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We investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and are partitioned into thermodynamic growth and dynamic volume change. Both components are compared with validated sea ice–ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of −2 km3 month−1 yr−1 on average over 2002–19 as derived from satellite observations. [ABSTRACT FROM AUTHOR]

State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high-frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along-track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03-0.06 m. [ABSTRACT FROM AUTHOR]

In April 2017, we collected unique, extensive in situ data of sea ice and snow thickness. At 10 sampling sites, located under a CryoSat-2 overpass, between Ellesmere Island and 87.1°N mean and modal total ice thicknesses ranged between 2 to 3.4 m and 1.8 to 2.9 m, respectively. Coincident snow thicknesses ranged between 0.3 to 0.47 m (mean) and 0.1 to 0.5 m (mode). The profile spanned the complete multiyear ice zone in the Lincoln Sea, into the first-year ice zone farther north. Complementary snow thickness measurements near the North Pole showed a mean thickness of 0.31 m. Compared with scarce measurements from other years, multiyear ice was up to 0.75 m thinner than in 2004, but not significantly different from 2011 and 2014. We found excellent agreement with a commonly used snow climatology and with published long-term ice thinning rates. There was reasonable agreement with CryoSat-2 thickness retrievals. [ABSTRACT FROM AUTHOR]

The FSSCat mission was the 2017 ESA Sentinel Small Satellite (S⌃3) Challenge winner and the Copernicus Masters competition overall winner. It was successfully launched on 3 September 2020 onboard the VEGA SSMS PoC (VV16). FSSCat aims to provide coarse and downscaled soil moisture data and over polar regions, sea ice cover, and coarse resolution ice thickness using a combined L-band microwave radiometer and GNSS-Reflectometry payload. As part of the calibration and validation activities of FSSCat, a GNSS-R instrument was deployed as part of the MOSAiC polar expedition. The Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition was an international one-year-long field experiment led by the Alfred Wegener Institute to study the climate system and the impact of climate change in the Arctic Ocean. This paper presents the first results of the PYCARO-2 instrument, focused on the GNSS-R techniques used to measure snow and ice thickness of an ice floe. The Interference Pattern produced by the combination of the GNSS direct and reflected signals over the sea-ice has been modeled using a four-layer model. The different thicknesses of the substrate layers (i.e., snow and ice) are linked to the position of the fringes of the interference pattern. Data collected by MOSAiC GNSS-R instrument between December 2019 and January 2020 for different GNSS constellations and frequencies are presented and analyzed, showing that under general conditions, sea ice and snow thickness can be retrieved using multiangular and multifrequency data. [ABSTRACT FROM AUTHOR]

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU's Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services. [ABSTRACT FROM AUTHOR]

Brightness temperatures at 1.4 GHz (L-band) measured by the Soil Moisture and Ocean Salinity (SMOS) Mission have been used to derive the thickness of sea ice. The retrieval method is applicable only for relatively thin ice and not during the melting period. Hitherto, the availability of ground truth sea ice thickness measurements for validation of SMOS sea ice products was mainly limited to relatively thick ice. The situation has improved with an extensive field campaign in the Barents Sea during an anomalous ice edge retreat and subsequent freeze-up event in March 2014. A sea ice forecast system for ship route optimisation has been developed and was tested during this field campaign with the ice-strengthened research vessel RV Lance. The ship cruise was complemented with coordinated measurements from a helicopter and the research aircraft Polar 5. Sea ice thickness was measured using an electromagnetic induction (EM) system from the bow of RV Lance and another EM-system towed below the helicopter. Polar 5 was equipped among others with the L-band radiometer EMIRAD-2. The experiment yielded a comprehensive data set allowing the evaluation of the operational forecast and route optimisation system as well as the SMOS-derived sea ice thickness product that has been used for the initialization of the forecasts. Two different SMOS sea ice thickness products reproduce the main spatial patterns of the ground truth measurements while the main difference being an underestimation of thick deformed ice. Ice thicknesses derived from the surface elevation measured by an airborne laser scanner and from simultaneous EMIRAD-2 brightness temperatures correlate well up to 1.5 m which is more than the previously anticipated maximal SMOS retrieval thickness. [ABSTRACT FROM AUTHOR]

Abstract: In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions. [Copyright &y& Elsevier]