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Using airborne measurements, we provide a first direct glimpse of the sea ice thickness distribution in the western Ross Sea, Antarctica, where the distinguishing sea ice process is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Two flights in November 2017 over a length of 800 km reveal a heavily deformed ice regime with a mean thickness of 2.0 ± 1.6 m. Supported by satellite image analysis, we identify regional variability in ice thickness based on formation history. Sea ice thickness gradients are highest within 100 and 200 km of the Terra Nova Bay and McMurdo Sound polynyas, respectively, where the mean thickness of the thickest 10% of ice is 7.6 m. Overall, about 80% of the ice is heavily deformed, concentrated in ridges with thicknesses of 3.0–11.8 m. This is evidence that sea ice is much thicker than in the central Ross Sea. Plain Language Summary: Antarctic sea ice is a stabilizing factor for global climate but has an unknown mass trend as thickness is particularly hard to measure. The western Ross Sea is an important region of sea ice formation; here, persistent off‐shore winds form so called coastal polynyas enabling continued freezing by pushing new ice out into the pack ice zone. Using a specialized instrument on a fixed wing aircraft, we measured in this region a mean sea ice thickness of 2 m over a distance of 800 km, which is much thicker than measured in the central Ross Sea. Making use of high‐resolution satellite images and aerial photos, we found that narrow deformational ridges grow up to 16 m in thickness and more rapidly than level ice. Such deformed ice holds up to 80% of the ice mass, which is quite easily overlooked by satellite sensors. Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea, which with this new thickness data could be higher than previously thought. Providing this information, we establish a basis for change detection and the evaluation of computer simulations of Antarctic sea ice in a changing climate. Key Points: Airborne measurements of sea ice thickness in the Western Ross Sea reveal strongly deformed ice with a mean of 2.0 and a maximum of 15.6 mTime series of satellite images show evolution and deformation history of polynya‐derived pack ice along a total survey length of 800 kmSea ice thickness gradients are highest within 100–200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m [ABSTRACT FROM AUTHOR]

To better understand recent changes of Arctic sea ice thickness and extent, it is important to distinguish between the contributions of winter growth and summer melt to the sea ice mass balance. In this study we present a Lagrangian approach to quantify summer sea ice melt in which multiyear ice (MYI) floes that were surveyed by airborne electromagnetic thickness sounding within Nares Strait during summer were backtracked, using satellite imagery, to a region in close proximity (3–20 km) to spring ice thickness surveys carried out in the Lincoln Sea. Typical modal total MYI thicknesses, including ~0.4‐m snow, ranged between 3.9 and 4.7 m in the Lincoln Sea during April. Ice‐only modal thicknesses were between 2.2 and 3.0 m in Nares Strait during August. Total thinning including snow and ice was 1.3 ± 0.1 m including 0.4 ± 0.09 m of snow melt and 0.9 ± 0.2 m of ice melt. This translates to a seasonal net heat input of 305 ± 69 MJ/m2 (262 ± 60 MJ/m2 for ice only) and seasonal net heat flux of 57 ± 13 W/m2 (45 ± 10 W/m2 for ice only), which is unlikely to be explained by solar radiation fluxes alone. Furthermore, our approach provides an improvement on traditional ice mass balance buoy estimates because it integrates melt over larger spatial scales, where melt can be highly variable due to differential melt experienced between melt ponds, bare ice, hummocks, and ridges. Plain Language Summary: Some of the oldest and thickest sea ice remains in a small region between Canada, Greenland and the North Pole, the so‐called "Last Ice Area", and will retain this ice longest into the future. Thus, continued monitoring of this unique sea ice habitat is crucial in order to document ongoing changes in the physical environment and to better predict the fate of many animals that depend on sea ice for survival (e.g., polar bears and seals). In this study, we compared sea ice thickness measurements from spring and summer to provide a better understanding of how the ice changes from season to season. Ice thickness changes provide important information on the Arctic Ocean's energy budget, a good indicator of the health of the Arctic Ocean. Our measurements are particularly important because they are the first of their kind over old, thick ice during summer in this unique region. We showed that 0.9 m of old sea ice melted from spring to summer. Although this is comparable to previous studies of old ice, it is important to continue these measurements in order document the expected ongoing changes to the sea ice environment and monitor the health of the Arctic Ocean. Key Points: MYI surveyed in the Lincoln Sea during spring had modal thicknesses of 3.9‐4.7 m, which thinned to 2.2‐3.0 m in summer in Nares StraitBacktracking individual floes with satellite images was used to reconstruct positions of the same ice fields surveyed in spring and summerEstimated sea ice melt was 0.9 +/‐ 0.2 m, slightly higher than observations by ice mass balance buoys for the same region [ABSTRACT FROM AUTHOR]

The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = -0.85) compared to FYI roughness and fp (rs = -0.52). The association between MYI thickness and fp was also negative (rs = -0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree well (mean deviations below 3.5 K). In a second comparison, we use high-resolution measurements of total ice thickness (sum of ice and snow thickness) from an electromagnetic (EM) sounding system to simulate brightness temperatures for 12 circular areas. While the SMOS observations and the simulations that use the EM modal ice thickness are highly correlated (r2=0.95), the simulated brightness temperatures are on average 12 K higher than observed by SMOS. This would correspond to an 8-cm overestimation of the modal ice thickness by the SMOS retrieval. In contrast, if the simulations take into account the shape of the EM ice thickness distributions (r2=0.87), the mean deviation between simulated and observed brightness temperatures is below 0.1 K. [ABSTRACT FROM AUTHOR]

The Arctic atmospheric boundary layer (AABL) in the central Arctic was characterized by dropsonde, lidar, ice thickness and airborne in situ measurements during the international Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) in April 2009. We discuss AABL observations in the lowermost 500 m above (A) open water, (B) sea ice with many open/refrozen leads (C) sea ice with few leads, and (D) closed sea ice with a front modifying the AABL. Above water, the AABL had near-neutral stratification and contained a high water vapor concentration. Above sea ice, a low AABL top, low near-surface temperatures, strong surface-based temperature inversions and an increase of moisture with altitude were observed. AABL properties and particle concentrations were modified by a frontal system, allowing vertical mixing with the free atmosphere. Above areas with many leads, the potential temperature decreased with height in the lowest 50 m and was nearly constant above, up to an altitude of 100-200 m, indicating vertical mixing. The increase of the backscatter coefficient towards the surface was high. Above sea ice with few refrozen leads, the stably stratified boundary layer extended up to 200-300 m altitude. It was characterized by low specific humidity and a smaller increase of the backscatter coefficient towards the surface. [ABSTRACT FROM AUTHOR]

Abstract: Sea ice is an important climate variable and is also an obstacle for marine operations in polar regions. We have developed a small and lightweight, digitally operated frequency-domain electromagnetic-induction (EM) system, a so-called EM bird, dedicated for measurements of sea ice thickness. It is 3.5 m long and weighs only 105 kg, and can therefore easily be shipped to remote places and operated from icebreakers and small helicopters. Here, we describe the technical design of the bird operating at two frequencies of f1=3.68 kHz and f2=112 kHz, and study its technical performance. On average, noise amounts to ±8.5 ppm and ±17.5 ppm for f1 and f2, respectively. Electrical drift amounts to 200 ppm/h and 2000 ppm/h for f1 and f2, during the first 0.5 h of operation. It is reduced by 75% after 2 h. Calibration of the Inphase and Quadrature ppm signals varies by 2 to 3%. A sensitivity study shows that all these signal variations do affect the accuracy of the ice thickness retrieval, but that it remains better than ±0.1 m over level ice in most cases. This accuracy is also confirmed by means of comparisons of the helicopter EM data with other thickness measurements. The paper also presents the ice thickness retrieval from single-component Inphase data of f1. [Copyright &y& Elsevier]

Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from ADCP bottom track (BT) mode measurements of error velocity and range. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings. We demonstrate that this method can be used to obtain daily mean sea ice draft time series that, on average, are within 20% of ULS-derived draft time series. ULS and ADCP ice draft time series were observed by four moorings in the Laptev Sea and show correlations between 0.7 and 0.9. This new approach is not a substitute for high-frequency, high-precision ULS measurements of ice draft but it provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data that have not been not used for that purpose to date. This method has the potential to close data gaps and extend existing ice draft time series in all ice-covered regions and supports the validation of sea ice thickness products from satellite missions such as CryoSat-2, SMOS or ENVISAT. [ABSTRACT FROM AUTHOR]

Abstract: We present a semi-automatic classification algorithm for processing of aerial photographs of sea ice in combination with total (sea ice plus snow) sea ice thickness measurements from a helicopter-borne electromagnetic induction device. The algorithm is based on discriminant analysis of spectral and textural features in normalised images using a training set and includes five ice classes. Supervision and optional additional training sets can be used to further reduce misclassifications. The photographs were taken simultaneously with the sea ice thickness measurements and the results from the classification and the thickness data have been merged for more detailed analysis. The algorithm was tested using two case studies from flights over the summer pack ice north of Svalbard and in Fram Strait. The observed ice thickness distribution was very similar in both regions but the results from the classified images revealed considerable differences. The sea ice cover north of Svalbard contained a high fraction of melt ponds and only very little thin or brash ice. In Fram Strait, however, with predominantly advected ice, the ice cover was subject to more ageing and deterioration and included significantly higher fractions of brash ice and submerged ice. The results from the algorithm in combination with the ice thickness provide new information about the state of the observed sea ice and the relationship of present ice types to ice thickness. [Copyright &y& Elsevier]