Sea Ice Lead Research Articles

Arctic and Southern Ocean polar sea level maps and along-tracks from multi-mission satellite altimetry from 2011 to 2021

Polar sea surface height observation by radar altimeters requires missions with high-latitude orbit and specific processing to observe the sea-ice-covered region within fractures in the ice. Here, we combine sea surface height estimates from different radar satellites over the ice-free and ice-covered polar oceans to create cross-calibrated along-tracks and gridded products over the Arctic Ocean (2011–2021) and the Southern Ocean (2013–2021). The sea surface height from our regional polar products is in great agreement with tide gauges and bottom pressure recorders at monthly timescales in seasonally to year-round ice-covered regions. Thanks to the use of several missions and the mapping strategy, our multi-mission products have a greater resolution than mono-mission products. Part of the sea level variability of the Arctic Ocean product is related to the Arctic Oscillation atmospheric circulation. At long term, the Arctic altimetry sea level is coherent with in-situ steric height evolution in the Beaufort gyre, and negative sea level trends over the 10-year period are observed in the East Siberian slope region, which may be related to the local freshwater decrease observed by other studies. Our regional polar sea level products are limited by current understanding of the sea-ice lead measurements, and homogenization of these polar products with global sea level products needs to be tackled.

Central Arctic Ocean surface–atmosphere exchange of CO2 and CH4 constrained by direct measurements

Abstract. The central Arctic Ocean (CAO) plays an important role in the global carbon cycle, but the current and future exchange of the climate-forcing trace gases methane (CH4) and carbon dioxide (CO2) between the CAO and the atmosphere is highly uncertain. In particular, there are very few observations of near-surface gas concentrations or direct air–sea CO2 flux estimates and no previously reported direct air–sea CH4 flux estimates from the CAO. Furthermore, the effect of sea ice on the exchange is not well understood. We present direct measurements of the air–sea flux of CH4 and CO2, as well as air–snow fluxes of CO2 in the summertime CAO north of 82.5∘ N from the Synoptic Arctic Survey (SAS) expedition carried out on the Swedish icebreaker Oden in 2021. Measurements of air–sea CH4 and CO2 flux were made using floating chambers deployed in leads accessed from sea ice and from the side of Oden, and air–snow fluxes were determined from chambers deployed on sea ice. Gas transfer velocities determined from fluxes and surface-water-dissolved gas concentrations exhibited a weaker wind speed dependence than existing parameterisations, with a median sea-ice lead gas transfer rate of 2.5 cm h−1 applicable over the observed 10 m wind speed range (1–11 m s−1). The average observed air–sea CO2 flux was −7.6 mmolm-2d-1, and the average air–snow CO2 flux was −1.1 mmolm-2d-1. Extrapolating these fluxes and the corresponding sea-ice concentrations gives an August and September flux for the CAO of −1.75 mmolm-2d-1, within the range of previous indirect estimates. The average observed air–sea CH4 flux of 3.5 µmolm-2d-1, accounting for sea-ice concentration, equates to an August and September CAO flux of 0.35 µmolm-2d-1, lower than previous estimates and implying that the CAO is a very small (≪ 1 %) contributor to the Arctic flux of CH4 to the atmosphere.

Distribution Characteristics and Influencing Factors of Sea Ice Leads in the Weddell Sea, Antarctica

The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion.

Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents

Abstract. We use a novel sea-ice lead climatology for the winters of 2002/03 to 2020/21 based on satellite observations with 1 km2 spatial resolution to identify predominant patterns in Arctic wintertime sea-ice leads. The causes for the observed spatial and temporal variabilities are investigated using ocean surface current velocities and eddy kinetic energies from an ocean model (Finite Element Sea Ice–Ice-Shelf–Ocean Model, FESOM) and winds from a regional climate model (CCLM) and ERA5 reanalysis, respectively. The presented investigation provides evidence for an influence of ocean bathymetry and associated currents on the mechanic weakening of sea ice and the accompanying occurrence of sea-ice leads with their characteristic spatial patterns. While the driving mechanisms for this observation are not yet understood in detail, the presented results can contribute to opening new hypotheses on ocean–sea-ice interactions. The individual contribution of ocean and atmosphere to regional lead dynamics is complex, and a deeper insight requires detailed mechanistic investigations in combination with considerations of coastal geometries. While the ocean influence on lead dynamics seems to act on a rather long-term scale (seasonal to interannual), the influence of wind appears to trigger sea-ice lead dynamics on shorter timescales of weeks to months and is largely controlled by individual events causing increased divergence. No significant pan-Arctic trends in wintertime leads can be observed.

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.

An Improved Lightweight U-Net for Sea Ice Lead Extraction From Multipolarization SAR Images

An Improved Lightweight U-Net for Sea Ice Lead Extraction From Multipolarization SAR Images

Winter Sea-Ice Lead Detection in Arctic Using FY-3D MERSI-II Data

Lead is an important feature of the Arctic ice cover, with possible contents of thin ice /or open water. In this letter, we present an algorithm for lead detection based on brightness temperature observations from a single thermal infrared channel of MERSI-II onboard the Chinese FY-3D satellite. Lead contents is classified into open water and thin ice with support information from Sentinel-1 SAR data. Results are evaluated based on visual interpretation of MERSI-II TIR (Thermal infrared) and Sentinel-2 NIR (Near Infrared) data. The accuracy is found to be 85.6% for lead detection and 67% and 52% for thin ice and open water within the lead, respectively.

On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic

Abstract. We explore several statistical properties of the observed and simulated Arctic sea-ice lead fraction, as well as the statistics of simulated Arctic ocean–atmosphere heat fluxes. First we show that the observed lead fraction in the Central Arctic has a monofractal spatial scaling, which we relate to the multifractal spatial scaling present in sea-ice deformation rates. We then show that the relevant statistics of the observed lead fraction in the Central Arctic are well represented by our model, neXtSIM. Given that the heat flux through leads may be up to 2 orders of magnitude larger than that through unbroken ice, we then explore the statistical properties (probability distribution function – PDF – and spatial scaling) of the heat fluxes simulated by neXtSIM. We demonstrate that the modelled heat fluxes present a multifractal scaling in the Central Arctic, where heat fluxes through leads dominate the high-flux tail of the PDF. This multifractal character relates to the multi- and monofractal character of deformation rates and the lead fraction. In the wider Arctic, the high-flux tail of the PDF is dominated by an exponential decay, which we attribute to the presence of coastal polynyas. Finally, we show that the scaling of the simulated lead fraction and heat fluxes depends weakly on the model resolution and discuss the role sub-grid-scale parameterisations of the ice heterogeneity may have in improving this result.

A New Algorithm for Daily Sea Ice Lead Identification in the Arctic and Antarctic Winter from Thermal-Infrared Satellite Imagery

The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice leads contribute to the sea ice production and are sources for the formation of dense water which affects the ocean circulation. Atmospheric and ocean models strongly rely on observational data to describe the respective state of the sea ice since numerical models are not able to produce sea ice leads explicitly. For the Arctic, some lead datasets are available, but for the Antarctic, no such data yet exist. Our study presents a new algorithm with which leads are automatically identified in satellite thermal infrared images. A variety of lead metrics is used to distinguish between true leads and detection artefacts with the use of fuzzy logic. We evaluate the outputs and provide pixel-wise uncertainties. Our data yield daily sea ice lead maps at a resolution of 1 km2 for the winter months November– April 2002/03–2018/19 (Arctic) and April–September 2003–2019 (Antarctic), respectively. The long-term average of the lead frequency distributions show distinct features related to bathymetric structures in both hemispheres.

Wintertime Arctic Sea Spray Aerosol Composition Controlled by Sea Ice Lead Microbiology.

The Arctic is experiencing the greatest warming on Earth, as most evident by rapid sea ice loss. Delayed sea ice freeze-up in the Alaskan Arctic is decreasing wintertime sea ice extent and changing marine biological activity. However, the impacts of newly open water on wintertime sea spray aerosol (SSA) production and atmospheric composition are unknown. Herein, we identify SSA, produced locally from open sea ice fractures (leads), as the dominant aerosol source in the coastal Alaskan Arctic during winter, highlighting the year-round nature of Arctic SSA emissions. Nearly all of the individual SSA featured thick organic coatings, consisting of marine saccharides, amino acids, fatty acids, and divalent cations, consistent with exopolymeric secretions produced as cryoprotectants by sea ice algae and bacteria. In contrast, local summertime SSA lacked these organic carbon coatings, or featured thin coatings, with only open water nearby. The individual SSA composition was not consistent with frost flowers or surface snow above sea ice, suggesting that neither hypothesized frost flower aerosolization nor blowing snow sublimation resulted in the observed SSA. These results further demonstrate the need for inclusion of lead-based SSA production in modeling of Arctic atmospheric composition. The identified connections between changing sea ice, microbiology, and SSA point to the significance of sea ice lead biogeochemistry in altering Arctic atmospheric composition, clouds, and climate feedbacks during winter.

Characterizing the System Impulse Response Function From Photon-Counting LiDAR Data

NASA’s Multiple Altimeter Beam Experimental LiDAR (MABEL) is an aircraft-based photon-counting laser altimeter designed as a simulator to test measurement techniques and algorithms for Advanced Topographic Laser Altimeter System (ATLAS), the sole instrument on NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. By measuring the time of flight, pointing angle, and absolute position for individual photons, ICESat-2 provides detailed elevation measurements of earth’s surface. Calculating accurate and precise elevations requires an understanding of how photons interact with surfaces, and characterization of the photon distribution after returning from surfaces. Neither MABEL nor ATLAS records the transmitted laser pulse shape, relying instead on aggregating several pulses worth of photons, often using histograms, to characterize the pulse shape. In this paper, we assess the limitations of using histograms and propose a more robust method to describe MABEL’s system impulse-response function using an exponentially modified Gaussian distribution. We also provide standard error estimates for the arithmetic mean and standard deviation calculations, and for exponentially modified Gaussian parameters using a Monte Carlo sensitivity analysis. We apply this method to photon returns from a sea ice lead and from a dry salt lake bed as case studies for estimating the standard error associated with sample size for the arithmetic mean and standard deviation, and for the exponentially modified Gaussian parameters. We use these standard errors to calculate the minimum number of photons required to find both Gaussian and exponentially modified Gaussian distribution parameters within 3 cm of their parent population values.

Comparative Evaluation of Sea Ice Lead Detection Based on SAR Imagery and Altimeter Data

The detection of sea ice leads is a prerequisite for the estimation of ice freeboard and thickness from altimeter data. The classification of altimeter waveforms is generally performed using statistical parameters on the echo power or machine learning approaches directly on the waveforms. The validation and optimization of such algorithms can be carried out using a set of reference cases provided by Earth Observation images. In this paper, we first developed a new lead detector based on Sentinel-1 (S-1) synthetic aperture radar (SAR) images. A robust and consistent methodology for the joint assessment of Altimeter and SAR leads detector is then provided. We propose to fully account for the 2-D geometric problem when comparing the 1-D altimeter track and 2-D SAR image. The surface of the lead intersecting the altimeter footprint and its distance to nadir are considered here. Based on collocated Sentinel-3 (S-3) altimeter data and S-1 images, the performance of our S-3 lead detector is fully assessed. A new parameterization is found resulting in a better tradeoff between good detection and false alarm rate. A similar analysis is performed using AltiKa altimeter data, showing enhanced performance for S-3 altimeter data acquired in Delay-Doppler mode with reduced off-nadir returns.

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

Development of an ENVISAT Altimetry Processor Providing Sea Level Continuity Between Open Ocean and Arctic Leads

Over the Arctic regions, current conventional altimetry products suffer from a lack of coverage or from degraded performance due to the inadequacy of the standard processing applied in the ground segments. This paper presents a set of dedicated algorithms able to process consistently returns from open ocean and from sea-ice leads in the Arctic Ocean (detection of water surfaces and derivation of water levels using returns from these surfaces). This processing extends the area over which a precise sea level can be computed. In the frame of the European Space Agency Sea Level Climate Change Initiative ( http://cci.esa.int ), we have first developed a new surface identification method combining two complementary solutions, one using a multiple-criteria approach (in particular the backscattering coefficient and the peakiness coefficient of the waveforms) and one based on a supervised neural network approach. Then, a new physical model has been developed (modified from the Brown model to include anisotropy in the scattering from calm protected water surfaces) and has been implemented in a maximum likelihood estimation retracker. This allows us to process both sea-ice lead waveforms (characterized by their peaky shapes) and ocean waveforms (more diffuse returns), guaranteeing, by construction, continuity between open ocean and ice-covered regions. This new processing has been used to produce maps of Arctic sea level anomaly from 18-Hz ENVIronment SATellite/RA-2 data.

A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E

We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series.

A Sea-Ice Lead Detection Algorithm for Use With High-Resolution Airborne Visible Imagery

The detection of leads, or cracks, in sea ice is critical for the derivation of sea-ice freeboard from altimetric measurements of sea-ice elevation. We present an approach for lead detection in sea ice using high-resolution visible imagery from airborne platforms. We develop a new algorithm, i.e., the sea-ice lead detection algorithm using minimal signal (SILDAMS), that detects clouds, extracts leads, and classifies ice types within leads from airborne visible imagery. Cloud detection is based on an assessment of local variances of pixel brightness across image scenes and where available coincident altimetric measurements are used to confirm suspected cloudy scenes. The lead extraction step computes affine time-frequency distributions (minimal signal) for the Red, Green, and Blue channels of each image. The transformed outputs are combined to take advantage of three channels simultaneously. Finally, lead pixel geolocations are extracted using a set of uniform thresholds for ice typing (including open water, thin ice, and gray ice) within leads along each flight line. SILDAMS was tested using data from the Digital Mapping System (DMS). DMS digital photographs represent the highest resolution ( ≈10 cm) visible imagery available over sea ice and were collected during NASA Operation IceBridge sea-ice flights in the Antarctic and the Arctic in 2009 and 2010, respectively. We demonstrate that SILDAMS has a high lead detection capability of 99%.

Holes in Progressively Thinning Arctic Sea Ice Lead to New Ice Algae Habitat

Holes in Progressively Thinning Arctic Sea Ice Lead to New Ice Algae Habitat

Brine-Driven Eddies under Sea Ice Leads and Their Impact on the Arctic Ocean Mixed Layer

Abstract Eddy generation induced by a line-shaped salt flux under a sea ice lead and associated salt transport are investigated using a three-dimensional numerical model. The model is designed to represent a typical condition for the wintertime Arctic Ocean mixed layer, where new ice formation within leads is known to be one of the primary sources of dense water. The result shows that along-lead baroclinic jets generate anticyclonic eddies at the base of the mixed layer, and almost all the lead-originated salt is contained inside these eddies. These eddies survive for over a month after closing of the lead and transport the lead-originated salt laterally. Consequently, the lead-origin salt settles only on the top of the halocline and is not used for increasing salinity of the mixed layer. Sensitivity experiments suggest that the horizontal scale of generated eddies depends only on the surface forcing and is proportional to the cube root of the total amount of salt input. This scaling of eddy size is consistent with a theoretical argument based on a linear instability theory. Parameterizing these processes would improve representation of the Arctic Ocean mixed layer in ocean general circulation models.

The sensitivity of a one‐dimensional thermodynamic sea ice model to changes in cloudiness

A thermodynamic sea ice‐lead model is used to assess the importance of cloud cover changes to modeled ice thickness. For regions of either permanent multiyear ice or seasonal sea ice, the cloud amount variations have relatively little impact. However, for regions where the presence of summer ice is variable from year to year, the predicted ice thickness is strongly dependent on cloud cover. In general, with a snow covered surface, decreased cloud leads to surface cooling while increased cloud gives rise to a surface warming. For a melting bare ice surface, the reverse occurs. The ice model response time is too long for interannual variations in cloud amount to explain interannual variations in ice thickness and extent. Nevertheless, the implication of the results is that numerical modeling of sea ice distribution requires accurate cloud data or cloud prediction and that trends in cloud cover may lead to significant perturbations in sea ice extent and thickness.