIS SIC threshold at the Arctic sea ice edge based on ice-water boundary lines extracted from MOD29 was 33%, which was higher than that of the commonly used 15% discriminant threshold. The average SIC threshold at sea ice edge based on ice-water boundary lines extracted by visual interpretation from four scenes of the MODIS image was 35% when compared to the average value of 36% from the MOD29 extracted ice edge pixels for the same days. The average SIC of 31% at the sea ice edge points extracted from ship-based observations also confirmed that choosing around 30% as the SIC threshold during summer is recommended for sea ice extent calculations based on SSMIS PM data. These results can provide a reference for further studying the variation of sea ice under the rapidly changing Arctic.