A document by Kyle Duncan. Click on the document to view its contents.

A document by Kyle Duncan. Click on the document to view its contents.

Melt ponds play an important role in the seasonal evolution of Arctic sea ice. During the melt season, snow atop the sea ice begins to metamorphose and melt, forming ponds on the ice. These ponds reduce the albedo of the surface, allowing for increased solar energy absorption and thus further melting of snow and ice. Analyzing the spatial distribution and temporal evolution of melt ponds helps us understand the sea ice processes that occur during the summer melt season. It has been shown that the inclusion of melt pond parameters in sea ice models increases the skill of predicting the summer sea ice minimum extent. Previous studies have used remote sensing imagery to characterize surface features and calculate melt pond statistics. Here we use new observations of melt ponds obtained by the Digital Mapping System (DMS) flown onboard NASA Operation IceBridge (OIB) during two Arctic summer melt campaigns which surveyed thousands of kilometers of sea ice and resulted in more than 45,000 images. One campaign was conducted in the Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic Ocean north of Greenland (July 2017). Using these data we expect to advance our understanding of the differences and similarities between melt pond features on young, thin sea ice seen in the Beaufort Sea versus those on multi-year ice. We have developed a pixel-based classification scheme by considering the different RGB spectral values associated with each surface type. We identify four sea ice surface types (level ice, rubbled ice, open water, and melt ponds). The classification scheme enables the calculation of parameters including melt pond fraction, ice concentration, melt pond area, and melt pond dimensions. We compare results with data from the Airborne Topographic Mapper (ATM), a laser altimeter also operated during these OIB missions. Given the extent over which the OIB data are available, regional information may be derived. Leveraging existing satellite data products, we examine whether the high-resolution airborne statistics are representative of the region and can be scaled up for comparison against satellite-derived parameters such as ice concentration and extent.

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

During the Arctic summer season, snow atop the sea ice melts and pools into low-lying areas on the surface. These melt ponds reduce surface albedo and increase solar absorption in the Arctic Ocean. Throughout the summer, melt ponds grow, drain, and connect, through a complex drainage system. Current melt pond schemes in sea ice models, such as the level-ice scheme in the Los Alamos Sea Ice Model (CICE), rely on a linear relationship between pond depth and fraction to predict the evolution of pond growth as the snow and sea ice melt. Although the inclusion of melt ponds in models has been shown to improve forecasts of end-of-summer sea ice extent, observations of melt pond depth and fraction guiding these models are from SHEBA, a spatially-limited field campaign which occurred over 20 years ago. Until recently, melt ponds characteristics have been difficult to resolve from spaceborne platforms due to their small size (10s - 100s m in diameter), and indistinguishable radiometric similarity to open water. Here we show that new, high-resolution laser altimetry measurements from ICESat-2 (IS2), combined with coincident high-resolution satellite imagery, provides a three-dimensional view of the melting sea ice cover. IS2, launched in September 2018, has now observed two summer melt seasons in the Arctic. IS2 operates at 532 nm, a wavelength that penetrates low turbidity water, and can therefore be used to capture the bathymetry of shallow water features. Building on previous work, we demonstrate IS2’s ability to detect and measure melt ponds on multiyear sea ice. We validate the existence of melt ponds with high resolution (10 m) visible imagery from the Sentinel-2 (S2) MultiSpectral Instrument. We apply the “density dimension algorithm – bifurcate” (DDA-bifurcate), an auto-adaptive algorithm utilizing data aggregation with the ability to track two surfaces, as well as a second algorithm that tracks melt pond surface and bottom, to derive melt pond depth for dozens of melt ponds in 2019 and 2020. Applying a sea ice surface classification algorithm to S2 imagery, we are able to determine melt pond fraction. We compare our findings of coincident melt pond fraction and depth with the melt pond parameterization used in the level-ice scheme in CICE. We discuss our results in the context of the existing literature on pond depth and volume.

ICESat-2 (IS2), launched in September 2018, has been providing high-resolution measurements of Arctic sea ice for two years. IS2’s main instrument, the Advanced Topographic Laser Altimeter System (ATLAS), is a photon-counting lidar with a pulse frequency of 10 kHz, translating to 0.7 m along-track sampling. We use high-resolution ATLAS measurements with a new algorithm, to derive sea ice surface elevations for all available IS2 reference ground tracks over sea ice for the 2019-2020 winter period. Sea ice surface elevations are used to calculate sea ice parameters including surface roughness, ridge sail height, and ridge frequency for the period October 2019 to April 2020. Near coincident Airborne Topographic Mapper (ATM) lidar data were collected along IS2 orbits during the Spring 2019 NASA Operation IceBridge flight campaign. Using ATM data as a validation tool, we show that, when compared to the existing ATL07 sea ice surface height product, our algorithm discerns pressure ridge frequency and height more accurately while maintaining measurement precision at under 2 cm. We show monthly variability in the probability distributions for these sea ice parameters, with respect to ice type. Following our previous studies, we show that pressure ridges have distinct characteristics depending on the ice type in which the ridge was formed. These results demonstrate the utility of photon-counting lidar for deriving new parameterizations of sea ice surface roughness, ridge sail height, and ridge frequency, that may be used to advance high-resolution sea ice modeling.

We investigate the characteristics and distribution of pressure ridges in Arctic sea ice using surface height profiles from the Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2. Applying a new algorithm to ATLAS measurements we derive the frequency and height of individual pressure ridges and map surface roughness and ridging intensity at the basin scale over three winters between 2019 and 2021. Comparisons with near-coincident airborne lidar data show that not only can we detect individual ridges 5.6 m wide, but also measure sail height more accurately than the existing ICESat-2 sea ice height product. We find regional variability in ridge morphology is large while annual variability is low. Ridge characteristics are not only related to their parent ice type but also their geographic location. High-resolution satellite altimetry data are valuable for characterizing sea ice deformation at short length-scales, providing observations that will advance ridge parameterizations in sea ice models.

Pressure ridges are deformation features within the sea ice pack created through the collision of sea ice floes. Pressure ridges play an important role in ice drift and influence the mass and energy budgets of the Arctic Ocean. Over the past decade annual airborne surveys over Arctic sea ice have been conducted in late winter (March and April) by NASA’s Operation IceBridge (OIB) mission. A total of 74 OIB flights between 2010 and 2018 surveyed tens of thousands of kilometers of sea ice, providing observations of pressure ridges at a higher spatial and temporal resolution than previous airborne studies. Here we utilize Digital Mapping System (DMS) imagery to identify shadows cast by pressure ridge sails and, then, use these shadows to derive sail height. Over 64,000 DMS images were analyzed, allowing for more than 33 million individual sail height measurements to be calculated. We present the full sail-height distributions of new pressure ridges recently formed across a range of ice conditions on first-year (FYI) and multiyear ice (MYI), and we assess year-to-year variability. We find distinct characteristics depending on the ice type in which the pressure ridge formed. The mean and standard deviation of sail heights on FYI is ~20-30 cm lower than those formed on MYI. Maximum sail heights on FYI are ~1.5 m lower on average. Arctic sea ice is getting younger, shifting from predominantly MYI to predominantly FYI. Our results may inform new model parameterizations of pressure ridges on sea ice in the changing Arctic, thereby supporting advances in sea ice forecasting.

Since its launch, in September 2018, ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) has collected high-resolution measurements of Arctic sea ice by sampling the surface every 70 cm along-track. We utilize the high-resolution capabilities of ATLAS with a novel algorithm called the University of Maryland-Ridge Detection Algorithm (UMD-RDA) to investigate sea ice topography across a range of scales. Applying the UMD-RDA to the ATL03 Global Geolocated Photon product we measure surface roughness and derive the frequency, height, width, and angle of individual pressure ridge sails. Aggregating data from multiple orbit crossings per day we investigate ridge characteristics at length-scales varying from 1 km (individual floes) to the pan-Arctic scale (central Arctic Ocean). Here, we present an evaluation of pressure ridge characteristics during the winter seasons of 2018/19, 2019/20, and 2020/21, comparing results from distinct regions with varying ice conditions. Near-coincident, independent observations of pressure ridges with Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) lidar data, OIB near-coincident Continuous Airborne Mapping By Optical Translator (CAMBOT) high-resolution (~15 cm) optical imagery, and WorldView high-resolution (~30 cm) panchromatic satellite imagery are used to evaluate the accuracy of our ICESat-2 ridge detection scheme. There are many potential use-cases for a high-resolution sea ice topography data product within the community, ranging from navigational hazard mitigation to ecological studies of marine mammal habitats. We discuss plans for releasing these data products and discuss the improvements such data would make within high-resolution sea ice models.